A method and system for intelligent state evaluation of a water-based damping material

Abstract

The application discloses a kind of water-based damping material intelligent state evaluation method and system, it is related to water-based damping material state evaluation technical field, comprising: obtaining the film-forming process data of water-based damping material to be evaluated on target substrate, film-forming environmental parameter, vibration response data before and after coating and test environmental parameter;According to film-forming process data and film-forming environmental parameter, generate film-forming hidden state feature;According to vibration response data before coating, determine key resonance frequency band and generate frequency band weight;According to film-forming environmental parameter and test environmental parameter, vibration response data before and after coating is normalized to standard working condition;According to film-forming hidden state feature, generate predicted damping contribution value, and according to standard vibration response data and frequency band weight, generate real damping contribution value;According to the deviation of both, generate film-forming contribution deviation result and output intelligent state evaluation result.The application can reduce environmental interference, significantly improve evaluation pertinence, comparability and reason traceability.

Description

Technical Field

[0001] This invention relates to the field of waterborne damping material condition assessment technology, specifically to an intelligent condition assessment method and system for waterborne damping materials. Background Technology

[0002] Waterborne damping materials, using water as the dispersion medium, feature low volatile organic compound emissions, good construction adaptability, and the ability to form damping layers on the surfaces of metal sheets, composite substrates, and engineering components. They have been increasingly applied in vibration and noise reduction scenarios for vehicles, rail transit, home appliances, equipment housings, and building components. Current evaluation methods typically focus on the material's bulk loss factor, coating thickness, adhesion state, curing state, and changes in vibration response before and after coating, using dynamic mechanical analysis, vibration beam testing, frequency response testing, infrared detection, and image detection to evaluate material performance. However, with the increasing complexity of engineering applications, the actual damping effect of waterborne damping materials no longer depends solely on the material's bulk parameters but is also influenced by the film-forming process, film-forming environment, resonance characteristics of the target substrate, and differences in testing conditions. Therefore, a comprehensive evaluation of waterborne damping materials based on the actual service conditions of the substrate has become an important technical direction.

[0003] However, existing methods for assessing the condition of waterborne damping materials still have significant shortcomings. First, current methods often separate film quality testing from vibration response testing, typically judging material condition only by external cracks, film thickness uniformity, or final loss factor. This makes it difficult to identify latent film defects such as surface curing with high residual moisture, uneven local curing, or microcracks that have not yet significantly affected the appearance, resulting in a lack of interpretable correlation between film condition and actual damping contribution. Second, existing vibration evaluation methods usually directly compare response changes before and after coating, rarely considering differences in the key resonant frequency bands of the target substrate itself. This easily leads to the inclusion of response attenuation in non-critical frequency bands in the evaluation results, while ignoring the contribution of the main resonant frequency band that truly determines vibration reduction requirements. Third, waterborne damping materials are highly sensitive to temperature and humidity. Both the film formation environment and the vibration testing environment can alter the material's viscoelastic state, substrate response, and test data stability. Without standard operating condition normalization, the response differences before and after coating may be mixed with environmental disturbances, making it difficult to accurately reflect the material's true damping contribution. Finally, existing methods typically output a pass / fail conclusion or a single damping performance value, lacking a mechanism for analyzing the deviation between the predicted damping contribution and the actual damping contribution. This makes it impossible to further determine whether the insufficient damping originates from defects in film quality, insufficient frequency band adaptation, abnormal coverage thickness, abnormal interface bonding, or insufficient test reliability. Consequently, it is difficult to provide a clear basis for process optimization, local verification, and material application adaptation. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by this invention is that existing methods for assessing the state of waterborne damping materials have the following problems: the latent defects in film formation are difficult to correlate with damping contribution; the vibration response evaluation before and after coating does not fully incorporate the key resonant frequency band of the target substrate; the difference between the film formation environment and the vibration response test environment leads to insufficient credibility of the evaluation of the true damping contribution; and how to output intelligent state assessment results with abnormal attribution ability based on the deviation between the predicted damping contribution value and the true damping contribution value.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, embodiments of the present invention provide a smart state assessment method for aqueous damping materials, comprising the following steps: S1: Obtain film formation process data and film formation environment parameters of the water-based damping material to be evaluated on the target substrate, and obtain vibration response data of the target substrate before coating, vibration response data after coating, and vibration response test environment parameters. S2: Generate latent state characteristics of film formation based on the film formation process data and the film formation environment parameters; S3: Determine the key resonant frequency band of the target substrate based on the vibration response data before coating, and generate frequency band weights based on the vibration energy distribution within the key resonant frequency band; S4: Based on the film formation environment parameters and the vibration response test environment parameters, the vibration response data before coating and the vibration response data after coating are normalized to standard operating conditions to obtain standard vibration response data before coating and standard vibration response data after coating. S5: Generate a predicted damping contribution value based on the film-forming latent state characteristics, and generate a true damping contribution value based on the standard vibration response data before coating, the standard vibration response data after coating, and the frequency band weight; S6: Generate a film formation contribution deviation result based on the deviation between the predicted damping contribution value and the actual damping contribution value, and output the intelligent state evaluation result of the waterborne damping material to be evaluated based on the film formation latent state characteristics, the actual damping contribution value and the film formation contribution deviation result.

[0007] As a preferred embodiment of the intelligent state assessment method for water-based damping materials described in this invention, step S1 specifically includes: The initial coating mass, film formation quality time series data, mass decay rate, film thickness distribution, surface image, infrared thermogram, surface temperature and curing time of the waterborne damping material to be evaluated on the target substrate are obtained as the film formation process data. The film-forming temperature and humidity during the film-forming process of the water-based damping material to be evaluated are obtained as the film-forming environment parameters; The excitation frequency, vibration acceleration, resonance peak frequency, resonance peak amplitude, and half-power bandwidth of the target substrate before coating are obtained as the vibration response data before coating. The excitation frequency, vibration acceleration, resonance peak frequency, resonance peak amplitude, half-power bandwidth, and composite loss factor of the target substrate after coating are obtained as the vibration response data after coating. The test temperature and test humidity during the acquisition of the vibration response data before and after coating are used as the vibration response test environment parameters. The damping layer coverage and damping layer thickness of the water-based damping material to be evaluated on the target substrate are obtained as damping layer structural parameters. The film formation process data, film formation environment parameters, pre-coating vibration response data, post-coating vibration response data, vibration response test environment parameters, and damping layer structure parameters are associated according to the same target substrate number and the same coating area number.

[0008] As a preferred embodiment of the intelligent state assessment method for water-based damping materials described in this invention, step S2 specifically includes: A moisture evaporation curve is constructed based on the initial coating quality, the film formation quality time series data, the quality decay rate, the film formation temperature, the film formation humidity, and the curing time. A residual moisture risk value is generated based on the degree of deviation of the moisture evaporation curve from the preset standard evaporation curve. The film thickness variation coefficient is calculated based on the film thickness distribution, and a film thickness uniformity value is generated based on the film thickness variation coefficient. The temperature difference between different sub-regions of the damping layer within the coating area is determined based on the infrared thermal image and the surface temperature, and a surface thermal uniformity value is generated based on the temperature difference. Based on the surface image, the local shrinkage region and microcrack region on the surface of the damping layer formed by the water-based damping material to be evaluated are determined, and a local shrinkage risk value is generated based on the local shrinkage region, and a microcrack risk value is generated based on the microcrack region. The latent state characteristics of film formation are generated based on the residual moisture risk value, the film thickness uniformity value, the surface thermal uniformity value, the local shrinkage risk value, and the microcrack risk value.

[0009] As a preferred embodiment of the intelligent state assessment method for water-based damping materials described in this invention, step S3 specifically includes: Based on the excitation frequency and vibration acceleration in the vibration response data before coating, the frequency response curve and vibration energy distribution curve of the target substrate before coating are generated. The key resonant frequency band of the target substrate is determined based on the resonant peak frequency, resonant peak amplitude, and half-power bandwidth in the frequency response curve. The vibration energy within each of the key resonant frequency bands is determined based on the vibration energy distribution curve. Based on the proportion of vibration energy in each key resonance frequency band to the total vibration energy of all key resonance frequency bands, the frequency band weights corresponding to each key resonance frequency band are generated.

[0010] As a preferred embodiment of the intelligent state assessment method for water-based damping materials described in this invention, step S4 specifically includes: A film formation environment correction coefficient is generated based on the deviation of the film formation environment parameters relative to the preset standard film formation conditions, and a test environment correction coefficient is generated based on the deviation of the vibration response test environment parameters relative to the preset standard test conditions. An environmental mapping relationship is constructed based on the film formation environment correction coefficient and the test environment correction coefficient; Based on the test environment correction coefficient, the vibration acceleration, resonance peak amplitude and half-power bandwidth in the pre-coating vibration response data are normalized to standard operating conditions to obtain the pre-coating standard vibration response data. Based on the environmental mapping relationship, the vibration acceleration, resonance peak amplitude, half-power bandwidth and composite loss factor in the post-coating vibration response data are normalized according to standard operating conditions to obtain the post-coating standard vibration response data. Based on the key resonance frequency band, the standard vibration response data before coating and the standard vibration response data after coating are mapped to the same frequency sampling points to generate standard response pairing data before and after coating. Calculate the first response correction difference between the vibration response data before coating and the standard vibration response data before coating at the same frequency sampling point, and the second response correction difference between the vibration response data after coating and the standard vibration response data after coating at the same frequency sampling point, and generate environmental normalized residual values ​​based on the first response correction difference and the second response correction difference. A standard response confidence level is generated based on the environmental normalized residual value, and the pairwise standard response data before and after coating is marked for validity based on the standard response confidence level.

[0011] As a preferred embodiment of the intelligent state assessment method for waterborne damping materials described in this invention, step S5, which involves generating a predicted damping contribution value based on the film-forming latent state characteristics, specifically includes: The film integrity index is generated by weighting the residual moisture risk value, the film thickness uniformity value, the surface thermal uniformity value, the local shrinkage risk value, and the microcrack risk value. Obtain the standard loss factor curve of the waterborne damping material to be evaluated under standard film-forming conditions, and perform attenuation correction on the standard loss factor curve according to the comprehensive index of film integrity to obtain the predicted loss factor curve. Based on the integral value of the loss factor curve in each of the key resonance frequency bands and the corresponding frequency band weights of each key resonance frequency band, a normalized predicted damping contribution value is generated.

[0012] As a preferred embodiment of the intelligent state assessment method for water-based damping materials described in this invention, step S5, which involves generating a true damping contribution value based on the pre-coating standard vibration response data, the post-coating standard vibration response data, and the frequency band weight, specifically includes: Based on the validity marker, extract valid response pairing data from the standard response pairing data before and after coating; Based on the effective response pairing data, determine the decrease in resonance peak amplitude, the increase in half-power bandwidth, and the decrease in vibration energy within each of the key resonance frequency bands; Based on the frequency band weights corresponding to each of the key resonance frequency bands, the decrease in resonance peak amplitude, the increase in half-power bandwidth, and the decrease in vibration energy are weighted and fused to generate an initial true damping contribution value. The initial true damping contribution value is corrected based on the damping layer coverage and the damping layer thickness to generate a normalized true damping contribution value.

[0013] As a preferred embodiment of the intelligent state assessment method for waterborne damping materials according to the present invention, step S6, which generates a film formation contribution deviation result based on the deviation between the predicted damping contribution value and the actual damping contribution value, specifically includes: The contribution deviation value is calculated based on the predicted damping contribution value and the actual damping contribution value; The contribution deviation value is normalized based on the predicted damping contribution value to generate the contribution deviation rate; Based on the relationship between the actual damping contribution value and the predicted damping contribution value, a contribution deviation direction marker is generated; A film formation contribution deviation level is generated based on the contribution deviation rate and a preset deviation level threshold. The film formation contribution deviation result is generated based on the contribution deviation value, the contribution deviation rate, the contribution deviation direction marker, and the film formation contribution deviation level.

[0014] As a preferred embodiment of the intelligent state assessment method for waterborne damping materials according to the present invention, step S6, which involves outputting the intelligent state assessment result of the waterborne damping material to be assessed based on the film-forming latent state characteristics, the true damping contribution value, and the film-forming contribution deviation result, specifically includes: The film formation state level is determined based on the residual moisture risk value, film thickness uniformity value, surface thermal uniformity value, local shrinkage risk value, and microcrack risk value in the film formation latent state characteristics. The actual damping contribution level is determined based on the comparison between the actual damping contribution value and the preset contribution level threshold. The type of abnormal cause is determined based on the matching relationship between the film formation state level, the actual damping contribution level, the film formation contribution deviation level, and the contribution deviation direction mark. The intelligent state assessment result is generated based on the film formation state level, the actual damping contribution level, the film formation contribution deviation level, the contribution deviation direction marker, and the anomaly cause type.

[0015] Secondly, embodiments of the present invention provide an intelligent state assessment system for water-based damping materials, comprising: Data acquisition module: acquires film formation process data and film formation environment parameters of the water-based damping material to be evaluated on the target substrate, and acquires vibration response data of the target substrate before coating, vibration response data after coating, and vibration response test environment parameters. Film-forming latent state generation module: Generates film-forming latent state characteristics based on the film-forming process data and the film-forming environment parameters; Frequency band weight generation module: Determines the key resonant frequency band of the target substrate based on the vibration response data before coating, and generates frequency band weights based on the vibration energy distribution within the key resonant frequency band; Standard operating condition normalization module: Based on the film formation environment parameters and the vibration response test environment parameters, the vibration response data before coating and the vibration response data after coating are normalized according to standard operating conditions to obtain standard vibration response data before coating and standard vibration response data after coating. Damping contribution value generation module: Generates predicted damping contribution value based on the film-forming latent state characteristics, and generates actual damping contribution value based on the standard vibration response data before coating, the standard vibration response data after coating, and the frequency band weight; Intelligent state assessment module: Generates film formation contribution deviation result based on the deviation between the predicted damping contribution value and the actual damping contribution value, and outputs the intelligent state assessment result of the waterborne damping material to be evaluated based on the film formation latent state characteristics, the actual damping contribution value and the film formation contribution deviation result.

[0016] The beneficial effects of this invention are as follows: This invention generates latent state characteristics of the film-forming process by collecting data on the film-forming process of waterborne damping materials, film-forming environmental parameters, and vibration response data of the target substrate before and after coating. It then generates predicted and actual damping contribution values ​​by combining the key resonant frequency bands and frequency band weights of the target substrate. Furthermore, it reduces the interference of environmental differences on vibration response data through standard operating condition normalization and outputs intelligent state assessment results using the deviation between the predicted and actual damping contribution values. Therefore, this invention improves the relevance, comparability, and traceability of state assessment for waterborne damping materials, providing more reliable data for film quality verification, damping performance judgment, and engineering application optimization. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein: Figure 1 This is an overall flowchart of a smart state assessment method for water-based damping materials provided in the first embodiment of the present invention; Figure 2 The module connection diagram is provided for a smart state assessment system for water-based damping materials according to the third embodiment of the present invention. Detailed Implementation

[0018] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0019] Example 1, referring to Figure 1 As an embodiment of the present invention, a smart state assessment method for water-based damping materials is provided.

[0020] S1: Obtain film formation process data and film formation environment parameters of the waterborne damping material to be evaluated on the target substrate, and obtain vibration response data of the target substrate before coating, vibration response data after coating, and vibration response test environment parameters.

[0021] The initial coating mass, film formation quality time series data, mass decay rate, film thickness distribution, surface image, infrared thermogram, surface temperature and curing time of the waterborne damping material to be evaluated on the target substrate are obtained as the film formation process data. The film-forming temperature and humidity during the film-forming process of the water-based damping material to be evaluated are obtained as the film-forming environment parameters; The excitation frequency, vibration acceleration, resonance peak frequency, resonance peak amplitude, and half-power bandwidth of the target substrate before coating are obtained as the vibration response data before coating. The excitation frequency, vibration acceleration, resonance peak frequency, resonance peak amplitude, half-power bandwidth, and composite loss factor of the target substrate after coating are obtained as the vibration response data after coating. The test temperature and test humidity during the acquisition of the vibration response data before and after coating are used as the vibration response test environment parameters. The damping layer coverage and damping layer thickness of the water-based damping material to be evaluated on the target substrate are obtained as damping layer structural parameters. The film formation process data, film formation environment parameters, pre-coating vibration response data, post-coating vibration response data, vibration response test environment parameters, and damping layer structure parameters are associated according to the same target substrate number and the same coating area number.

[0022] In this embodiment, step S1 is used to establish the correspondence between the water-based damping material to be evaluated, the target substrate, the coating area, the film formation process data, and the vibration response data, so that the subsequently generated latent film formation characteristics, predicted damping contribution values, and actual damping contribution values ​​all originate from the same substrate object and the same coating area, avoiding the distortion of evaluation results caused by mixing data from different samples.

[0023] It should be noted that the target substrate can be a metal substrate, composite material substrate, or engineering structure sample requiring damping and vibration reduction treatment. Before data collection, the target substrate is numbered, and the coating area is divided according to the actual coating position of the water-based damping material. The target substrate number is used to distinguish different substrate samples, and the coating area number is used to distinguish different coating positions on the same target substrate. For example, when the target substrate is a steel plate with a length of 300 mm, a width of 200 mm, and a thickness of 2 mm, its surface can be divided into a central area, a left area, a right area, an upper area, and a lower area, and each area is assigned a unique coating area number. For coating areas with an area less than 100 square centimeters, one coating area number can be assigned; for coating areas with an area of ​​100 square centimeters to 500 square centimeters, the area can be divided into 3 to 9 sub-areas according to the shape of the area, so as to obtain more refined film thickness distribution and temperature distribution data later.

[0024] Furthermore, when acquiring film-forming process data, the initial coating mass refers to the actual mass of material adhered to the target substrate before the water-based damping material completes coating and enters the film-forming timer. This initial coating mass can be obtained by weighing the target substrate before and after coating, or by weighing the difference in mass between the coating container and the substrate before and after coating. To ensure data accuracy, the weighing accuracy can be set to 0.001 grams to 0.01 grams. When the coating area is large, the initial coating mass corresponding to each coating area can be recorded separately, so that the mass data can correspond to the coating area number.

[0025] It should also be noted that the film-forming quality time-series data refers to the quality change data of the water-based damping material recorded at multiple sampling times starting from the completion of coating. The sampling interval is determined according to the material curing speed. For water-based damping materials that complete surface drying in 30 to 240 minutes at room temperature, the sampling interval is set to 5 to 30 minutes.

[0026] For the At each sampling time, the current mass of the water-based damping material after deducting the initial mass of the target substrate is: The initial mass of the coating is The first one is determined according to the following formula. The remaining mass ratio at each sampling time : ; For adjacent th Each sampling time and the Each sampling time The mass decay rate within the corresponding sampling time interval is determined according to the following formula. : ; In the formula, and These represent the remaining mass percentages at two adjacent sampling times. The time interval between two adjacent sampling times. The mass attenuation rate. Used to characterize the rate of mass decay of aqueous damping materials relative to the initial coating mass within the corresponding sampling time interval.

[0027] When from the first If, within a consecutive 30-minute period starting from the sampling time, the difference between the maximum and minimum mass residual ratios does not exceed 0.005, and the mass residual ratio continues to meet the above condition after that sampling time, then the sampling time will be... Each sampling time point is defined as the quality stabilization time.

[0028] Furthermore, the film thickness distribution is used to characterize the uniformity of the damping layer thickness within the coating area. The film thickness can be obtained using a wet film thickness gauge, a dry film thickness gauge, a laser rangefinder, or an ultrasonic thickness gauge. For a single coating area, measurement points can be selected according to a regular grid, with the number of measurement points ranging from 5 to 25; when the coating area is greater than 500 square centimeters, the number of measurement points can be increased to 25 to 49. The corresponding thickness value is recorded for each measurement point, and the thickness values ​​of each measurement point are saved according to their position within the coating area, forming film thickness distribution data. A large thickness difference refers to the difference between the maximum and minimum film thickness within the same coating area exceeding 20% ​​of the average film thickness; a small thickness difference refers to this difference not exceeding 5% of the average film thickness.

[0029] It should also be noted that the surface images are used to identify whether there are localized shrinkage, cracks, bubbles, build-up, or exposed substrate areas on the surface of the damping layer. Surface images can be obtained using industrial cameras or high-definition video equipment, ensuring consistent lighting direction and shooting distance during acquisition. For fine crack identification, the image resolution can be set to correspond to an actual size of 0.05 mm to 0.2 mm per pixel; for large-area coverage identification, the image resolution can be set to correspond to an actual size of 0.2 mm to 1 mm per pixel. Acquisition time can include when coating is complete, during the middle of film formation, and at the end of curing, enabling subsequent determination of whether defects formed in the early stages of coating or gradually formed during moisture evaporation and curing.

[0030] Furthermore, the infrared thermal image and surface temperature are used to characterize the thermal state of the damping layer during the film formation process. The infrared thermal image can be obtained using an infrared thermal imager, and the surface temperature can be determined by the temperature values ​​of each sub-region in the infrared thermal image, or it can be collected by a contact temperature sensor at preset measurement points. During infrared thermal image acquisition, the distance between the infrared thermal imager and the target substrate surface can be set to 0.3 meters to 1.5 meters, and the shooting angle should be kept stable. For the same coating area, it is divided into multiple non-overlapping sub-regions, and the average surface temperature of each sub-region is recorded. The temperature difference between different sub-regions refers to the difference between the maximum and minimum average surface temperatures of each sub-region. By recording the average surface temperature of each sub-region, a data basis is provided for subsequently determining the temperature difference and surface thermal uniformity values.

[0031] Recording infrared thermograms and surface temperatures can provide a data basis for subsequent determination of surface thermal uniformity values.

[0032] It should also be noted that the curing time refers to the time elapsed from the completion of coating and film formation of the water-based damping material to the damping layer reaching the preset curing state. The preset curing state can be determined by the stability of mass change, surface non-adhesion, or the placement time before vibration testing. For example, when the mass change does not exceed 0.5% of the initial coating mass within 30 consecutive minutes, and no visible adhesion occurs on the surface of the damping layer after light touch, it can be determined that the curing state has been reached. For different water-based damping materials, the curing time can be determined according to the material specifications, calibration tests, and actual environmental conditions.

[0033] Furthermore, the film-forming environment parameters include film-forming temperature and film-forming humidity. Film-forming temperature refers to the temperature of the air surrounding the coating area during the film formation of the water-based damping material, and film-forming humidity refers to the relative humidity of the air surrounding the coating area during this period. During data collection, temperature and humidity sensors can be positioned 0.1 to 0.5 meters from the target substrate surface, with the sensor height approximately the same as the height of the coating area. Film-forming temperature and humidity can be recorded at time intervals of 5 to 30 minutes. Significant fluctuations in film-forming temperature are defined as a difference exceeding 5 degrees Celsius between the highest and lowest temperatures during film formation; significant fluctuations in film-forming humidity are defined as a difference exceeding 15% between the highest and lowest relative humidity. These ranges define the criteria for judging environmental fluctuations, avoiding reliance solely on subjective experience to determine the stability of the film-forming environment.

[0034] It should also be noted that film-forming temperature and humidity affect the water evaporation process, emulsion particle fusion process, and damping layer continuity in waterborne damping materials. When the film-forming temperature is high and the humidity is low, for example, when the film-forming temperature is above 35 degrees Celsius and the relative humidity is below 40%, the surface moisture of the damping layer may evaporate before the internal moisture, causing the surface to form a dense layer earlier, thus hindering further escape of internal moisture. When the film-forming temperature is low and the humidity is high, for example, when the film-forming temperature is below 10 degrees Celsius and the relative humidity is above 80%, the water evaporation process is prolonged, increasing the risk of residual moisture inside the damping layer. Therefore, in this embodiment, film-forming temperature and humidity are collected as environmental parameters for film formation, not simply to record environmental conditions, but to provide external operating condition data for subsequent assessment of residual moisture risk and latent film characteristics.

[0035] Furthermore, when acquiring vibration response data before coating, vibration testing needs to be conducted on the target substrate before it is coated with the water-based damping material. During testing, the clamping method, support position, sensor installation position, and excitation position of the target substrate should be recorded. The excitation method can be frequency sweep excitation, impact excitation, or fixed frequency excitation. For plate-shaped substrates, the frequency sweep range can be set to 20 Hz to 2000 Hz; for substrates with higher stiffness, the frequency sweep range can be extended to 5000 Hz. The excitation frequency is used to record the frequency point corresponding to the input excitation, and the vibration acceleration is used to record the response intensity of the target substrate at each frequency point. Based on the response curve formed by the change of vibration acceleration with the excitation frequency, the resonance peak frequency, resonance peak amplitude, and half-power bandwidth can be determined. The resonance peak frequency is the frequency corresponding to the local peak value in the vibration response curve, the resonance peak amplitude is the response amplitude corresponding to this local peak value, and the half-power bandwidth is the frequency interval between the two frequency points corresponding to the response amplitude on both sides of the resonance peak dropping to 70.7% of the peak value.

[0036] It should also be noted that the vibration response data after coating should be obtained after the damping layer has reached the preset curing state, and the boundary conditions, excitation position, sensor position, frequency sweep range, and sampling interval should be consistent with those before coating. The boundary conditions include the clamping method, support position, constraint method, and test posture of the target substrate. The vibration response data after coating includes excitation frequency, vibration acceleration, resonance peak frequency, resonance peak amplitude, half-power bandwidth, and composite loss factor.

[0037] The composite loss factor is used to characterize the energy dissipation capability of the composite structure formed by the target substrate and the damping layer under resonant conditions. To determine the composite loss factor, a swept-frequency vibration test is used, ensuring that the input excitation amplitude is consistent at each frequency sampling point during the sweep process. A frequency response curve after coating is generated based on the excitation frequency and the vibration acceleration.

[0038] Resonance peaks were identified in the frequency response curve after coating. For the first... A resonance peak is defined as a valid resonance peak when both its low-frequency and high-frequency sides have frequencies corresponding to the half-power amplitude, and there is no other resonance peak between the two corresponding frequencies. The resonant frequency corresponding to each effective resonant peak and resonance peak amplitude The half-power amplitude is determined according to the following formula. : ; In the first The vibration response amplitude is determined to be equal to the half-power amplitude based on the low-frequency and high-frequency sides of each effective resonance peak. The first half-power frequency corresponding to time Second half-power frequency ,in: ; According to the first half-power frequency and the second half-power frequency The first one is determined according to the following formula. Half-power bandwidth corresponding to each effective resonant peak : ; When there is no vibration response amplitude at the discrete frequency sampling points that is exactly equal to the half-power amplitude When sampling the frequency points, adjacent frequency sampling points whose vibration response amplitudes are located on both sides of the half-power amplitude are selected on the corresponding side of the effective resonance peak, and the corresponding half-power frequency is determined by linear interpolation.

[0039] For adjacent th The frequency sampling point and the first Each frequency sampling point, half-power frequency Determined according to the following formula: ; In the formula, and These are the excitation frequencies of the adjacent frequency sampling points. and These are the vibration response amplitudes corresponding to the adjacent frequency sampling points, and the half-power amplitudes are... lie in and Between. The first half-power frequency is determined using the linear interpolation method described above on the low-frequency side of the resonance peak. The second half-power frequency is determined at the high-frequency side of the resonance peak using the linear interpolation method described above. .

[0040] No. The composite loss factor corresponding to each effective resonance peak Defined as the ratio of the half-power bandwidth to the resonant frequency, and determined according to the following formula: ; In the formula, This is a dimensionless value. A larger composite loss factor indicates a higher degree of structural integrity in the composite structure formed by the target substrate and the damping layer during the [missing value] phase. The stronger the energy dissipation capacity near an effective resonance peak.

[0041] When multiple effective resonance peaks exist, the composite loss factor corresponding to each effective resonance peak is determined, and the composite loss factor data is formed by associating the target substrate number, coating area number, and resonance peak number.

[0042] If no frequency sampling point with a vibration response amplitude reaching the half-power amplitude is collected on the low-frequency or high-frequency side of a resonance peak, the vibration response data is re-collected after expanding the frequency sweep range corresponding to the resonance peak. When another resonance peak exists between two half-power frequencies, the corresponding resonance peak is marked as an overlapping resonance peak, and the composite loss factor corresponding to the resonance peak is not calculated using the half-power bandwidth method. The vibration response data corresponding to the overlapping resonance peak is still retained for subsequent determination of key resonance frequency bands and vibration energy.

[0043] To ensure comparability of data before and after coating, if the sensor position deviates by more than 5 mm from the position before coating, the sensor is reinstalled and data is collected again; if the excitation position deviation exceeds 10 mm, the pairing test before and after coating is repeated. Through the above consistency control, the difference in vibration response before and after coating mainly reflects the change in structural damping after the introduction of the damping layer, reducing the influence of clamping state, excitation position, and sensor position changes on the vibration response comparison results.

[0044] Furthermore, the vibration response test environment parameters include test temperature and test humidity. Test temperature and test humidity are used as the external test conditions for recording vibration response data before and after coating, respectively. Test temperature can be obtained using a temperature sensor, and test humidity can be obtained using a humidity sensor. The sensors can be positioned within a test space 0.1 to 1 meter away from the target substrate. A large change in test temperature refers to a temperature change exceeding 3 degrees Celsius during a single vibration test; a large change in test humidity refers to a relative humidity change exceeding 10% during a single vibration test. When the test temperature or test humidity changes significantly, the corresponding test segment should be marked in the data record for subsequent standard condition normalization processing. Since the modulus, interfacial adhesion state, and energy dissipation performance of the substrate and damping layer may change with temperature and humidity, synchronously recording the test temperature and test humidity can provide a basis for subsequently eliminating the influence of test condition differences on the vibration response data.

[0045] Furthermore, the damping layer structural parameters include damping layer coverage and damping layer thickness. Damping layer coverage characterizes the degree of coverage of the actual damping layer area relative to the preset coating area. It can be obtained by identifying the actual coverage area through surface image segmentation and comparing it with the area of ​​the preset coating area. High coverage means the actual coverage area reaches more than 95% of the preset coating area; low coverage means the actual coverage area is less than 85% of the preset coating area. Damping layer thickness can be measured using the film thickness distribution or the dry film thickness after curing. Stable thickness means the thickness difference between measurement points within the same coating area does not exceed 10% of the average thickness; unstable thickness means the difference exceeds 20% of the average thickness. Damping layer coverage and thickness not only describe the completion status of construction but also help to correct the actual damping contribution value later, preventing the actual damping contribution value from being misjudged as a high material contribution due to a large coating area or a large damping layer thickness. This ensures that subsequent evaluation results better reflect the effective damping contribution of the water-based damping material on the target substrate.

[0046] Furthermore, after completing the above data acquisition, the film formation process data, film formation environment parameters, pre-coating vibration response data, post-coating vibration response data, vibration response test environment parameters, and damping layer structure parameters are associated with the same target substrate number and the same coating area number. Specifically, each set of data records includes the target substrate number, coating area number, acquisition time, acquisition device number, and acquisition condition marker. For pre-coating vibration response data, since the damping layer has not yet formed at the time of acquisition, the target substrate can be pre-numbered according to the planned coating area, so that the pre-coating vibration response data can correspond to the subsequent coating area. For post-coating vibration response data, the same target substrate number and coating area number should be used for storage. If the same target substrate contains multiple coating areas, corresponding data records should be established separately to avoid cross-referencing of film formation data and vibration response data from different coating areas.

[0047] It should also be noted that, through the above-mentioned correlation processing, the residual moisture risk value, film thickness uniformity value, surface thermal uniformity value, local shrinkage risk value, microcrack risk value, key resonance frequency band, frequency band weight, predicted damping contribution value, and actual damping contribution value generated in subsequent steps can all be traced back to the same target substrate and the same coating area. Therefore, step S1 not only completes data acquisition but also provides a unified data foundation for subsequent assessment of the latent state of film formation, vibration response normalization, and damping contribution deviation analysis.

[0048] S2: Generate latent state characteristics of film formation based on the film formation process data and the film formation environment parameters.

[0049] A moisture evaporation curve is constructed based on the initial coating quality, the film formation quality time series data, the quality decay rate, the film formation temperature, the film formation humidity, and the curing time. A residual moisture risk value is generated based on the degree of deviation of the moisture evaporation curve from the preset standard evaporation curve. The film thickness variation coefficient is calculated based on the film thickness distribution, and a film thickness uniformity value is generated based on the film thickness variation coefficient. The temperature difference between different sub-regions of the damping layer within the coating area is determined based on the infrared thermal image and the surface temperature, and a surface thermal uniformity value is generated based on the temperature difference. Based on the surface image, the local shrinkage region and microcrack region on the surface of the damping layer formed by the water-based damping material to be evaluated are determined, and a local shrinkage risk value is generated based on the local shrinkage region, and a microcrack risk value is generated based on the microcrack region. The latent state characteristics of film formation are generated based on the residual moisture risk value, the film thickness uniformity value, the surface thermal uniformity value, the local shrinkage risk value, and the microcrack risk value.

[0050] In this embodiment, step S2 is used to generate latent film-forming state features that characterize the actual film-forming quality of the aqueous damping material based on the film-forming process data and film-forming environment parameters obtained in step S1. It should be noted that these latent film-forming state features are not simply visual inspection results, but rather a comprehensive expression of the moisture evaporation state, film thickness spatial consistency, surface thermal state, local shrinkage state, and microcrack state, used for subsequent prediction of damping contribution values. Since aqueous damping materials may exhibit conditions such as a dry surface but residual internal moisture, localized thickness accumulation without obvious visual defects, and uneven heat distribution despite no surface cracking during film formation, relying solely on a single image or quality data is insufficient to accurately reflect their film-forming state. This step generates latent film-forming state features through multi-dimensional data, providing a more stable data foundation for subsequent evaluation.

[0051] Furthermore, a moisture evaporation curve was constructed based on the initial coating mass, film formation quality time-series data, mass decay rate, film formation temperature, film formation humidity, and curing time. Specifically, the coating completion time was taken as the film formation start time, and the mass values ​​corresponding to each sampling time were arranged in chronological order to form a curve showing the change in mass over time. Simultaneously, the mass reduction between adjacent sampling times was recorded as a change in the mass decay rate, and the film formation temperature and humidity were matched with the corresponding sampling times. This ensures that the moisture evaporation curve not only reflects the mass change but also the external film formation conditions at the time of the mass change. It should be noted that when the film formation temperature is between 20°C and 30°C and the film formation humidity is between 40% and 65%, it can be considered as normal temperature and humidity film formation conditions; when the film formation temperature is above 35°C and the film formation humidity is below 40%, it can be considered as a relatively fast evaporation condition; and when the film formation temperature is below 10°C and the film formation humidity is above 80%, it can be considered as a relatively slow evaporation condition. By defining the above range, we can avoid judging the rate of water evaporation based solely on subjective experience.

[0052] It should also be noted that the preset standard evaporation curve was generated from multiple qualified calibration samples of the same type of water-based damping material under preset standard film-forming conditions. The preset standard film-forming conditions included a film-forming temperature of 20°C to 30°C, a film-forming humidity of 40% to 65%, a ventilation speed not exceeding 0.5 meters per second, and airflow not directly blowing onto the coated area. The qualified calibration samples were those that, after curing, had no visible cracks exceeding 5 mm in length, had localized shrinkage areas not exceeding 2% of the coated area, a film thickness variation coefficient not exceeding 10%, and whose vibration test results met the preset damping contribution requirements.

[0053] For the The qualified calibration sample, in the first Each sampling time The corresponding material quality is The initial coating mass of the qualified calibration sample is The first one is determined according to the following formula. The qualified calibration sample was in the first The remaining mass ratio at each sampling time : ; In the formula, The current mass of the waterborne damping material is obtained after deducting the initial mass of the target substrate. The initial mass of the water-based damping material when the qualified calibration sample is coated. It is a dimensionless numerical value.

[0054] When the actual sampling times of each qualified calibration sample are not exactly the same, the mass remaining ratio of each qualified calibration sample is linearly interpolated according to a unified standard sampling time so that each qualified calibration sample has the same sampling time sequence.

[0055] Let the number of qualified calibration samples be... The first one is determined according to the following formula. The standard mass remaining ratio at each standard sampling time : ; Standard sampling times and the corresponding standard mass residual ratio The preset standard evaporation curve is formed by associating the data in chronological order.

[0056] For adjacent th The first standard sampling time and the first For each standard sampling time, the standard mass decay rate within the corresponding standard time interval is determined according to the following formula. : ; The standard quality stabilization time The sampling time at which the preset standard evaporation curve first meets the following conditions is: within a continuous 30 minutes from the sampling time, the difference between the maximum and minimum values ​​of the standard mass remaining proportion does not exceed 0.005, and the standard mass remaining proportion continues to meet the above stable conditions after the sampling time.

[0057] Through the above processing, the preset standard evaporation curve includes the standard sampling time, the standard mass remaining proportion, the standard mass decay rate, and the standard mass stabilization time, and serves as a unified comparison benchmark for the residual moisture risk value of the sample to be evaluated.

[0058] Furthermore, a residual moisture risk value is generated based on the degree of deviation of the moisture evaporation curve from the preset standard evaporation curve. When generating the residual moisture risk value, the remaining mass deviation value at the curing endpoint, the mass decay rate deviation value, and the mass stabilization time deviation value are determined respectively, and the residual moisture risk value is generated based on these three deviation values.

[0059] For the sample to be evaluated, in the first The mass of the aqueous damping material at each sampling time is The initial coating mass of the sample to be evaluated is The sample to be evaluated is determined according to the following formula at the [number]th [year]. The remaining mass ratio at each sampling time : ; When the sampling time of the sample to be evaluated is inconsistent with the standard sampling time of the preset standard volatile curve, the mass remaining ratio of the sample to be evaluated is linearly interpolated according to the standard sampling time of the preset standard volatile curve so that the sample to be evaluated and the preset standard volatile curve have the same sampling time sequence.

[0060] The moment when the sample to be evaluated reaches the preset solidification state is defined as the endpoint of the solidification evaluation. The remaining mass percentage of the sample to be evaluated at the end of the curing evaluation is determined as follows: The standardized mass residual ratio corresponding to the same curing evaluation endpoint is determined based on the preset standard volatilization curve. .

[0061] The positive deviation of the remaining mass percentage at the curing endpoint is determined by the following formula. : ; When the curing evaluation endpoint exceeds the last standard sampling time of the preset standard volatile curve, the average of the standardized remaining mass ratio after the preset standard volatile curve enters a quality stable state is taken as... .

[0062] Based on the positive deviation of the remaining mass ratio at the curing endpoint The remaining deviation value of the curing endpoint mass is determined according to the following formula. : ; In the formula, 0.03 and 0.08 represent the differences in the quality surplus ratio of 3 percentage points and 8 percentage points, respectively. Through the above piecewise mapping, when When it is 0, =0; when When it is 0.03, It is 0.5; when When it reaches or exceeds 0.08, The value is 1.

[0063] For two adjacent sampling times of the sample to be evaluated and The mass decay rate within the corresponding time interval is determined according to the following formula. : ; Extract the samples within the standardized quality stabilization time from the common sampling time interval of the sample to be evaluated and the pre-set standard evaporation curve. Previously, and standardized mass decay rate Greater than the preset minimum value The effective time intervals form a set of effective time intervals. .

[0064] For the set of valid time intervals The first in For each effective time interval, the relative deficiency value of the mass decay rate is determined according to the following formula. : ; In the formula, To prevent the use of a pre-defined minimum positive number with a denominator of zero, the value is set to... The mass decay rate deviation value is determined according to the following formula. : ; In the formula, This represents the number of valid time intervals. When the set of valid time intervals... When empty, the mass decay rate deviation value is... Determined as .

[0065] The quality stability time of the sample to be evaluated The sampling time at which the moisture evaporation curve of the sample to be evaluated first meets the following conditions: within a continuous 30 minutes from the sampling time, the difference between the maximum and minimum values ​​of the remaining mass ratio of the sample to be evaluated does not exceed 0.005, and the remaining mass ratio after the sampling time continues to meet the above stable conditions.

[0066] If the sample to be evaluated still does not meet the quality stability condition by the preset end time of film formation observation, a mark indicating that the quality stability state has not been reached is generated, and the quality stability time deviation value is recorded. The value is set to 1; in this case, the quality stabilization time delay is no longer calculated. .

[0067] When the sample to be evaluated meets the quality stability condition before the preset end time of film formation observation, the quality stability time delay is determined according to the following formula. : ; According to the quality stabilization time delay The quality stabilization time deviation value is determined according to the following formula. : ; In the formula, The unit is minutes. Through the above segmented mapping, when there is no delay in the quality stabilization time, The value is 0; when the quality stabilization time is delayed by 30 minutes, The value is 0.5; when the quality stabilization time delay reaches or exceeds 90 minutes, The value is 1.

[0068] The residual moisture risk value is determined according to the following formula. : ; In the formula, The residual moisture risk value is... The remaining deviation value of the cured endpoint mass. The mass decay rate deviation value, This is the deviation value of the quality stabilization time. Because... , and All values ​​are within the range of 0 to 1, therefore the residual moisture risk value... The value is in the range of 0 to 1, and the higher the value, the higher the risk of residual moisture in the water-based damping material to be evaluated at the end of the curing evaluation.

[0069] Based on the residual moisture risk value Determine the residual moisture risk level, where: when At that time, it was determined to be a low residual moisture risk; when At that time, it was determined to be a slight risk of residual moisture. when At that time, it was determined to be a high risk of residual moisture. when At that time, it was identified as a significant residual moisture risk.

[0070] The residual moisture risk level is used to interpret the residual moisture risk value. The numerical residual moisture risk value is used when subsequently generating the film-forming latent state characteristics and the comprehensive index of film integrity. The residual moisture risk level is not used to replace the residual moisture risk value.

[0071] The residual moisture risk value generated by the above treatment can reflect whether the internal moisture of the water-based damping material has evaporated sufficiently, avoiding the judgment of film formation completion based solely on the surface dryness.

[0072] Furthermore, the film thickness variation coefficient is calculated based on the film thickness distribution, and a film thickness uniformity value is generated based on the film thickness variation coefficient.

[0073] Let the number of effective film thickness measurement points within the same coating area be . , No. The damping layer thickness corresponding to each effective film thickness measurement point is: The average film thickness is determined according to the following formula. : ; Based on the damping layer thickness corresponding to each effective film thickness measurement point and the average film thickness, the standard deviation of the film thickness is determined according to the following formula. : ; The coefficient of variation for film thickness is determined by the following formula. : ; In the formula, This is a dimensionless value. When the coefficient of variation of film thickness is expressed as a percentage, divide the percentage value by 100 and substitute it into the following formula.

[0074] According to the film thickness variation coefficient The film thickness uniformity value is determined according to the following formula. : ; Through the above piecewise linear mapping, when At that time, the film thickness uniformity value for ;when At that time, the film thickness uniformity value for ;when At that time, the film thickness uniformity value for ;when At that time, the film thickness uniformity value for ;when Reaching or exceeding At that time, the film thickness uniformity value for The film thickness uniformity value The larger the value, the more uniform the thickness distribution of the damping layer.

[0075] When the average film thickness for Or the number of effective film thickness measurement points is less than If the film thickness variation coefficient and the film thickness uniformity value are not calculated, an invalid film thickness data marker is generated, and the film thickness distribution data is re-acquired.

[0076] Furthermore, the proportion of thickness anomaly measuring points is determined according to the following formula. : ; In the formula, This is an indicator function that takes the value when the condition is true. When the condition is not met, the value is taken as... .when At that time, local thickness anomaly markers are generated. These local thickness anomaly markers serve as auxiliary interpretations of the film thickness state and do not alter the film thickness uniformity value determined according to the above formula. .

[0077] Specifically, the coating area is divided into multiple non-overlapping sub-regions. For the first... At each infrared thermal image sampling time, the pixel temperature belonging to the effective coating area in each sub-region is extracted, and the arithmetic mean of the pixel temperature is determined as the average surface temperature of the corresponding sub-region.

[0078] Let the first The number of valid sub-regions corresponding to each infrared thermal image sampling time is , No. The number of effective temperature pixels contained in each effective sub-region is: , No. The temperature corresponding to each effective temperature pixel is: The first one is determined according to the following formula. Average surface temperature of each effective sub-region : ; The highest and lowest surface temperatures of each sub-region are recorded as auxiliary data for localized temperature anomalies and are not used to calculate the temperature difference and the surface thermal uniformity value. The temperature difference is determined based on the maximum and minimum average surface temperatures of each sub-region. Further, the temperature difference between different sub-regions of the damping layer within the coated area is determined based on the infrared thermogram and the average surface temperature, and the surface thermal uniformity value is generated based on the temperature difference.

[0079] For the For each infrared thermal image sampling time, the temperature difference of the sub-region corresponding to that sampling time is determined according to the following formula. : ; In the formula, For the first In the first infrared thermal image sampling time The average surface temperature of each effective sub-region. The temperature difference between the sub-regions is determined by the difference between the maximum and minimum average surface temperatures of the different sub-regions, not by the highest or lowest surface temperature within each sub-region. The temperature difference corresponding to the coated area is determined according to the following formula across all effective infrared thermal sampling times from the moment of coating completion to the moment the preset curing state is reached. : ; In the formula, This refers to the number of effective infrared thermal image sampling moments during the period from coating completion to reaching the preset curing state. The unit is Celsius.

[0080] Based on the temperature difference The surface thermal uniformity value is determined according to the following formula. : ; Through the above piecewise linear mapping, when At 100 degrees Celsius, the surface thermal uniformity value =1; when At 100 degrees Celsius, the surface thermal uniformity value It is 0.90; when At 100 degrees Celsius, the surface thermal uniformity value It is 0.60; when At 100 degrees Celsius, the surface thermal uniformity value It is 0.30; when When the temperature reaches or exceeds 11 degrees Celsius, the surface thermal uniformity value The value is 0. The larger the value, the more uniform the spatial thermal state of the damping layer during the film formation process.

[0081] When the number of valid infrared thermal image sampling times is 0, or the number of valid sub-regions at any infrared thermal image sampling time is less than 2, the temperature difference and the surface thermal uniformity value are not calculated, an invalid infrared thermal image data mark is generated, and infrared thermal image data is re-acquired.

[0082] For the The overall average surface temperature of the coated area is determined at each infrared thermal image sampling time using the following formula. : ; The average surface temperature is lower than The sub-regions defined by Celsius are designated as low-temperature sub-regions, and the ratio of the area of ​​these low-temperature sub-regions to the area of ​​the corresponding coated area is calculated. When this ratio exceeds 0.10, continuous low-temperature region auxiliary markers are generated. These continuous low-temperature region auxiliary markers are used for subsequent defect cross-validation and do not change the surface thermal uniformity value determined according to the above formula. .

[0083] Furthermore, candidate local shrinkage regions and candidate microcrack regions on the surface of the damping layer formed by the water-based damping material to be evaluated are determined based on the surface image. Specifically, the surface image is first processed with brightness equalization, noise suppression, and edge enhancement, and then regions in the image with textures different from those of the normal damping layer surface are identified. Connected regions in the processed surface image whose area, shape, grayscale, or texture features differ from those of the normal region are identified as candidate anomalous regions. Based on the area, boundary continuity, linear features, and mesh features of each candidate anomalous region, these regions are further classified into candidate local shrinkage regions and candidate microcrack regions. The candidate local shrinkage regions are characterized by inward boundary retraction, surface wrinkling, local exposure of the substrate, reduced coverage area, or discontinuous coating edges; the candidate microcrack regions are characterized by thin, elongated dark lines, bifurcated linear structures, or mesh-like crack structures.

[0084] The candidate local shrinkage regions and candidate microcrack regions are candidate image defect regions initially identified based on surface images. After subsequent cross-validation using film thickness distribution, infrared thermography, and mass decay status, it is determined whether they are included in the calculation of local shrinkage risk values ​​and microcrack risk values. After identifying candidate local shrinkage regions and candidate microcrack regions based on the surface images, multi-source cross-validation is performed on each candidate local shrinkage region and each candidate microcrack region. Based on the cross-validation results, the local shrinkage regions and microcrack regions retained through cross-validation are determined.

[0085] The multi-source cross-validation includes multi-source data spatial registration, determination of film thickness anomaly evidence, determination of thermal anomaly evidence, determination of quality degradation anomaly evidence, and confirmation of candidate image defects.

[0086] 1. Spatial registration of multi-source data.

[0087] Using the image coordinate system of the surface image as the reference coordinate system, at least four non-collinear corresponding feature points are selected at the boundary of the coating area. These corresponding feature points have identifiable positions in both the surface image and the infrared thermal image, and their actual positions on the target substrate are known.

[0088] Based on the coordinate relationship between the corresponding feature points in the surface image and the infrared thermal image, the infrared thermal image is projected and transformed to map the infrared thermal image to the reference coordinate system where the surface image is located.

[0089] Based on the actual position of each film thickness measurement point within the coating area, each film thickness measurement point is mapped to the reference coordinate system, and bilinear interpolation is performed based on the film thickness measurement values ​​on the regular grid to generate a film thickness distribution field with the same spatial range as the surface image.

[0090] After spatial registration is completed, the Euclidean distance between the actual mapped position and the target position of each corresponding feature point in the reference coordinate system is calculated, and the maximum value is determined as the spatial registration error. .

[0091] When the spatial registration error When the thickness is less than 2 mm, the surface image, the film thickness distribution field, and the infrared thermogram are determined as valid registration data; when the spatial registration error is less than 2 mm... When the difference exceeds 2 mm, an invalid multi-source data registration marker is generated, and image registration is re-performed or the corresponding data is reacquired. When multi-source data registration is invalid, local shrinkage risk values ​​or microcrack risk values ​​are not calculated based on the corresponding candidate image defects.

[0092] For the A candidate image defect region is defined, and the region range of the candidate image defect region in the reference coordinate system is determined as follows: and the range of the region Extending outwards by 2 millimeters along the region boundary, we obtain the cross-validation region. The cross-validation region is used to reduce the impact of image registration errors and the spatial dispersion of film thickness measurement points on the cross-validation results.

[0093] 2. Determination of evidence of abnormal film thickness.

[0094] Based on the film thickness distribution field, the cross-validation region is extracted. The film thickness value within the cross-validation region. Let the number of effective grid points in the film thickness distribution field within the cross-validation region be... , No. The interpolation film thickness corresponding to each effective grid point is: The first one is determined according to the following formula. Local average film thickness corresponding to the defect region in each candidate image : ; Let the number of effective grid points in the film thickness distribution field of the entire coating area be... , No. The interpolation film thickness corresponding to each effective grid point is: The average film thickness of the coated area is determined according to the following formula. : ; The number is determined according to the following formula. The relative deviation of local film thickness corresponding to the defect region in each candidate image : ; Based on the local film thickness relative deviation Determine the evidence value for abnormal film thickness according to the following formula. : ; In the formula, This indicates that the defect region in the candidate image also contains local film thickness anomalies. This indicates that no corresponding evidence of local film thickness anomalies was obtained from the film thickness distribution.

[0095] When the cross-validation region There are no effective film thickness distribution field grid points within the coating area, or the average film thickness of the coating area is... When the value is 0, an invalid marker is generated for the film thickness verification data. Cross-validation of the defective region of the candidate image is not performed, and the film thickness distribution data is reacquired.

[0096] 3. Determination of evidence of thermal anomalies.

[0097] The infrared thermal image with the smallest time difference from the surface image acquisition time, and with a time difference not exceeding 10 minutes, is selected as the corresponding verification thermal image.

[0098] Based on the spatial registration results, extract the region located in the cross-validation area from the corresponding validation heatmap. The number of effective temperature pixels inside is , No. The temperature corresponding to each effective temperature pixel is: The local average surface temperature is determined according to the following formula. : ; Let the number of effective temperature pixels in the entire coated area of ​​the corresponding verification thermal image be . , No. The temperature corresponding to each effective temperature pixel is: The overall average surface temperature of the coated area is determined according to the following formula. : ; The number is determined according to the following formula. Local temperature deviation corresponding to the defect region in each candidate image : ; Based on the local temperature deviation The evidence value for thermal anomalies is determined according to the following formula. : ; In the formula, The unit is Celsius; This indicates that the defective region in the candidate image also exhibits local thermal anomalies. This indicates that no evidence of local thermal anomalies was obtained from the corresponding verification heatmap.

[0099] If there is no infrared thermal image with a time difference of no more than 10 minutes between the acquisition time and the acquisition time of the surface image, or if there are no valid temperature pixels in the cross-validation area, an invalid thermal validation data marker is generated, the cross-validation of the defective area of ​​the candidate image is not continued, and infrared thermal image data is reacquired.

[0100] 4. Determination of evidence of abnormal quality degradation.

[0101] The residual moisture risk value generated according to the method described above in the instruction manual As a quantitative result of the quality degradation state, the residual moisture risk value is jointly determined by the residual mass deviation value at the curing endpoint, the mass degradation rate deviation value, and the mass stabilization time deviation value, reflecting the moisture evaporation and quality stability state of the same coated area. Based on the residual moisture risk value... The evidence value for anomalies in mass degradation is determined according to the following formula. : ; The same quality attenuation anomaly evidence value is used for all candidate image defect areas within the same coating area. This quality attenuation anomaly evidence is used to characterize whether there is a delay in moisture evaporation or anomaly in quality stability during the overall film formation process of the coating area, and it is not used to individually determine the spatial location of candidate image defect areas.

[0102] 5. Confirmation of defects in candidate images.

[0103] Based on the evidence value of the film thickness anomaly The thermal anomaly evidence value and the aforementioned quality degradation anomaly evidence value The first one is determined according to the following formula. Cross-validation support for each candidate image defect region : ; When the cross-validation support satisfies When at least two of the following data types—film thickness distribution, infrared thermal state, and mass decay state—provide anomalous support for the candidate image defect region, the candidate image defect region is determined as the image defect region retained through cross-validation.

[0104] When the candidate image defect region belongs to the candidate local contraction region and the cross-validation support meets the retention condition, the candidate local contraction region is determined as a valid local contraction region; when the candidate image defect region belongs to the candidate microcrack region and the cross-validation support meets the retention condition, the candidate microcrack region is determined as a valid microcrack region.

[0105] The effective local shrinkage region refers to the candidate local shrinkage region that is retained after cross-validation and used to calculate the local shrinkage risk value; the effective microcrack region refers to the candidate microcrack region that is retained after cross-validation and used to calculate the microcrack risk value.

[0106] When the cross-validation support satisfies If the multi-source data other than the surface image does not provide sufficient support for the candidate image defect region, the candidate image defect region is marked as an image interference candidate region. The image interference candidate region is not determined as a valid local shrinkage region or a valid microcrack region, and is not included in the calculation of the local shrinkage risk value or the microcrack risk value.

[0107] The evidence of quality degradation anomalies is part of the overall film formation status of the coated area and cannot alone pass cross-validation for candidate image defect regions. Candidate image defect regions are only retained when at least two of the evidences of film thickness anomalies, thermal anomalies, and quality degradation anomalies are equal to 1.

[0108] Through the above cross-validation, candidate abnormal regions formed by illumination shadows, local reflections, and attached dust in the surface image, when they do not simultaneously correspond to local film thickness abnormalities, local thermal abnormalities, or overall quality degradation abnormalities, have a cross-validation support of less than two-thirds. Therefore, they are marked as image interference candidate regions and excluded, reducing the misidentification of defects caused by a single surface image.

[0109] A local contraction risk value is generated based on the effective local contraction region. The effective local contraction region is the candidate local contraction region retained after the multi-source cross-validation.

[0110] Let the effective area of ​​the corresponding coating region be... The number of effective local contraction areas is , No. The area of ​​each effective local contraction region is The total area of ​​local contraction is determined according to the following formula. : ; The proportion of localized contraction area is determined using the following formula. : ; Based on the proportion of the localized contraction area The area risk component is determined according to the following formula. : ; Through the above piecewise linear mapping, when At that time, the area risk component for ;when At that time, the area risk component for ;when At that time, the area risk component for ;when At that time, the area risk component for ;when Reaching or exceeding At that time, the area risk component for .

[0111] Based on the number of localized contraction areas The quantity risk component is determined according to the following formula. : ; When there is no local contraction region, the quantity risk component for When the number of areas to be contracted reaches or exceeds At any given time, the quantity risk component for .

[0112] Furthermore, based on the distance between each effective local shrinkage area and the coating boundary, and the overlap between each effective local shrinkage area and the preset key coating area, the risk component of the local shrinkage location is determined.

[0113] Before identifying local shrinkage areas, the preset key coating areas are determined according to the coating process document corresponding to the target substrate and mapped to the image coordinate system where the surface image is located. The preset key coating areas include the main vibration-affected areas, main load-bearing areas, and edge sealing areas marked in the coating process document. When the coating process document does not set preset key coating areas, these areas are designated as empty areas.

[0114] Let the first The minimum distance between the effective local shrinkage area and the coating boundary is Preset boundary affects distance For a length of 10 millimeters, determine the first millimeter according to the following formula. Boundary position coefficient of each effective local contraction region : In the formula, when the first... When an effective local shrinkage area is connected to the coating boundary, The corresponding boundary position coefficient =1; when the minimum distance is 1 When the value is 5 mm, the boundary position coefficient It is 0.5; when the minimum distance is... When the boundary position coefficient reaches or exceeds 10 mm, Let the first be 0. The area of ​​the portion where the effective local shrinkage area overlaps with the preset key coating area is , No. The area of ​​each effective local contraction region is The first one is determined according to the following formula. The overlap coefficient of key areas in an effective local contraction zone : ; In the formula, the overlap coefficient of the key area Located in the range of 0 to 1. When the first When all effective local shrinkage areas are located within the preset key coating area When the first When the effective local shrinkage area does not overlap with the preset key coating area .

[0115] According to the boundary position coefficient Coefficient of overlap with the key area The first one is determined according to the following formula. Location coefficient of an effective local contraction zone : ; The position coefficient Characterizing the first The location risk of an effective local shrinkage area arises from its proximity to the coating boundary or its location within a pre-defined key coating area. When the boundary location coefficient and the key area overlap coefficient differ, the larger one is used as the location coefficient of the effective local shrinkage area.

[0116] Based on the area of ​​each effective local contraction region and its corresponding location coefficient, the local contraction location risk component is determined according to the following formula. : ; In the formula, For the risk component of local contraction location, To determine the number of effective local contraction areas, For the first The area of ​​each effective local contraction region For the first The location coefficient of an effective local contraction region This represents the total area of ​​the localized contraction.

[0117] When effective local contraction regions exist, the location coefficients of each effective local contraction region are weighted and averaged to obtain the local contraction location risk component; when no effective local contraction regions exist, the local contraction location risk component is set to 0. Since all location coefficients are within the range of 0 to 1, the local contraction location risk component is also within the range of 0 to 1.

[0118] The local shrinkage location risk component is used to simultaneously characterize the degree to which the local shrinkage area is close to the coating boundary, and the degree of overlap between the local shrinkage area and the preset key coating area. The closer the effective local shrinkage area is to the coating boundary, or the higher the proportion of its area located within the preset key coating area, the greater the local shrinkage location risk component.

[0119] According to the area risk component The aforementioned quantity risk component and the location risk component The risk value of local contraction is determined according to the following formula. : ; In the formula, the area risk component, quantity risk component, and location risk component are all within the range of 0 to 1, therefore the local contraction risk value Located within the range of 0 to 1. The area risk component is used to characterize the overall coverage of local shrinkage, the quantity risk component is used to characterize the dispersion of local shrinkage, and the location risk component is used to characterize the proximity between the local shrinkage area and the coating boundary, as well as the spatial overlap between the local shrinkage area and the preset key coating area.

[0120] Based on the above calculations, the proportion of the same localized shrinkage area, the number of localized shrinkage regions, and the overlap ratio of key areas uniquely correspond to a localized shrinkage risk value. The larger the total localized shrinkage area, the more localized shrinkage regions there are, and the higher the proportion of localized shrinkage regions located within the preset key coating area, the greater the localized shrinkage risk value.

[0121] Microcrack risk values ​​are generated based on the effective microcrack regions. The effective microcrack regions are the candidate microcrack regions retained after multi-source cross-validation.

[0122] Each effective microcrack region is subjected to skeletonization processing. The length of the skeleton centerline is determined as the crack length of the corresponding effective microcrack region, and the maximum crack width of the corresponding effective microcrack region is determined based on the maximum vertical distance between crack boundaries.

[0123] Let the number of effective microcrack regions be... , No. The crack length of each effective microcrack region is The total crack length is determined by the following formula. : ; The number of effective microcrack regions with a crack length of not less than 5 mm is counted to obtain the number of long cracks. Let the maximum crack width in all effective microcrack regions be... .

[0124] Based on the total crack length The crack length risk component is determined according to the following formula. : ; In the formula, The unit is millimeters. 50 millimeters corresponds to the cumulative crack length benchmark formed by 10 cracks, each with a length of not less than 5 millimeters.

[0125] Based on the number of long cracks The risk component of the number of cracks is determined according to the following formula. : ; According to the maximum crack width The crack width risk component is determined according to the following formula. : ; In the formula, The unit is millimeters, and 0.20 millimeters is the preset severe crack width. In this embodiment, the preset severe crack width is determined based on the surface image resolution and crack calibration samples.

[0126] Furthermore, the connected microcrack region containing at least two bifurcation points is defined as a network crack region. Let the first... The maximum projected length of each connected microcrack region along the width of the coated area is: The maximum projected length of each connected microcrack region is The effective width of the coating area is The total area of ​​all network crack regions is The effective area of ​​the coated region is .

[0127] Determine the crack connectivity risk component as follows: : ; When the projected length of any connected crack in the width direction of the coating area reaches 30% of the effective width of the coating area, or when the total area of ​​the network crack area reaches 5% of the effective area of ​​the coating area, the crack connectivity risk component is 1.

[0128] Based on the crack length risk component, crack number risk component, crack width risk component, and crack connectivity risk component, the microcrack risk value is determined according to the following formula. : ; The microcrack risk value It is located in the range of 0 to 1. The higher the microcrack risk value, the more severe the cumulative crack length, the number of long cracks, the maximum crack width, or the degree of crack connectivity.

[0129] When there is no effective microcrack region, the crack length risk component, crack number risk component, crack width risk component, crack connectivity risk component, and microcrack risk value are all set to 0.

[0130] Furthermore, based on the residual moisture risk value, the film thickness uniformity value, the surface thermal uniformity value, the local shrinkage risk value, and the microcrack risk value, the film-forming latent state characteristics are generated in a fixed arrangement order.

[0131] The film-forming latent state features are specifically represented as a five-dimensional numerical column vector, denoted as... It can be expressed as follows: ; In the formula, This refers to the latent state characteristics of the film formation; This represents the residual moisture risk value. This represents the film thickness uniformity value. This represents the surface thermal uniformity value. This represents the risk value for localized contraction. This represents the risk value for microcracks.

[0132] The film-forming latent state characteristics for The numerical column vector. The five feature dimensions are fixedly arranged in the order of residual moisture risk value, film thickness uniformity value, surface thermal uniformity value, local shrinkage risk value, and microcrack risk value, and the arrangement order is not changed according to the sample to be evaluated, the target substrate, or the application scenario.

[0133] The residual moisture risk value Film thickness uniformity value Surface thermal uniformity value Local contraction risk value and microcrack risk value All values ​​are within the range of 0 to 1. The residual moisture risk value is mentioned below. Local contraction risk value and microcrack risk value This is a risk-related status value; a higher value indicates a higher risk of film formation defects. The film thickness uniformity value... and surface thermal uniformity value This is a positive state value for integrity; the larger the value, the more uniform the corresponding film formation.

[0134] In generating the film-forming latent state features At that time, the five state values ​​are not weighted and summed, nor are they pre-converted into state levels. Instead, the original values ​​of each state value and their evaluation direction are retained, so that the latent state characteristics of film formation can respectively characterize the five film formation state dimensions of residual moisture, film thickness spatial consistency, surface thermal state consistency, local shrinkage, and microcracks.

[0135] When generating a comprehensive index of film integrity based on the aforementioned latent film-forming state characteristics, the risk-type state values ​​in the latent film-forming state characteristics are then converted into integrity-related values. Specifically, the residual moisture risk value is... Local contraction risk value and microcrack risk value Convert to , and The film thickness uniformity value and surface thermal uniformity value This remains unchanged. Therefore, all state values ​​involved in the calculation of the film integrity comprehensive index satisfy the evaluation principle that a larger value indicates a more complete film formation state.

[0136] For each coated area on the same target substrate, a corresponding latent state feature of film formation is generated. The data is associated with the target substrate number and coating area number, and with the corresponding film formation process data. When the same target substrate contains multiple coating areas, the state values ​​of different coating areas do not mix with each other, and each coating area forms an independent five-dimensional latent state feature of film formation.

[0137] If any of the residual moisture risk value, film thickness uniformity value, surface thermal uniformity value, local shrinkage risk value, and microcrack risk value contains an invalid data marker, the film formation latent state feature for the corresponding coated area will not be generated. Instead, an invalid film formation latent state feature marker will be generated, and the corresponding data will be re-acquired or reprocessed according to the corresponding invalid data reason. Only when all five state values ​​are valid values ​​will the film formation latent state feature be generated in the above fixed order. .

[0138] The film-forming latent state characteristics This excludes the subsequently generated comprehensive film integrity index, film state level, predicted damping contribution value, and actual damping contribution value. The comprehensive film integrity index is a comprehensive value further calculated in step S5 based on the five feature dimensions of the latent film state characteristics; the film state level is the interpretation result further determined in step S6 based on the latent film state characteristics.

[0139] It should also be noted that the beneficial effect of step S2 is that it transforms internal residual water, uneven local curing, abnormal film thickness, and surface continuity defects, which are difficult to observe directly during the film formation process, into calculable latent state characteristics of the film formation. Compared with simply observing whether the surface is cracked after curing, this step can make a joint judgment using quality time-series changes, temperature and humidity conditions, infrared thermal state, and surface images. For example, when there are no obvious cracks on the surface of the damping layer, but the film formation quality time-series data shows that the quality stabilization time is prolonged by more than 90 minutes, and the infrared thermal image shows that the low-temperature area exceeds 10% of the coated area, it can be judged that there is a high risk of residual moisture. This risk will reduce the predicted damping contribution value in subsequent steps, thereby avoiding misjudging samples with intact surfaces but insufficient internal film formation as being in good condition. Thus, this step not only completes the data conversion but also provides physically meaningful intermediate features for subsequent prediction of damping contribution values ​​and intelligent state assessment results.

[0140] S3: Determine the key resonant frequency band of the target substrate based on the vibration response data before coating, and generate frequency band weights based on the vibration energy distribution within the key resonant frequency band.

[0141] Based on the excitation frequency and vibration acceleration in the vibration response data before coating, the frequency response curve and vibration energy distribution curve of the target substrate before coating are generated. The key resonant frequency band of the target substrate is determined based on the resonant peak frequency, resonant peak amplitude, and half-power bandwidth in the frequency response curve. The vibration energy within each of the key resonant frequency bands is determined based on the vibration energy distribution curve. Based on the proportion of vibration energy in each key resonance frequency band to the total vibration energy of all key resonance frequency bands, the frequency band weights corresponding to each key resonance frequency band are generated.

[0142] In this embodiment, step S3 is used to determine the key resonant frequency bands where the target substrate is prone to vibration amplification based on its vibration response state before the water-based damping material is coated, and further generate frequency band weights based on the vibration energy distribution within each key resonant frequency band. It should be noted that the vibration response data before coating is selected as the analysis object in this step because the vibration response before coating can reflect the inherent vibration-sensitive areas of the target substrate itself, and the actual damping effect of the subsequent water-based damping material is mainly reflected in the suppression effect on these vibration-sensitive areas. If only the vibration response data after coating is used to determine the evaluation frequency bands, it may be affected by the attenuation effect already generated by the damping layer, leading to the weakening or omission of the resonant areas that should be emphasized for evaluation. Therefore, this embodiment first determines the key resonant frequency bands based on the vibration response data before coating, and then uses these key resonant frequency bands to generate the subsequent predicted damping contribution value and the actual damping contribution value, so that the evaluation object is consistent with the actual vibration reduction requirements of the target substrate.

[0143] Furthermore, based on the excitation frequency and vibration acceleration in the pre-coating vibration response data, a frequency response curve of the target substrate before coating is generated. Specifically, each excitation frequency point is paired with its corresponding vibration acceleration amplitude, and arranged in ascending order of excitation frequency to form a frequency response curve of vibration acceleration amplitude as a function of excitation frequency. The vibration acceleration amplitude can be the peak value, effective value, or amplitude obtained through spectral analysis at the same frequency point; the specific selection method should be consistent across the same set of pre-coating and post-coating tests. To reduce the impact of test noise on resonance peak identification, the frequency response curve can be smoothed. This smoothing can be achieved by averaging adjacent frequency points, moving window smoothing, or the built-in spectral smoothing method of the vibration testing system. It should be noted that when the sudden change in response amplitude between adjacent frequency points exceeds 50% of the average amplitude of the previous five frequency points, and this change only occurs at a single frequency point, this frequency point can be marked as an isolated noise point and replaced with the response amplitude of adjacent frequency points to avoid misidentifying test noise as a resonance peak.

[0144] It should also be noted that the sampling interval of the excitation frequency in the frequency response curve affects the accuracy of formant identification. For a sweep frequency range of 20 Hz to 2000 Hz, the frequency sampling interval can be set to 1 Hz to 5 Hz; for target substrates with denser formant peaks, the frequency sampling interval can be set to 0.5 Hz to 2 Hz; when the frequency interval between two adjacent formant peaks is less than 20 Hz, localized encrypted sampling can be performed within this frequency band to make the identification of formant peak frequency, formant peak amplitude, and half-power bandwidth more accurate. Denserity of formant peaks refers to the identification of more than three local peaks within a 100 Hz range, and each local peak is higher than 1.5 times the average response amplitude of the adjacent frequency band.

[0145] Furthermore, a vibration energy distribution curve is generated based on the excitation frequency and vibration acceleration. Specifically, the vibration acceleration amplitude corresponding to the same frequency point can be converted into a relative vibration energy value at that frequency point, which can be represented by the square of the vibration acceleration amplitude. When the frequency sampling intervals are inconsistent, the energy contribution at each frequency point can be further determined by combining the frequency intervals corresponding to that frequency point. For ease of understanding, this can be represented as follows: ; In the formula, Indicates the first The relative vibrational energy value at each frequency point Indicates the first The vibration acceleration amplitude corresponding to each frequency point Indicates the first The frequency interval corresponding to each frequency point This indicates the frequency point number. When the frequency intervals between each frequency point are the same, and it is only used to compare the energy magnitude within different frequency bands, the square of the vibration acceleration amplitude can also be directly used as the relative vibration energy value. By arranging the relative vibration energy values ​​of each frequency point in frequency order, the vibration energy distribution curve can be obtained.

[0146] It should be noted that the frequency response curve and the vibration energy distribution curve serve different processing purposes in this step. The frequency response curve is mainly used to identify the resonant frequency, resonant amplitude, and half-power bandwidth, thereby determining the location and boundaries of key resonant frequency bands; the vibration energy distribution curve is mainly used to calculate the vibration energy within each key resonant frequency band, thereby generating the corresponding frequency band weights for each key resonant frequency band. Together, they enable this step to determine both "which frequency ranges to evaluate" and "what proportion of each frequency range will be weighted in the subsequent evaluation."

[0147] It should also be noted that the vibration energy distribution curve does not require obtaining the absolute mechanical energy of the target substrate, but rather is used to compare the relative magnitudes of the vibration response contributions in different frequency ranges under the same test conditions. Therefore, even using relative vibration energy values ​​can meet the requirements for generating frequency band weights in this invention. The higher the relative vibration energy value, the stronger the vibration response of the target substrate near that frequency point, and the more attention should be paid to the frequency band where that frequency point is located in the subsequent evaluation of damping effect.

[0148] Furthermore, the key resonant frequency band of the target substrate is determined based on the resonant peak frequency, resonant peak amplitude, and half-power bandwidth in the frequency response curve. Specifically, in the frequency response curve, frequency points with response amplitudes greater than those of their adjacent frequency points are identified as candidate peak points. When the response amplitude of a candidate peak point is higher than 1.5 times the average response amplitude within a preset frequency range before and after the candidate peak point, the candidate peak point can be identified as a resonant peak. The preset frequency range can be 10 Hz to 50 Hz on both sides of the resonant peak, and the specific range can be determined based on the sweep frequency range, substrate size, and resonant peak distribution density. When the response amplitude of a candidate peak point is only less than 1.2 times higher than the surrounding average response amplitude, its peak characteristic is considered weak and it is not considered a key resonant peak; when the response amplitude of a candidate peak point is more than 2 times higher than the surrounding average response amplitude, its peak characteristic is considered significant and it is preferentially considered a key resonant peak.

[0149] It should also be noted that after determining the resonance peak, the frequency corresponding to the resonance peak is taken as the resonance peak frequency, and the vibration response amplitude corresponding to the resonance peak is taken as the resonance peak amplitude. The resonance peak amplitude is then divided by... The half-power amplitude is obtained. On the low-frequency and high-frequency sides of the resonance peak, the first half-power frequency and the second half-power frequency corresponding to when the vibration response amplitude equals the half-power amplitude are determined according to the linear interpolation method described above. The difference between the second half-power frequency and the first half-power frequency is determined as the half-power bandwidth. A candidate resonance band centered on the resonance peak frequency is formed based on the first half-power frequency and the second half-power frequency.

[0150] In cases where another resonance peak exists between two half-power frequencies, the composite loss factor is not calculated based on this resonance peak. However, the candidate resonance bands can be merged to form a key resonance band based on the overlap relationship between the candidate resonance bands corresponding to adjacent resonance peaks. Therefore, overlapping resonance peaks do not participate in the calculation of the single-peak composite loss factor, but they do not affect the subsequent determination of the key resonance band and vibration energy.

[0151] Furthermore, not all candidate resonance bands are directly designated as critical resonance bands. To avoid including test noise, local weak peaks, or non-service-related frequency bands in subsequent evaluations, this embodiment can screen candidate resonance bands based on resonance peak amplitude, half-power bandwidth, and vibration energy magnitude. Specifically, when the resonance peak amplitude within a candidate resonance band is more than 1.5 times higher than the average response amplitude across the entire frequency band, and the vibration energy within that candidate resonance band accounts for more than 5% of the total vibration energy of all candidate resonance bands, it can be identified as a critical resonance band. When the vibration energy percentage within a candidate resonance band is less than 2%, and the band does not belong to the expected service frequency band of the target substrate, it can be excluded from being considered a critical resonance band. The expected service frequency band refers to the frequency range in which the target substrate is frequently excited or requires focused vibration suppression in actual use scenarios. For example, for thin-plate structures in vehicles, the range of 20 Hz to 500 Hz can be considered, and for equipment housing structures, the range of 50 Hz to 1500 Hz can be considered. The above ranges are only used to illustrate the screening method for critical resonance bands and do not limit the actual application scenarios of the target substrate.

[0152] It should also be noted that when two candidate resonance bands overlap, or the interval between two candidate resonance bands is less than 50% of the smaller half-power bandwidth, the two candidate resonance bands can be merged into a single key resonance band. The merged key resonance band can extend from the low-frequency boundary of the previous candidate resonance band to the high-frequency boundary of the next candidate resonance band. By merging adjacent bands, it is possible to avoid splitting the same vibration-sensitive region into multiple overly narrow bands in substrates with significant modal coupling, thereby improving the stability of subsequent band weight and damping contribution calculations. Furthermore, the vibration energy within each key resonance band is determined based on the vibration energy distribution curve. Specifically, for each key resonance band, all frequency points falling within the range of that key resonance band are extracted, and the relative vibration energy values ​​corresponding to these frequency points are accumulated to obtain the vibration energy within that key resonance band. For ease of understanding, this can be represented as follows: ; In the formula, Indicates the first Vibrational energy within a key resonant frequency band Indicates falling into the first The first key resonant frequency band within the first The relative vibrational energy value at each frequency point Indicates the first The set of frequency points corresponding to the key resonant frequency bands This indicates the key resonance frequency band number. The above cumulative results are used to characterize the contribution of the corresponding key resonance frequency band to the overall vibration response of the target substrate. The larger the value, the more energy that key resonant frequency band occupies in the vibration response before coating, and thus it should be given greater attention when evaluating the damping contribution of the waterborne damping material. Furthermore, based on the proportion of vibration energy within each key resonant frequency band to the total vibration energy of all key resonant frequency bands, a frequency band weight corresponding to each key resonant frequency band is generated. Specifically, the vibration energy within all key resonant frequency bands is first summed to obtain the total vibration energy of all key resonant frequency bands. Then, the vibration energy within each key resonant frequency band is compared with this total to obtain the frequency band weight corresponding to that key resonant frequency band. For ease of understanding, this can be represented as follows: ; In the formula, Indicates the first The frequency band weights corresponding to the key resonant frequency bands Indicates the first Vibrational energy within a key resonant frequency band This indicates the number of key resonant frequency bands. The value of the frequency band weight ranges from 0 to 1, and the sum of the frequency band weights corresponding to all key resonant frequency bands is 1. The larger the frequency band weight, the higher the contribution of the key resonant frequency band to the vibration response of the target substrate before coating. In subsequent calculations of the predicted damping contribution value and the actual damping contribution value, the damping performance of this frequency band has a greater impact on the final result.

[0153] It should also be noted that when only one key resonance frequency band is identified, the corresponding band weight can be set to 1; when multiple key resonance frequency bands are identified, the weight of each band is determined according to its respective vibration energy proportion. If the band weight of a candidate resonance frequency band is less than 0.03 and does not belong to the expected service frequency band, it may not be included in the final set of key resonance frequency bands; if the frequency band has already been included in the set of key resonance frequency bands, it can be marked as a low-contribution frequency band and its participation weight reduced. If the low-contribution frequency band belongs to the expected service frequency band, it will still be retained and designated as a frequency band of special concern to avoid ignoring low-energy frequency bands that may be continuously excited in actual use.

[0154] Furthermore, after generating the band weights, the key resonant band, resonant peak frequency, resonant peak amplitude, half-power bandwidth, band vibration energy, and band weights can be stored together and associated with the target substrate number and coating area number. For cases where the same target substrate has multiple coating areas, the key resonant band and band weights corresponding to different coating areas can be stored separately. For cases where multiple coating areas of the same target substrate use the same pre-coating overall vibration response data, the same set of key resonant bands and band weights can be associated with each coating area, and then corrected in subsequent steps based on the damping layer coverage and damping layer thickness of each coating area.

[0155] It should also be noted that the beneficial effect of this step lies in its transformation of the vibration response characteristics of the target substrate before coating into the key resonant frequency bands and band weights required for subsequent damping contribution evaluation. Compared with the average evaluation of the vibration response across the entire frequency band, this step can avoid the response changes in non-key frequency bands masking the insufficient damping in the key resonance region. For example, when the aqueous damping material exhibits significant response attenuation in the high-frequency, low-energy range, but the attenuation is insufficient in the low-frequency main resonance range of the target substrate, the traditional average evaluation across the entire frequency band may still give a high damping effect evaluation; however, this embodiment, through the key resonant frequency bands and band weights, ensures that the low-frequency main resonance range occupies a higher proportion in the subsequent true damping contribution value, thereby more accurately reflecting the degree to which the aqueous damping material meets the actual vibration reduction requirements of the target substrate.

[0156] It should also be noted that this step provides a unified frequency band evaluation scale for the subsequent generation of predicted damping contribution values. Since the predicted damping contribution value in step S5 is generated based on the contribution of the predicted loss factor curve within the key resonant frequency band, and the actual damping contribution value is generated based on the response improvement of standard vibration response data before and after coating within the key resonant frequency band, the key resonant frequency band and frequency band weights generated in this step allow the predicted and measured sides to be compared around the same set of target frequency bands. Therefore, the deviation between the predicted and actual damping contribution values ​​in step S6 is no longer a difference between evaluation objects at different frequencies, but rather a difference in contribution within the same key resonant frequency band evaluation framework, thereby improving the interpretability and reliability of the film formation contribution deviation results.

[0157] S4: Based on the film formation environment parameters and the vibration response test environment parameters, the vibration response data before coating and the vibration response data after coating are normalized to standard operating conditions to obtain standard vibration response data before coating and standard vibration response data after coating.

[0158] A film formation environment correction coefficient is generated based on the deviation of the film formation environment parameters relative to the preset standard film formation conditions, and a test environment correction coefficient is generated based on the deviation of the vibration response test environment parameters relative to the preset standard test conditions. An environmental mapping relationship is constructed based on the film formation environment correction coefficient and the test environment correction coefficient; Based on the test environment correction coefficient, the vibration acceleration, resonance peak amplitude and half-power bandwidth in the pre-coating vibration response data are normalized to standard operating conditions to obtain the pre-coating standard vibration response data. Based on the environmental mapping relationship, the vibration acceleration, resonance peak amplitude, half-power bandwidth and composite loss factor in the post-coating vibration response data are normalized according to standard operating conditions to obtain the post-coating standard vibration response data. Based on the key resonance frequency band, the standard vibration response data before coating and the standard vibration response data after coating are mapped to the same frequency sampling points to generate standard response pairing data before and after coating. Calculate the first response correction difference between the vibration response data before coating and the standard vibration response data before coating at the same frequency sampling point, and the second response correction difference between the vibration response data after coating and the standard vibration response data after coating at the same frequency sampling point, and generate environmental normalized residual values ​​based on the first response correction difference and the second response correction difference. A standard response confidence level is generated based on the environmental normalized residual value, and the pairwise standard response data before and after coating is marked for validity based on the standard response confidence level.

[0159] In this embodiment, step S4 is used to perform standard operating condition normalization processing on the vibration response data before and after coating based on the film-forming environment parameters and vibration response test environment parameters. This allows vibration response data obtained under different film-forming and test environments to be converted to a unified standard operating condition for comparison. It should be noted that waterborne damping materials typically exhibit viscoelastic characteristics, and their vibration energy dissipation may change with temperature and humidity. The elastic response of the target substrate, the stability of the sensor output, and the operating state of the excitation device may also be affected by the test temperature and humidity. Directly comparing the original pre-coating and post-coating vibration response data may misinterpret the response differences caused by environmental changes as differences in the damping layer's contribution, leading to subsequent true damping contribution values ​​that cannot accurately reflect the actual vibration reduction effect of the waterborne damping material on the target substrate. Therefore, this step, through standard operating condition normalization processing, reduces the confounding influence of the film-forming and test environments on the vibration response data, providing a unified and reliable data foundation for the subsequent calculation of the true damping contribution value.

[0160] Furthermore, a film-forming environment correction coefficient is generated based on the deviation of the film-forming environment parameters relative to the preset standard film-forming conditions. The preset standard film-forming conditions can be determined based on calibration tests of the same type of waterborne damping material. For example, a film-forming temperature of 20°C to 30°C and a film-forming humidity of 40% to 65% can be used as the standard film-forming condition range. When the film-forming temperature is around 25°C and the film-forming humidity is around 50%, it can be considered the center condition of the standard film-forming conditions. The deviation of the film-forming environment parameters relative to the preset standard film-forming conditions includes film-forming temperature deviation and film-forming humidity deviation. A small film-forming temperature deviation means that the difference between the actual film-forming temperature and the center value of the standard film-forming temperature does not exceed 3°C; a large film-forming temperature deviation means that the difference exceeds 8°C. A small film-forming humidity deviation means that the difference between the actual film-forming humidity and the center value of the standard film-forming humidity does not exceed 10%; a large film-forming humidity deviation means that the difference exceeds 25%. Through the above ranges, the judgment criteria for film-forming environment deviation can be clearly defined.

[0161] It should also be noted that the film-forming environment correction coefficient can be established using calibration samples. Specifically, qualified samples formed under standard film-forming conditions using the same type of waterborne damping material can be selected as reference samples. Calibration samples are then prepared under different film-forming conditions, such as high temperature and low humidity, low temperature and high humidity, and normal temperature and high humidity. The film-forming environment parameters, film-forming process data, and post-coating vibration response data of each calibration sample are recorded. By comparing the changes in vibration response of the calibration samples relative to the reference samples under different film-forming conditions, the degree of influence of film-forming temperature offset and film-forming humidity offset on the correctable environmental influence portion of the post-coating vibration response data can be determined, and the film-forming environment correction coefficient can be generated accordingly. The high temperature and low humidity can be a film-forming temperature above 35 degrees Celsius and a film-forming humidity below 40%; the low temperature and high humidity can be a film-forming temperature below 10 degrees Celsius and a film-forming humidity above 80%; and the normal temperature and high humidity can be a film-forming temperature between 20 and 30 degrees Celsius and a film-forming humidity above 75%. The above operating conditions are used to cover the common film-forming offset states of water-based damping materials, so that the film-forming environment correction coefficient has a calibration basis, rather than being arbitrarily set based on subjective experience.

[0162] Furthermore, the role of the film-forming environment correction factor is not to directly correct abnormal film-forming states to standard film-forming states, but rather to characterize the impact of film-forming environment deviations on the reversible environmental disturbance portion of the post-coating vibration response data. For genuine film-forming defects caused by residual moisture, uneven film thickness, local shrinkage, and microcracks, this step does not eliminate them using the film-forming environment correction factor. In other words, the film-forming environment correction factor is used to improve the comparability of data under different film-forming conditions, but does not change the actual structural state of the damping layer itself, to avoid the subsequent loss of the ability of the actual damping contribution value to reflect differences in film-forming quality.

[0163] Furthermore, a test environment correction coefficient is generated based on the deviation of the vibration response test environment parameters relative to the preset standard test conditions. The preset standard test conditions can be test temperatures of 20°C to 25°C and test humidity of 40% to 60%. A small test temperature deviation means the difference between the actual test temperature and the center value of the standard test temperature does not exceed 2°C; a large test temperature deviation means the difference exceeds 5°C. A small test humidity deviation means the difference between the actual test humidity and the center value of the standard test humidity does not exceed 8%; a large test humidity deviation means the difference exceeds 20%. When the test temperature or test humidity deviation is large, the stiffness response of the target substrate, the viscoelastic state of the damping layer, the stability of the sensor output, and the working state of the excitation device may all be affected. Therefore, it is necessary to generate a test environment correction coefficient to normalize the vibration response data before and after coating to standard conditions.

[0164] It should also be noted that the test environment correction factor can be obtained through repeated test calibration. Specifically, the same target substrate or the same standard sample can be selected, and vibration response tests can be performed under preset standard test conditions to obtain standard test response data. Then, repeated tests can be performed under different test temperatures and different test humidity conditions to obtain offset test response data. By comparing the changes in offset test response data with standard test response data, the influence of test temperature offset and test humidity offset on vibration acceleration, resonance peak amplitude, and half-power bandwidth can be determined. If, under a certain test environment, the resonance peak amplitude of the same standard sample changes by more than 5% relative to the standard test conditions, the test environment can be considered to have a correctable influence on the response amplitude; if the half-power bandwidth changes by more than 3%, the test environment can be considered to have a correctable influence on the damping-related response. The above calibration results can be saved in the form of correction tables, lookup tables, piecewise correction rules, or calibration models.

[0165] Furthermore, an environmental mapping relationship is constructed based on the film-forming environment correction coefficient and the test environment correction coefficient. This environmental mapping relationship describes the change in vibration response data after coating relative to vibration response data under standard operating conditions under the combined effects of film-forming environment offset and test environment offset. It should be noted that the vibration response data before coating is collected when the target substrate is not coated with the water-based damping material, and is not affected by the film-forming environment. Therefore, the standard vibration response data before coating is mainly generated based on the test environment correction coefficient. The vibration response data after coating is collected after the damping layer has formed and cured, and is affected by both the film-forming process and the test environment. Therefore, the standard vibration response data after coating needs to be generated based on the environmental mapping relationship jointly constructed by the film-forming environment correction coefficient and the test environment correction coefficient. This processing method can maintain consistency between the data processing logic before and after coating and the actual physical process, avoiding the incorrect application of film-forming stage factors to the pre-coating data.

[0166] It should also be noted that the environmental mapping relationship in this embodiment is established using a preset environmental correction calibration table and interpolation processing, and does not simultaneously use segmentation rules, linear models or nonlinear models.

[0167] For vibration acceleration, resonance peak amplitude, and half-power bandwidth, corresponding test environment correction calibration tables and film formation environment correction calibration tables are established respectively. The test environment correction calibration table uses test temperature and test humidity as indexes to record the response correction coefficients of the same standard sample under different test environments relative to the preset standard test conditions; the film formation environment correction calibration table uses film formation temperature and film formation humidity as indexes to record the reversible response correction coefficients of the same type of waterborne damping material under different film formation environments relative to the preset standard film formation conditions.

[0168] When the actual temperature and humidity match the calibration nodes in the calibration table, the corresponding correction coefficient is read directly. When the actual temperature or humidity is between adjacent calibration nodes, four adjacent calibration nodes surrounding the actual temperature and humidity are selected, and bilinear interpolation is used to determine the corresponding correction coefficient.

[0169] Let the first The film formation environment correction factor corresponding to the vibration response parameters is: The test environment correction factor is The first one is determined according to the following formula. Environmental mapping coefficients corresponding to vibration response parameters : ; When normalizing the vibration response data before coating, only the test environment correction coefficient is used; when normalizing the vibration response data after coating, the environment mapping coefficient is used. The film-forming environment correction coefficient only corrects the reversible environmental response portion determined by the calibration test, and does not correct for actual film-forming defects caused by residual moisture, uneven film thickness, local shrinkage, and microcracks.

[0170] Furthermore, the vibration acceleration, resonance peak amplitude, and half-power bandwidth in the pre-coating vibration response data are normalized to standard operating conditions based on the test environment correction coefficient to obtain standard pre-coating vibration response data. Specifically, for the pre-coating vibration response data, the vibration acceleration amplitude at each frequency point is corrected based on the deviation of the test temperature and humidity from the preset standard test conditions, the resonance peak amplitude is corrected for the peak value, and the half-power bandwidth is corrected for the bandwidth. If both the test temperature and humidity are within the preset standard test conditions, the test environment correction coefficient can be taken as close to 1, and the pre-coating vibration response data only needs to be formatted and the frequency points organized; if the test temperature or humidity exceeds the preset standard test conditions, the corresponding data is normalized based on the test environment correction coefficient. It should be noted that the purpose of the normalization is not to change the vibration characteristics of the target substrate itself, but to convert data collected under different test environments to the same standard test conditions for comparison.

[0171] Furthermore, based on the environmental mapping relationship, the vibration acceleration, resonance peak amplitude, half-power bandwidth, and composite loss factor in the post-coating vibration response data are normalized to standard operating conditions to obtain standard post-coating vibration response data. Specifically, for the post-coating vibration response data, the influence of test temperature and humidity offsets on the vibration response test results is first eliminated using the test environment correction coefficient. Then, based on the environmental mapping relationship formed by the film formation environment correction coefficient, the correctable environmental influences caused by film formation environment offsets are mapped to obtain standard post-coating vibration response data under standard operating conditions.

[0172] When performing standard operating condition normalization on the composite loss factor, the vibration response amplitude corresponding to each frequency sampling point in the coated frequency response curve is first normalized according to the environmental mapping relationship to obtain the normalized coated frequency response curve.

[0173] Re-identify the resonance peaks in the normalized post-coating frequency response curve, and determine the normalized effective resonance peaks according to the aforementioned effective resonance peak determination criteria in the specification. For the first... After normalizing the effective resonance peaks, the resonance peak frequencies are redefined. resonance peak amplitude First half-power frequency Second half-power frequency The normalized half-power amplitude is determined according to the following formula: ; The normalized half-power bandwidth is determined by the following formula: ; The normalized composite loss factor is determined according to the following formula: ; In the formula, For the first The composite loss factor corresponding to the normalized effective resonance peak. This is the normalized half-power bandwidth. The normalized resonant frequency. and These are the normalized first half-power frequency and the second half-power frequency, respectively.

[0174] The normalized composite loss factor is recalculated based on the normalized post-coating frequency response curve, without directly scaling the original composite loss factor. When multiple normalized effective resonance peaks exist, the normalized composite loss factor corresponding to each effective resonance peak is calculated separately and associated according to the target substrate number, coating area number, and resonance peak number.

[0175] It should also be noted that the environmental mapping relationship is used to eliminate or reduce the reversible effects of external environmental deviations on vibration response data, but not to eliminate irreversible response differences caused by actual film-forming defects in the damping layer. The reversible effects include short-term changes in material modulus due to temperature and humidity variations, sensor response drift, excitation device output fluctuations, and response amplitude deviations caused by the test environment; the irreversible response differences include structural differences in the damping layer caused by residual moisture, uneven film thickness, local shrinkage, and microcracks. In other words, standard operating condition normalization is used to improve the comparability of data from different batches and under different test environments, but does not correct actual film-forming defects to a acceptable state. This limitation prevents the subsequent loss of the ability to reflect differences in film-forming quality due to environmental normalization.

[0176] Furthermore, based on the key resonance frequency bands, the standard vibration response data before and after coating are mapped to the same frequency sampling points to generate paired standard response data before and after coating. Specifically, using the key resonance frequency bands determined in step S3 as the processing range, a unified set of frequency sampling points is established within each key resonance frequency band. If the frequency sampling points of the standard vibration response data before and after coating are completely consistent, they are directly paired according to the same frequency points; if the two frequency sampling points are not completely consistent, adjacent frequency point interpolation, local curve fitting, or test system resampling can be used to map them to the same frequency sampling point. The same frequency sampling point refers to the frequency position within the same key resonance frequency band used to compare the difference in response before and after coating. To ensure pairing accuracy, when the frequency sampling point interval does not exceed 2 Hz, adjacent frequency points can be directly used for pairing; when the frequency sampling point interval exceeds 5 Hz, interpolation processing or re-encryption sampling can be performed.

[0177] It should also be noted that limiting the frequency alignment operation to the critical resonance frequency band, rather than performing frequency alignment on all vibration response data across the entire frequency band, aims to focus data processing and subsequent evaluation on the characteristic frequency bands where the vibration energy of the target substrate is concentrated. This reduces the interference of a large amount of low-contribution paired data in non-critical frequency bands on the subsequent calculation of environmental normalization residuals and true damping contribution values. Since the critical resonance frequency band and its weights have already been determined in step S3, this step unifies the frequency sampling points within the critical resonance frequency band, enabling subsequent comparisons to focus on the frequency range where the actual vibration reduction requirements of the target substrate are higher.

[0178] Furthermore, when generating paired standard response data before and after coating, each paired data unit includes at least a frequency sampling point, standard vibration acceleration before coating, standard vibration acceleration after coating, standard resonance peak amplitude before coating, standard resonance peak amplitude after coating, standard half-power bandwidth before coating, and standard half-power bandwidth after coating. When the frequency sampling point is near the resonance peak, the key resonance band to which the frequency point belongs, the corresponding resonance peak number, and the band weight can also be recorded. Through the above pairing method, it can be ensured that the data before and after coating have the same frequency basis when calculating the decrease in resonance peak amplitude, the increase in half-power bandwidth, and the decrease in vibration energy.

[0179] Furthermore, the first response correction difference between the pre-coating vibration response data and the pre-coating standard vibration response data at the same frequency sampling points, and the second response correction difference between the post-coating vibration response data and the post-coating standard vibration response data at the same frequency sampling points are calculated. Environmental normalization residuals are then generated based on these first and second response correction differences. It should be noted that the first response correction difference characterizes the correction magnitude of the pre-coating vibration response data by the test environment normalization process; the second response correction difference characterizes the correction magnitude of the post-coating vibration response data by the environmental mapping relationship. If both the first and second response correction differences are small, it indicates that the original test environment is close to the standard operating conditions, and the normalization process has high reliability. If one type of response correction difference is large, it indicates that this set of data is significantly affected by environmental offset, requiring a reduction in the reliability of the corresponding paired data or retesting for judgment.

[0180] For ease of understanding, the first response correction difference can be determined by the relative difference between the vibration response data before coating and the standard vibration response data before coating, and the second response correction difference can be determined by the relative difference between the vibration response data after coating and the standard vibration response data after coating. The environmental normalized residual value can be generated by the average or weighted result of the first and second response correction differences. For example, it can be expressed as follows: ; In the formula, This represents the environmentally normalized residual value. This indicates the first response correction difference. This indicates the second response correction difference. Represents the weight parameters. The value ranges from 0 to 1. When the test environment differences between the vibration response data before and after coating are small, and the film formation environment shift is not significant, A value of 0.5 can be used to ensure that the first and second response correction differences have the same effect; when the post-coating data is more significantly affected by both film formation environment shift and testing environment shift, the value can be reduced. This results in the second response correction difference having a higher proportion in the environmental normalized residuals. The higher proportion means that the second response correction difference accounts for 10% to 30% more of the proportion in the environmental normalized residuals than the first response correction difference.

[0181] It should also be noted that, and The correction difference can be determined separately based on the differences in corrections for vibration acceleration, resonant peak amplitude, and half-power bandwidth. For example, the relative differences in vibration acceleration amplitude before and after correction can be compared at the same frequency sampling points, and then the corresponding response correction difference can be formed by combining the differences in resonant peak amplitude correction and half-power bandwidth correction. When only amplitude quality control is required, the difference in vibration acceleration amplitude can be used as... and The main source; when it is necessary to control the reliability of the damped related response simultaneously, the correction differences of half-power bandwidth and composite loss factor should be included together. Using the above method, the environmental normalized residual value can reflect the overall change caused by the standard operating condition normalization process to the original vibration response data.

[0182] Furthermore, a standard response confidence level is generated based on the environmental normalized residual value. The standard response confidence level includes four categories: high confidence, medium confidence, low confidence, and invalid.

[0183] When the environmental normalized residual value does not exceed 5%, the confidence level of the standard response is determined to be high confidence, and the corresponding paired data of the standard response before and after coating are marked as valid data.

[0184] When the environmental normalized residual value is greater than 5% but not more than 15%, the confidence level of the standard response is determined to be medium confidence, and the corresponding standard response pair data before and after coating is marked as valid data, while the medium confidence mark is retained.

[0185] When the environmental normalized residual value is greater than 15% but not more than 25%, the confidence level of the standard response is determined to be low confidence, and the corresponding paired data of the standard response before and after coating is marked as data to be reviewed.

[0186] When the environmental normalized residual value exceeds 25%, the standard response confidence level is determined to be invalid, and the corresponding standard response pairing data before and after coating is marked as invalid data.

[0187] When the sensor position deviation exceeds 5 mm, the excitation position deviation exceeds 10 mm, or more than 30% of the frequency sampling points within the same key resonant frequency band are missing, the corresponding paired data is directly marked as invalid data without considering the magnitude of the environmental normalized residual value.

[0188] Specifically, when the confidence level of the standard response is high or medium, the corresponding pair of standard responses before and after coating is marked as valid data; when the confidence level of the standard response is low, the corresponding pair of standard responses before and after coating is marked as data to be reviewed; when the confidence level of the standard response is invalid, the corresponding pair of standard responses before and after coating is marked as invalid data.

[0189] The valid data is used for subsequent calculation of the true damping contribution value. The data to be verified is only used to generate retest prompts and auxiliary verification information, and does not participate in the calculation of the final true damping contribution value, contribution deviation rate, and film formation contribution deviation level. Invalid data is not included in subsequent calculations and will trigger a prompt for re-acquisition or retesting.

[0190] It should also be noted that for the same critical resonance frequency band, if the proportion of valid data to all paired data within that band is not less than 80%, the data validity of that critical resonance frequency band can be considered to meet the requirements of subsequent calculations. If the proportion of valid data is less than 50%, the data validity of that critical resonance frequency band can be considered insufficient, and the vibration test of that frequency band should be repeated or its participation in subsequent contribution calculations should be reduced. If the proportion of valid data is between 50% and 80%, the frequency band can be retained based on its weight. When the weight of that frequency band is greater than 0.3, even if the proportion of valid data is less than 80%, it should be retested first, rather than directly discarding the critical frequency band, to avoid omitting the main vibration energy concentration area of ​​the target substrate.

[0191] Furthermore, the standard vibration response data before coating, standard vibration response data after coating, paired standard response data before and after coating, environmental normalized residual values, standard response confidence, and validity markers generated in this step are all associated and saved with the target substrate number, coating area number, and key resonance frequency band number. Through this associated saving method, subsequent step S5 can extract only the data used to calculate the true damping contribution value from the valid response paired data, and can trace the standard operating condition normalization process corresponding to each true damping contribution value.

[0192] It should also be noted that the beneficial effect of step S4 is that it solves the problem of difficulty in directly comparing data under different film-forming environments and different vibration test environments. Compared with directly comparing the original vibration response data before and after coating, this step first eliminates reversible environmental disturbances through standard operating condition normalization, and then judges the reliability of the normalized data by the environmental normalization residual value and the standard response reliability. This can reduce the impact of temperature and humidity offsets, test state changes, and sampling differences on the true damping contribution value. For example, when the test temperature after coating is significantly higher than the test temperature before coating, the viscoelastic state of the damping layer may change. If the original response data is directly compared, the response change caused by temperature may be mistakenly regarded as the actual damping contribution of the water-based damping material. However, this embodiment performs standard operating condition normalization through test environment correction coefficient and environmental mapping relationship, and marks the validity of data with excessively large normalization residuals, so that the subsequent true damping contribution value can better reflect the actual effect of the damping layer itself on the key resonant frequency band of the target substrate.

[0193] It should also be noted that this step supports the reliable attribution of subsequent film formation contribution deviation results. Since the predicted damping contribution value originates from the latent characteristics of the film formation state, while the actual damping contribution value originates from paired standard response data before and after coating, if the data upon which the actual damping contribution value is based has not undergone environmental normalization, the deviation between the predicted and actual damping contribution values ​​may contain a large number of environmental interference factors, making it difficult to interpret as a film formation contribution deviation. This step, by unifying the response data before and after coating to standard operating conditions and filtering valid data through the reliability of the standard response, makes the film formation contribution deviation results generated in step S6 more likely to reflect the true difference between the film formation state and the actual damping performance, rather than a false deviation caused by environmental testing errors.

[0194] S5: Generate a predicted damping contribution value based on the film-forming latent state characteristics, and generate a true damping contribution value based on the standard vibration response data before coating, the standard vibration response data after coating, and the frequency band weight.

[0195] Step S5, which involves generating the predicted damping contribution value based on the film-forming latent state characteristics, specifically includes: The film integrity index is generated by weighting the residual moisture risk value, the film thickness uniformity value, the surface thermal uniformity value, the local shrinkage risk value, and the microcrack risk value. Obtain the standard loss factor curve of the waterborne damping material to be evaluated under standard film-forming conditions, and perform attenuation correction on the standard loss factor curve according to the comprehensive index of film integrity to obtain the predicted loss factor curve. Based on the integral value of the loss factor curve in each of the key resonance frequency bands and the corresponding frequency band weights of each key resonance frequency band, a normalized predicted damping contribution value is generated.

[0196] Step S5, which involves generating the true damping contribution value based on the pre-coating standard vibration response data, the post-coating standard vibration response data, and the frequency band weight, specifically includes: Based on the validity marker, extract valid response pairing data from the standard response pairing data before and after coating; Based on the effective response pairing data, determine the decrease in resonance peak amplitude, the increase in half-power bandwidth, and the decrease in vibration energy within each of the key resonance frequency bands; Based on the frequency band weights corresponding to each of the key resonance frequency bands, the decrease in resonance peak amplitude, the increase in half-power bandwidth, and the decrease in vibration energy are weighted and fused to generate an initial true damping contribution value. The initial true damping contribution value is corrected based on the damping layer coverage and the damping layer thickness to generate a normalized true damping contribution value.

[0197] In this embodiment, step S5 generates predicted damping contribution values ​​and actual damping contribution values ​​from two technical paths: the prediction side and the measured side. This provides a comparable basis for the film formation contribution deviation analysis in the subsequent step S6. Specifically, the prediction side estimates the theoretical damping contribution that the waterborne damping material to be evaluated can exert under the current film formation state based on the latent film formation characteristics generated in step S2. The measured side determines the actual damping improvement generated by the waterborne damping material to be evaluated within the key resonant frequency band of the target substrate based on the standard vibration response data before and after coating obtained in step S4, and the frequency band weights obtained in step S3. It should be noted that although the predicted damping contribution values ​​and actual damping contribution values ​​originate from different sources, they are both generated around the same set of key resonant frequency bands and the same set of frequency band weights. Therefore, deviation comparisons can be performed within a unified frequency domain evaluation framework.

[0198] I. Generation of predicted damping contribution values.

[0199] When generating the predicted damping contribution value based on the latent state characteristics of film formation, a comprehensive index of film integrity is first generated based on the residual moisture risk value, the film thickness uniformity value, the surface thermal uniformity value, the local shrinkage risk value, and the microcrack risk value.

[0200] The residual moisture risk value Local contraction risk value and microcrack risk value This is a risk category value; the higher the value, the higher the corresponding defect risk. The film thickness uniformity value... and surface thermal uniformity value It belongs to the positive state value of integrity, and the larger the value, the more complete the corresponding state.

[0201] Before generating the comprehensive index of film integrity, the residual moisture risk value is... The risk value of local contraction and the microcrack risk value Converted to residual moisture integrity values ​​respectively Local contraction integrity value and microcrack integrity value This ensures that all state values ​​involved in the calculation of the film integrity comprehensive index satisfy the evaluation direction that a larger value indicates a more complete film formation state.

[0202] When the residual moisture risk value, film thickness uniformity value, surface thermal uniformity value, local shrinkage risk value, and microcrack risk value are all normalized to the range of 0 to 1, the evaluation direction of each state value is first unified. Among them, the residual moisture risk value, local shrinkage risk value, and microcrack risk value are risk-type state values, which are converted into residual moisture integrity value, local shrinkage integrity value, and microcrack integrity value, respectively; the film thickness uniformity value and surface thermal uniformity value are integrity-positive state values, and no direction conversion is performed.

[0203] The residual moisture integrity value, local shrinkage integrity value, and microcrack integrity value are determined according to the following formulas: ; ; ; In the formula, The residual moisture risk value is determined according to the calculation process described above in the instruction manual. This represents the risk value for localized contraction. This represents the risk value for microcracks. , and These are the residual moisture integrity value, the local shrinkage integrity value, and the microcrack integrity value, respectively. The higher the risk status value, the lower the corresponding integrity value.

[0204] Based on the unified state values, the initial film integrity comprehensive index is determined according to the following formula. : ; In the formula, This is a comprehensive index of initial film integrity. This represents the film thickness uniformity value. This represents the surface thermal uniformity value. to The weights for residual moisture integrity, film thickness uniformity, surface thermal uniformity, local shrinkage integrity, and microcrack integrity are respectively defined, and the following conditions must be met: ; In this embodiment, each state value uses the same weight, and the weights are as follows: ; Therefore, the initial film integrity comprehensive index Determined according to the following formula: ; To prevent severe single defects from being offset by other normal state values, the initial film integrity comprehensive index is further adjusted based on the number of severe defects. Perform constraint corrections.

[0205] The corresponding state is identified as a critical defect when any of the following conditions are met: The residual moisture risk value Greater than ; The risk value of localized contraction Greater than ; The microcrack risk value Greater than ; The film thickness uniformity value Less than ; The surface thermal uniformity value Less than .

[0206] The number of critical defects is obtained by counting the number of defects that meet the above conditions. The defect penalty coefficient is determined according to the following formula. : ; Based on the initial film integrity comprehensive index and the defect penalty coefficient The comprehensive index of film integrity is determined according to the following formula. : ; in, This is the comprehensive film integrity index after correction for severe defects. When there are no severe defects, the comprehensive film integrity index is equal to the initial comprehensive film integrity index; when a severe defect exists, the initial comprehensive film integrity index is lowered. When two or more serious defects exist, the overall index of initial film integrity is lowered. .

[0207] According to the comprehensive index of film integrity Determine the film integrity level: when At that time, it was determined that the film integrity was relatively high; when At that time, the film integrity was determined to be moderate; when At that time, it was determined that the integrity of the film formation was low; when At that time, it was determined that the integrity of the film formation was poor.

[0208] Through the above treatments, the higher the residual moisture risk value, local shrinkage risk value, and microcrack risk value, the lower the overall film integrity index; the higher the film thickness uniformity value and surface thermal uniformity value, the higher the overall film integrity index. For waterborne damping materials to be evaluated with severe single defects, defect penalty treatment is used to reduce the degree to which severe defects are offset by other normal state values.

[0209] Obtain the standard loss factor curve of the waterborne damping material to be evaluated under standard film-forming conditions. The standard loss factor curve represents the damping loss capacity of the same type of waterborne damping material under standard film-forming conditions as a function of frequency. Standard film-forming conditions refer to film formation under preset standard film-forming conditions, with low residual moisture risk, high film thickness uniformity, relatively uniform surface thermal state, no significant local shrinkage, and no significant microcracks. For example, samples with a film-forming temperature of 20°C to 30°C, film-forming humidity of 40% to 65%, film thickness difference not exceeding 10% of the average film thickness, local shrinkage area not exceeding 2% of the coated area, and no cracks longer than 5 mm are selected as standard film-forming samples. The standard loss factor curve is obtained through dynamic mechanical analysis tests, standard vibration beam tests, composite structure vibration tests, or material supplier calibration data, and corresponds to the target application frequency band. When using multiple standard film-forming samples to establish standard loss factor curves, first map the loss factor curves of each standard film-forming sample to the same frequency sampling point. For each sampling point at the same frequency, first calculate the initial arithmetic mean of the loss factor values ​​of all standard film-forming samples. Standard film-forming samples that deviate from the initial arithmetic mean by more than 20% are marked as abnormal samples and excluded. Then, calculate the arithmetic mean of the loss factor values ​​of the remaining standard film-forming samples at the sampling point at that frequency. The obtained arithmetic mean is determined as the standard loss factor value of the standard loss factor curve at the sampling point at that frequency.

[0210] If the number of remaining valid standard film-forming samples is less than 3 after excluding abnormal samples, the standard loss factor value corresponding to that sampling point will not be generated, and a new standard film-forming sample will be added. If the loss factor value of a standard film-forming sample at the same frequency point deviates from the average value of all standard film-forming samples by more than 20%, the sample will be marked as an abnormal sample and reviewed.

[0211] It should also be noted that the standard loss factor curve, serving as the benchmark curve for predicting the damping contribution value, should be established using samples from the same type or formulation system as the waterborne damping material being evaluated. Furthermore, the damping layer thickness, coverage, and curing conditions of the standard film-forming samples should be within the preset standard range. If there are significant differences in the loss factor curves between different batches of standard film-forming samples, separate standard loss factor curves can be established for each batch, or batch correction markers can be set on the standard loss factor curves. This process avoids the impact of differences in loss factors caused by different material batches or different construction thicknesses on the benchmark stability of the predicted damping contribution value.

[0212] The standard loss factor curve should include the frequency range covered by the key resonant frequency bands determined in step S3. If the frequency sampling points of the standard loss factor curve are inconsistent with the frequency sampling points within the key resonant frequency bands, interpolation, resampling, or local smoothing should be used to ensure that the standard loss factor curve provides loss factor values ​​for the corresponding frequency points within each key resonant frequency band. If a key resonant frequency band exceeds the measured frequency range of the standard loss factor curve, supplementary testing should be performed on that frequency band, or the frequency band should be marked as a band with insufficient prediction data.

[0213] The standard loss factor curve is attenuated and corrected based on the comprehensive index of film integrity to obtain the predicted loss factor curve.

[0214] Let the comprehensive index of film integrity be . The standard loss factor curve at frequency The corresponding standard loss factor is The frequency is determined according to the following formula. Predicted loss factor at the location : ; In the formula, Located within the range of 0 to 1. The higher the comprehensive index of film integrity, the closer the predicted loss factor curve is to the standard loss factor curve; the lower the comprehensive index of film integrity, the greater the attenuation of the predicted loss factor curve relative to the standard loss factor curve. For the first... There are several key resonant frequency bands, with their lower limit frequency set as follows: The upper limit frequency is The critical resonance bandwidth is determined according to the following formula. : ; Let the first The key resonant frequency band contains The frequency sampling point, the th Each frequency sampling point is The corresponding frequency interval is The integral value of the prediction loss factor is determined according to the following formula. : ; Based on the integral value of the predicted loss factor and the critical resonance bandwidth, the average predicted loss factor value is determined according to the following formula. : ; The average predicted loss factor value is used to... The cumulative predicted loss capability within each key resonant frequency band is converted into the average loss capability per unit bandwidth, eliminating the impact of different key resonant frequency band widths on inter-band comparisons. Following the same method, the loss factor curve is used to determine the... Standard loss factor integral value of key resonant frequency bands : ; The average standard loss factor value is determined according to the following formula. : ; The maximum value is selected from the average standard loss factor values ​​corresponding to all key resonant frequency bands, and used as the standard normalization benchmark. : ; According to the The normalized predicted loss contribution value is determined by the average predicted loss factor value corresponding to each key resonant frequency band and the standard normalized benchmark, according to the following formula. : ; The normalized predicted loss contribution value is used to characterize the first... The ratio of the average predicted loss capability of each key resonant band to the average loss capability of the strongest key resonant band under standard film deposition conditions.

[0215] Let the number of key resonance bands be... , No. The frequency band weights corresponding to the key resonant frequency bands are: And satisfy: ; ; The predicted damping contribution value is determined according to the following formula. : ; The predicted damping contribution value is within the range of 0 to 1. By normalizing the average predicted loss factor value of each key resonance frequency band using a unified standard normalization benchmark, the differences in standard loss levels and frequency band weights of different key resonance frequency bands can be retained in the final predicted damping contribution value.

[0216] When the width of any key resonance frequency band is not greater than 0, there are no effective frequency sampling points in the band, or the standard normalization reference is not greater than 0, an invalid prediction contribution data marker is generated, and the key resonance frequency band is re-determined, the standard loss factor curve data is supplemented, or the data is resampled.

[0217] II. Generation of the actual damping contribution value.

[0218] When generating the true damping contribution value based on the pre-coating standard vibration response data, the post-coating standard vibration response data, and the frequency band weights, only paired data marked as valid data are extracted from the pre- and post-coating standard response paired data to form valid response paired data, based on the validity markers. Paired data marked as pending verification or invalid data are not included in the calculation of the final true damping contribution value.

[0219] When the proportion of effective paired data in any key resonance band is less than 80% of the total paired data in that key resonance band, the key resonance band is marked as a data-insufficient band, the final true damping contribution value for the corresponding sample to be evaluated is not generated, and a retest prompt is output. When the proportion of effective paired data is not less than 80%, the decrease in resonance peak amplitude, the increase in half-power bandwidth, and the decrease in vibration energy are calculated.

[0220] Based on the effective response pairing data, the decrease in resonance peak amplitude, the increase in half-power bandwidth, and the decrease in vibration energy within each key resonance frequency band were determined. The decrease in resonance peak amplitude characterizes the degree of reduction in the response amplitude of the target substrate at the key resonance peak after coating; the increase in half-power bandwidth characterizes the degree of broadening of the resonance peak after the increase in damping of the composite structure after coating; and the decrease in vibration energy characterizes the degree of reduction in the overall vibration response energy within the key resonance frequency band. These three metrics represent the actual damping contribution from the perspectives of peak suppression, damping enhancement, and overall frequency band energy attenuation, respectively.

[0221] No. The decrease in resonance peak amplitude within each key resonance frequency band is: ; In the formula, Indicates the first The decrease in resonance peak amplitude within a key resonance frequency band; and These represent the standard resonance peak amplitudes before and after coating within the same frequency band, respectively. The larger the value, the more significant the decrease in resonance peak amplitude after coating. If Greater than ,Will Record it as 0 and mark it as a negative improvement in bandwidth.

[0222] No. The increase in half-power bandwidth within each key resonant frequency band is: ; In the formula, Indicates the first Increase in half-power bandwidth within a key resonant frequency band; and These represent the standard half-power bandwidth before and after coating within the frequency band, respectively. The larger the value, the more significant the increase in structural damping after coating. If Not greater than ,Will Record it as 0 and mark it as a bandwidth-unimproved band.

[0223] No. The decrease in vibrational energy within the key resonant frequency bands is: ; In the formula, Indicates the first The decrease in vibrational energy within a key resonant frequency band; and These represent the standard vibration energy before and after coating within the frequency band, respectively, determined by referring to the relative vibration energy calculation method in step S3. The larger the value, the more significant the decrease in overall vibrational energy within that frequency band after coating. If Greater than ,Will Record it as 0 and mark it as the frequency band where energy is not improved.

[0224] No. The actual band damping contribution value within each key resonant frequency band is: ; In the formula, Indicates the first The actual band damping contribution value within each key resonant frequency band; , and These represent the fusion weights of the three indicators, all greater than 0 and summing to 1. This improves performance for applications primarily focused on suppressing resonance peaks. The main focus is on improving damping in scenarios requiring enhanced damping. The main focus is on improving scenarios that reduce overall vibration across the frequency band. When there is no special emphasis, all three are set to the same weight.

[0225] The initial true damping contribution value is: ; In the formula, This represents the initial true damping contribution value; Indicates the first The frequency band weights corresponding to the key resonant frequency bands; This indicates the number of key resonant frequency bands. Key resonant frequency bands with a higher proportion of vibration energy have a greater impact on the initial true damping contribution value, making the true damping contribution value reflect the actual vibration reduction requirements of the target substrate.

[0226] When generating the initial true damping contribution value, boundary control is applied to the abnormal improvement value. When the decrease in resonance peak amplitude, the increase in half-power bandwidth, or the decrease in vibration energy within a certain frequency band exceeds 1, it is limited to within 1. When it is negative, it is recorded as 0 and the negative improvement mark is retained.

[0227] The initial true damping contribution value is corrected based on the damping layer coverage and damping layer thickness to generate a normalized true damping contribution value.

[0228] Assume the damping layer coverage of the sample to be evaluated is... The average dry film thickness of the damping layer is The standard coverage rate corresponding to the standard coating structure is The standard average dry film thickness is .

[0229] The structural correction factor is determined according to the damping layer coverage and the average dry film thickness of the damping layer using the following formula. : ; The product of the damping layer coverage and the average dry film thickness is used to characterize the equivalent damping material volume per unit preset coating area. The standard coverage and standard average dry film thickness are the corresponding values ​​of the standard film-forming samples used when establishing the standard loss factor curve.

[0230] When the equivalent damping material volume of the sample to be evaluated is not greater than the equivalent damping material volume corresponding to the standard coated structure, the structure correction factor is set to 1, so as not to amplify the true damping contribution value of the sample with insufficient coverage or thin thickness through structure correction; when the equivalent damping material volume of the sample to be evaluated is greater than the equivalent damping material volume corresponding to the standard coated structure, the structure correction factor is greater than 1, which is used to reduce the amplification effect of the increase in coating amount on the initial true damping contribution value.

[0231] Let the initial true damping contribution value be... The actual damping contribution value is determined according to the following formula. : ; When the damping layer coverage is less than 0.85, an insufficient coverage marker is generated; when the average dry film thickness of the damping layer is less than 0.80 times the standard average dry film thickness, a thinner marker is generated; when the average dry film thickness of the damping layer exceeds 1.20 times the standard average dry film thickness, a thicker marker is generated. These markers are used for subsequent anomaly cause determination and do not change the boundary rule that the structural correction factor is not less than 1.

[0232] When the damping layer coverage is 0, the average dry film thickness of the damping layer is 0, the standard coverage is not greater than 0, or the standard average dry film thickness is not greater than 0, no real damping contribution value is generated, and an invalid damping layer structure data marker is output.

[0233] Although the predicted and actual damping contribution values ​​originate from different sources, they are both generated around the key resonant frequency band and band weights determined in step S3. The predicted damping contribution value is obtained by correcting the standard loss factor curve based on the latent state characteristics of the film formation, while the actual damping contribution value is obtained based on the actual improvement of the standard vibration response data before and after coating. Both are formed within the same band weight framework, allowing for deviation comparison in subsequent steps.

[0234] The beneficial effect of this step lies in establishing a dual-channel evaluation mechanism: "damping contribution prediction based on film formation state" and "damping contribution measurement based on standard response pairing data." Compared to judging the damping effect solely based on post-coating vibration response data, this step can determine whether insufficient damping is due to a poor latent film formation state or a situation where the predicted film formation state is good but the actual response improvement is insufficient. For example, when the overall film integrity index is low, the predicted damping contribution value is low, and the actual damping contribution value is also low, insufficient damping contribution is strongly correlated with film quality defects. When the overall film integrity index is high, the predicted damping contribution value is high, but the actual damping contribution value is significantly low, there may be abnormal interface bonding, insufficient local coverage, abnormal test data, or insufficient frequency band adaptation of the target substrate. Therefore, this step not only generates contribution values ​​but also provides an interpretable data basis for subsequent judgment of film formation contribution deviation results and abnormal cause types.

[0235] S6: Generate a film formation contribution deviation result based on the deviation between the predicted damping contribution value and the actual damping contribution value, and output the intelligent state evaluation result of the waterborne damping material to be evaluated based on the film formation latent state characteristics, the actual damping contribution value and the film formation contribution deviation result.

[0236] Step S6, which involves generating a film formation contribution deviation result based on the deviation between the predicted damping contribution value and the actual damping contribution value, specifically includes: The contribution deviation value is calculated based on the predicted damping contribution value and the actual damping contribution value; The contribution deviation value is normalized based on the predicted damping contribution value to generate the contribution deviation rate; Based on the relationship between the actual damping contribution value and the predicted damping contribution value, a contribution deviation direction marker is generated; A film formation contribution deviation level is generated based on the contribution deviation rate and a preset deviation level threshold. The film formation contribution deviation result is generated based on the contribution deviation value, the contribution deviation rate, the contribution deviation direction marker, and the film formation contribution deviation level.

[0237] Step S6, which involves outputting the intelligent state assessment result of the waterborne damping material to be evaluated based on the film-forming latent state characteristics, the true damping contribution value, and the film-forming contribution deviation result, specifically includes: The film formation state level is determined based on the residual moisture risk value, film thickness uniformity value, surface thermal uniformity value, local shrinkage risk value, and microcrack risk value in the film formation latent state characteristics. The actual damping contribution level is determined based on the comparison between the actual damping contribution value and the preset contribution level threshold. The type of abnormal cause is determined based on the matching relationship between the film formation state level, the actual damping contribution level, the film formation contribution deviation level, and the contribution deviation direction mark. The intelligent state assessment result is generated based on the film formation state level, the actual damping contribution level, the film formation contribution deviation level, the contribution deviation direction marker, and the anomaly cause type.

[0238] In this embodiment, step S6 generates a film formation contribution deviation result based on the difference between the predicted damping contribution value and the actual damping contribution value obtained in step S5. It further combines the latent characteristics of the film formation state and the actual damping contribution value to output the intelligent state assessment result of the waterborne damping material to be evaluated. This step does not merely output a single pass / fail judgment, but further determines whether the film formation state, predicted damping contribution, and actual damping contribution match, and identifies possible anomaly causes based on the matching relationship, making the assessment result interpretable.

[0239] I. Generation of film-forming contribution deviation results.

[0240] When generating the film formation contribution deviation result based on the deviation between the predicted damping contribution value and the actual damping contribution value, the contribution deviation value is first calculated based on the predicted damping contribution value and the actual damping contribution value. The predicted damping contribution value is denoted as... The actual damping contribution value is denoted as Both are contribution values ​​that are normalized to the range of 0 to 1 in step S5.

[0241] Let the predicted damping contribution value be... The actual damping contribution value is The contribution deviation value is determined according to the following formula. : ; Let the lower bound of contribution bias normalization be... In this embodiment: ; The contribution deviation rate is determined according to the following formula. : ; when At the same time, low prediction contribution sample labels are generated.

[0242] when and When, generate true direction markers that are lower than the predicted direction; when and When, the actual direction marker is higher than the predicted direction marker; when and The absolute difference between them does not exceed At that time, a direction marker consistent with the predicted and measured directions is generated.

[0243] The film formation contribution deviation level is determined based on the aforementioned contribution deviation rate: when At that time, it was determined to be a normal fluctuation; when When this is the case, it is determined to be a general deviation; when At that time, it was determined to be a significant deviation; when At that time, it was determined to be a serious deviation.

[0244] It should also be noted that the direction of contribution deviation has attributional significance. When the direction of contribution deviation is marked as "actually lower than predicted," it indicates that the damping contribution inferred from the latent state characteristics of the film formation is not fully reflected in the measured response of the target substrate. This may be due to insufficient interfacial bonding, mismatch between the coverage area and the key vibration area, local latent defects, insufficient damping layer thickness, or insufficient reliability of the test data. When the direction of contribution deviation is marked as "actually higher than predicted," it indicates that the measured damping improvement is better than the predicted results of the latent state of the film formation. This may be due to a conservative assessment of the latent state of the film formation, the damping layer position exactly covering the key vibration area, the actual thickness being too large, or the target substrate being more sensitive to the damping material in that frequency band. When the direction of contribution deviation is marked as "predicted and measured are consistent," it indicates that the latent state characteristics of the film formation have a good explanatory power for the actual damping contribution.

[0245] Furthermore, a film-forming contribution deviation level is generated based on the contribution deviation rate and a preset deviation level threshold. The preset deviation level threshold can be determined based on calibration samples of the same type of waterborne damping material, historical evaluation samples, or engineering acceptance requirements. For example, when the contribution deviation rate does not exceed 10%, the film-forming contribution deviation level can be determined as normal fluctuation; when the contribution deviation rate is greater than 10% but not more than 25%, it can be determined as a general deviation; when the contribution deviation rate is greater than 25% but not more than 50%, it can be determined as a significant deviation; and when the contribution deviation rate exceeds 50%, it can be determined as a severe deviation.

[0246] It should also be noted that if the predicted damping contribution value is below 0.05 and is marked as a low-prediction contribution sample, the contribution deviation value can be used in conjunction with the prediction to avoid distortion of the contribution deviation rate level solely due to the low predicted damping contribution value. For example, in low-prediction contribution samples, a contribution deviation value below 0.05 can still be considered normal fluctuation; a contribution deviation value greater than 0.05 but not exceeding 0.20 can be considered a general deviation; and a contribution deviation value exceeding 0.20 can be considered a significant or serious deviation.

[0247] Furthermore, a film formation contribution deviation result is generated based on the contribution deviation value, contribution deviation rate, contribution deviation direction marker, and film formation contribution deviation level. The film formation contribution deviation result includes at least the contribution deviation value, contribution deviation rate, contribution deviation direction marker, and film formation contribution deviation level, and may also include markers for low predicted contribution samples, negative improvement bands, unimproved energy bands, insufficient coverage, excessive thickness, insufficient thickness, and data reliability. Through the combination of the above information, the film formation contribution deviation result can not only represent the magnitude of the deviation between the predicted damping contribution value and the actual damping contribution value, but also indicate whether the actual damping contribution value is higher or lower than the predicted damping contribution value, and further indicate whether the deviation is sufficient to affect subsequent state judgments.

[0248] It should also be noted that the film formation contribution deviation result is stored together with the validity marker, negative improvement band marker, energy non-improvement band marker, insufficient coverage marker, thinner thickness marker, and thicker thickness marker generated in steps S4 and S5. For example, when the contribution deviation direction marker is "actually lower than predicted," and both the energy non-improvement band marker and the insufficient coverage marker are present, the subsequent anomaly cause type can preferentially point to insufficient coverage area or the coating location not covering the key vibration area; when the contribution deviation direction marker is "actually lower than predicted," but the standard response confidence is low, the subsequent anomaly cause type can preferentially point to insufficient test data confidence. Thus, the film formation contribution deviation result not only serves as a numerical result output but also as an intermediate basis for anomaly attribution.

[0249] II. Output of intelligent state assessment results.

[0250] When determining the film formation state level based on the aforementioned latent film formation characteristics, the comprehensive film formation integrity index after severe defect constraint correction in step S5 is used. This serves as the sole criterion for judgment. Since the comprehensive index of film integrity has already been constrained and corrected based on the number of severe defects and the defect penalty coefficient, this step will not repeat the film state level downgrade process for the same severe defect.

[0251] Dangman At that time, the film formation status level was determined to be good; When satisfied At that time, the film formation state level was determined to be general; When satisfied At that time, the film-forming state level is determined as the deviation; When satisfied At that time, the film formation status level is determined to be poor.

[0252] By adopting a comprehensive index of film integrity that has been corrected for severe defect constraints, the film state level can simultaneously reflect the comprehensive results of five film state dimensions as well as the impact of severe single defects, and ensure that the latent state characteristics of the same component film uniquely correspond to one film state level.

[0253] Furthermore, the actual damping contribution level is determined based on the comparison between the actual damping contribution value and the preset contribution level threshold. The preset contribution level threshold can be determined based on the calibrated contribution value of the same type of water-based damping material under standard film-forming conditions, engineering acceptance requirements, or statistical results of historical qualified samples. For example, when the actual damping contribution value... When the value is not lower than 0.80, the true damping contribution level can be determined to be high contribution; when A value greater than 0.60 and less than 0.80 can be identified as a medium-to-high contribution; when... A value greater than 0.40 and not exceeding 0.60 can be identified as a low to medium contribution; when A value not exceeding 0.40 can be considered a low contribution. For critical vibration reduction applications, the acceptable threshold can be raised, for example, requiring the actual damping contribution value to be no less than 0.70 to be considered as meeting the target vibration reduction requirements.

[0254] It should also be noted that the true damping contribution level is used to reflect the actual vibration reduction effect of the water-based damping material within the key resonant frequency band of the target substrate. The judgment is not based on the average response change across the entire frequency band, but rather on the key resonant frequency band determined in step S3, the effective response pairing data obtained in step S4, and the normalized true damping contribution value generated in step S5. Therefore, the true damping contribution level can more directly reflect the ability of the water-based damping material to suppress the actual vibration-sensitive areas of the target substrate.

[0255] Furthermore, the type of anomaly is determined based on the matching relationship between the film formation state level, the true damping contribution level, the film formation contribution deviation level, and the contribution deviation direction marker. This matching relationship can be established using rule tables, statistical results of calibration samples, or historical evaluation data. For ease of understanding, the following matching method can be used.

[0256] When the film formation status is good or average, the actual damping contribution is high or medium-high, and the film formation contribution deviation is normal fluctuation or average deviation, the abnormality cause type can be determined as no obvious abnormality, indicating that the film formation status and the actual damping contribution are basically matched.

[0257] When the film formation status level is deviation or poor, the actual damping contribution level is medium-high contribution, medium-low contribution, or low contribution, and the contribution deviation direction is marked as consistent with the prediction or lower than the prediction, if at least one of the residual moisture risk value, film thickness uniformity value, surface thermal uniformity value, local shrinkage risk value, or microcrack risk value reaches the corresponding abnormal condition, then the abnormal cause type is determined to be film formation quality defect dominant.

[0258] The corresponding abnormal conditions include: residual moisture risk value not less than 0.50, film thickness uniformity value less than 0.60, surface thermal uniformity value less than 0.60, local shrinkage risk value not less than 0.50, or microcrack risk value not less than 0.50. When multiple abnormal conditions are met, the main film-forming defects are determined according to the degree to which each state value deviates from the corresponding normal range, and other states that meet the abnormal conditions are determined as accompanying film-forming defects.

[0259] When the actual damping contribution level is medium to high, the film quality defect indicates that a clear abnormality in the film formation state has been detected, but the abnormality has not yet reduced the overall actual damping contribution to medium to low or low. When the actual damping contribution level is medium to low or low, the film quality defect indicates that the abnormality in the film formation state has had a significant impact on the actual damping contribution.

[0260] When the residual moisture risk value is not less than 0.80, the main film-forming defect is identified as residual moisture or insufficient curing; when the film thickness uniformity value is less than 0.30, the main film-forming defect is identified as uneven film thickness; when the surface thermal uniformity value is less than 0.30, the main film-forming defect is identified as local uneven curing; when the local shrinkage risk value is not less than 0.80, the main film-forming defect is identified as local shrinkage; when the microcrack risk value is not less than 0.80, the main film-forming defect is identified as microcracks. When two or more state values ​​simultaneously meet the above conditions for main film-forming defects, the corresponding multiple defect types are jointly identified as the main film-forming defect.

[0261] When the film formation status level is good or average, the actual damping contribution level is low to medium or low, the film formation contribution deviation level is significant or severe, and the contribution deviation direction is marked as actual lower than predicted, the anomaly cause type can be determined as insufficient actual performance. This type indicates that the damping contribution predicted by the latent state of film formation is high, but the measured response improvement is insufficient. Furthermore, when both the insufficient coverage mark and the thin thickness mark are present, the anomaly cause can be further subdivided into insufficient coating structure; when neither the insufficient coverage nor the thin thickness mark exists, but a negative improvement band mark or an energy-unimproved band mark exists, the anomaly cause can be further subdivided into insufficient target substrate bandwidth adaptation or abnormal interface bonding; when the standard response confidence level is low confidence or invalid, the insufficient actual performance judgment is not performed, the final actual damping contribution level and film formation contribution deviation level are not generated, and instead, the test data confidence level and retest prompt are directly output.

[0262] It should also be noted that when determining actual underperformance, a further distinction can be made between overall underperformance and local underperformance. If multiple key resonant frequency bands exhibit low reductions in resonance peak amplitude, no increase in half-power bandwidth, or no decrease in vibrational energy, then it can be judged as overall underperformance. If only a few key resonant frequency bands show negative improvement band markers, no bandwidth improvement band markers, or no energy improvement band markers, while other key resonant frequency bands have high actual damping contributions, then it can be judged as local underperformance. This distinction can prevent local frequency band damping underperformance from being misjudged as overall material damping capacity underperformance.

[0263] When the overall true damping contribution level is high or medium-high, but the true damping contribution value of a certain key resonant frequency band is significantly lower than that of other key resonant frequency bands, or when the key resonant frequency band has a negative improvement band marker, a bandwidth-unimproved band marker, or an energy-unimproved band marker, the anomaly can be identified as insufficient local band damping. "Significantly lower" means that the true damping contribution value of the key resonant frequency band is more than 30% lower than the average value of all key resonant frequency bands, or more than 40% lower than the true damping contribution value of adjacent key resonant frequency bands. This type indicates that although the overall true damping contribution value meets the requirements, there is still a problem of insufficient suppression in local key resonant frequency bands. Further investigation should be conducted on the damping layer coverage, thickness distribution, interface bonding state, and material loss frequency band compatibility in the corresponding region of that frequency band.

[0264] When the film formation state is classified as poor or undesirable, but the actual damping contribution is high or medium-high, and the direction of the contribution deviation is marked as actual higher than predicted, the anomaly can be identified as either a conservative prediction or a high structural compensation contribution. This type indicates that the latent film formation characteristics show certain defects, but the actual damping response is well improved. Furthermore, when there is a thicker marking, it can be determined that the structural compensation contribution from the increased thickness is high; when there is no thicker marking and the actual damping contribution value is still high, it can be determined that the key resonant frequency band of the target substrate matches well with the material loss frequency band, or the latent film formation state assessment is conservative in estimating the impact of defects.

[0265] When the film formation status is rated as good or average, and the actual damping contribution is rated as high or medium-high, but the contribution deviation direction is marked as actual higher than predicted and the film formation contribution deviation is rated as significant or severe, the anomaly can be identified as an unexpected measured contribution. This type does not indicate non-compliance, but rather suggests that the actual damping improvement of the sample exceeds the predicted result. At this point, further checks can be made to determine whether the damping layer thickness, coverage area, key resonant frequency band weights, and standard loss factor curve need updating to avoid overly conservative prediction models for the same type of waterborne damping material in the future.

[0266] When the film formation status level, true damping contribution level, and film formation contribution deviation level do not satisfy any of the above-mentioned explicit matching relationships, or when multiple anomaly markers appear simultaneously and the dominant cause cannot be determined, the anomaly cause type can be identified as a composite anomaly. This type can further output a "to be reviewed" prompt and list the anomaly factors with the greatest contribution, such as high residual moisture risk, insufficient coverage, low standard response confidence, a large number of negative improvement bands, or abnormally low true damping contribution values ​​in local bands. By outputting composite anomalies, it is possible to avoid forcibly assigning a single cause when evidence is insufficient.

[0267] Furthermore, an intelligent state assessment result is generated based on the film formation state level, actual damping contribution level, film formation contribution deviation level, contribution deviation direction marker, and anomaly cause type. The intelligent state assessment result may include the target substrate number, coating area number, film formation state level, actual damping contribution level, predicted damping contribution value, actual damping contribution value, contribution deviation value, contribution deviation rate, contribution deviation direction marker, film formation contribution deviation level, anomaly cause type, key anomaly indicators, and retesting recommendations. The key anomaly indicators may include residual moisture risk value, film thickness uniformity value, surface thermal uniformity value, local shrinkage risk value, microcrack risk value, insufficient coverage marker, excessive thickness marker, insufficient thickness marker, standard response confidence level, number of negative improvement bands, and local band damping insufficient marker.

[0268] It should also be noted that the various indicators in the intelligent condition assessment results should be stored in association with the target substrate number, coating area number, and key resonant frequency band number. When there are multiple coating areas on the same target substrate, the intelligent condition assessment results corresponding to each coating area can be output separately; when the same coating area corresponds to multiple key resonant frequency bands, the local contribution evaluation results corresponding to each key resonant frequency band can be output separately. Through the above-mentioned association and storage method, the assessment results can not only reflect the overall material condition, but also be traced back to the specific coating area and the specific key resonant frequency band, which is convenient for subsequent process review and local touch-up treatment.

[0269] The intelligent status assessment results can be output in different levels according to application requirements. For example, when the film formation status level is good, the actual damping contribution level is high or medium-high, and the film formation contribution deviation level is within normal fluctuation, a qualified status can be output; when the film formation status level is average, the actual damping contribution level is medium-high or medium-low, and the film formation contribution deviation level does not exceed the general deviation, an acceptable status can be output; when the actual damping contribution level is low, or the film formation contribution deviation level is significant and the contribution deviation direction is marked as actual lower than predicted, a status requiring review can be output; when the film formation status level is poor, the actual damping contribution level is low, and the film formation contribution deviation level is severe deviation, an unqualified status can be output. The above status outputs are used for on-site quality judgment, process review, or subsequent data traceability.

[0270] Furthermore, the intelligent condition assessment results can also include handling suggestions based on the type of anomaly. When the anomaly is primarily caused by film quality defects, suggestions can be provided such as extending the curing time, adjusting the film formation temperature and humidity, improving coating uniformity, or recoating. When the anomaly is caused by insufficient coating structure, suggestions can be provided such as increasing the coverage area, recoating key vibration areas, or adjusting the damping layer thickness. When the anomaly is caused by abnormal interface bonding, suggestions can be provided such as checking the substrate surface cleanliness, interface adhesion status, or primer treatment. When the anomaly is caused by insufficient reliability of test data, suggestions can be provided such as reinstalling the sensor, recalibrating the excitation position, or re-performing standard operating condition tests. When the anomaly is caused by insufficient damping in local frequency bands, suggestions can be provided such as reviewing the damping layer coverage position, thickness distribution, and material loss frequency band compatibility for the corresponding key resonant frequency band. These handling suggestions can be automatically generated based on the type of anomaly and can also be used as supplementary information in the assessment report.

[0271] It should also be noted that the beneficial effect of this step lies in its transformation of the difference between the predicted damping contribution value and the actual damping contribution value into an interpretable film-forming contribution deviation result, and further combining the latent state of film formation and the measured damping contribution to output an intelligent state assessment result. Compared with simply outputting a single damping effect value, this step can distinguish different types of anomaly sources. For example, when the film formation state is poor and the actual damping contribution is low, it can be determined that the insufficient damping mainly originates from film formation quality defects; when the film formation state is good but the actual damping contribution is low, it can further point to interface bonding, coverage area, thickness, or test reliability issues; when the overall actual damping contribution is high but the contribution of local key resonant frequency bands is insufficient, it can indicate insufficient damping in local frequency bands; when the film formation state is average but the actual damping contribution is high, it can indicate that the actual structure compensation or the prediction model is too conservative. Thus, this step elevates the evaluation result of waterborne damping materials from a simple numerical judgment to an intelligent state assessment result with causal explanations and verification basis.

[0272] It should also be noted that the segment nodes, thresholds, and weights used in this embodiment for residual moisture risk, film thickness uniformity, surface thermal uniformity, local shrinkage risk, microcrack risk, cross-validation, film integrity evaluation, and contribution deviation evaluation are all determined based on the calibration samples of the same type of water-based damping material.

[0273] Specifically, standard film-forming samples and calibration samples with residual moisture, uneven film thickness, abnormal surface thermal state, local shrinkage, and microcrack defects were collected. Quality time-series data, film thickness distribution data, infrared thermogram data, surface image data, and vibration response data before and after coating were obtained for each calibration sample. For each candidate threshold, the consistency between the state results generated according to the candidate threshold and the results of manual verification and vibration testing was compared. The threshold that could reduce misidentification and maintain stable state levels was determined as the corresponding preset threshold.

[0274] After calibration, aqueous damping materials of the same type or formulation system are evaluated using fixed thresholds, weights, and standard curves, without temporarily changing the thresholds or weights based on individual samples. Recalibration is performed when the material type, formulation system, standard film thickness, or image acquisition system changes.

[0275] Example 2 is the second embodiment of the present invention, which differs from the previous embodiment in that: If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art or the current technical solution, can be embodied in the form of a software product. This current computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0276] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0277] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0278] Example 3, referring to Figure 2 In one embodiment of the present invention, an intelligent state assessment system for waterborne damping materials is provided, which includes a data acquisition module, a film-forming latent state generation module, a frequency band weight generation module, a standard operating condition normalization module, a damping contribution value generation module, and an intelligent state assessment module.

[0279] Data acquisition module: acquires film formation process data and film formation environment parameters of the water-based damping material to be evaluated on the target substrate, and acquires vibration response data of the target substrate before coating, vibration response data after coating, and vibration response test environment parameters. Film-forming latent state generation module: Generates film-forming latent state characteristics based on the film-forming process data and the film-forming environment parameters; Frequency band weight generation module: Determines the key resonant frequency band of the target substrate based on the vibration response data before coating, and generates frequency band weights based on the vibration energy distribution within the key resonant frequency band; Standard operating condition normalization module: Based on the film formation environment parameters and the vibration response test environment parameters, the vibration response data before coating and the vibration response data after coating are normalized according to standard operating conditions to obtain standard vibration response data before coating and standard vibration response data after coating. Damping contribution value generation module: Generates predicted damping contribution value based on the film-forming latent state characteristics, and generates actual damping contribution value based on the standard vibration response data before coating, the standard vibration response data after coating, and the frequency band weight; Intelligent state assessment module: Generates film formation contribution deviation result based on the deviation between the predicted damping contribution value and the actual damping contribution value, and outputs the intelligent state assessment result of the waterborne damping material to be evaluated based on the film formation latent state characteristics, the actual damping contribution value and the film formation contribution deviation result.

[0280] Example 4 is an embodiment of the present invention, which provides a smart state assessment method for waterborne damping materials. The specific process of the present invention for comprehensively assessing the film-forming state, damping contribution and data reliability is illustrated through comparative tests under different film-forming conditions.

[0281] In this embodiment, a certain type of waterborne acrylic damping coating, designated WD-208, was selected as the waterborne damping material to be evaluated, and a cold-rolled steel plate with a length of 400 mm, a width of 300 mm, and a thickness of 1.5 mm was used as the target substrate. Six groups of samples were set up for the experiment, designated A1, A2, B1, B2, C1, and C2, respectively.

[0282] Among them, A1 and A2 are cured under standard film-forming conditions, with the film-forming temperature controlled at around 24 degrees Celsius and the film-forming humidity controlled at around 52%RH; B1 and B2 are cured under high temperature and low humidity conditions, with the film-forming temperature controlled at around 37 degrees Celsius and the film-forming humidity controlled within the range of 33%RH to 34%RH; C1 and C2 are cured under low temperature and high humidity conditions, with the film-forming temperature controlled within the range of 11 degrees Celsius to 12 degrees Celsius and the film-forming humidity controlled within the range of 81%RH to 84%RH.

[0283] The preset coating area for each sample was 300 square centimeters, and the preset wet film thickness was 2.0 millimeters. Before the test, the surface of the cold-rolled steel sheet was degreased, polished, and cleaned with ethanol. Positioning marks were set on the outside of the coating area to ensure that the sensor installation position, excitation position, and target substrate constraint state were consistent for vibration tests before and after coating for each sample.

[0284] In practice, a pre-coating vibration response test is first performed on the target substrate that is not coated with water-based damping material. The vibration test uses a swept-frequency excitation method with a sweep range of 20 Hz to 2000 Hz and a frequency sampling interval of 2 Hz. The vibration acceleration response amplitude corresponding to each frequency sampling point is recorded, and the resonance peak frequency, resonance peak amplitude, and half-power bandwidth are determined based on the frequency response curve. This further determines the key resonance frequency bands of the target substrate and the corresponding frequency band weights of each key resonance frequency band.

[0285] Subsequently, each sample was coated with an aqueous damping material. Immediately after coating, the total mass of the target substrate and the aqueous damping material was weighed, and mass data was collected every 10 minutes from the moment coating was completed. When the difference between the maximum and minimum mass remaining percentages within 30 consecutive minutes does not exceed 0.005, and the mass remaining percentages after that sampling time continue to meet the aforementioned condition, the corresponding sampling time is determined as the mass stabilization time.

[0286] During the film formation process, film formation temperature, film formation humidity, surface image, infrared thermogram, and surface temperature were recorded simultaneously. Film thickness distribution was acquired using a laser displacement sensor at 49 measurement points (7 rows by 7 columns). Surface image was obtained using an industrial camera under fixed illumination, fixed shooting distance, and fixed shooting angle. Infrared thermogram was acquired using an infrared thermal imager at a distance of 0.6 meters from the target substrate surface.

[0287] After the damping layer reaches the preset curing state, the vibration response test after coating is carried out under the same clamping method, sensor position, excitation position, sweep frequency range and frequency sampling interval as the vibration test before coating, and the test temperature and test humidity are recorded simultaneously.

[0288] According to the aforementioned method of the present invention, residual moisture risk value, film thickness uniformity value, surface thermal uniformity value, local shrinkage risk value and microcrack risk value of each sample are generated respectively, and film-forming latent state characteristics are formed in a fixed order; further, key resonance frequency bands and frequency band weights are determined, standard operating condition normalization processing is performed, and standard response confidence level and validity label are determined based on environmental normalization residual value.

[0289] This embodiment also employs a traditional evaluation method to compare and evaluate the same batch of samples. This traditional evaluation method primarily bases its conclusions on the presence of visible cracks on the damping layer surface, whether film thickness fluctuations exceed empirical ranges, and whether the resonance peak amplitude decreases after coating. It does not generate latent film-forming characteristics, nor does it analyze the deviation between the predicted damping contribution value and the actual damping contribution value. The main film-forming process data for each sample are shown in Table 1.

[0290] Table 1: Film formation process data for each sample

[0291] It should be noted that the proportion of local shrinkage area in Table 1 is obtained based on the statistics of effective local shrinkage areas retained through multi-source cross-validation, and the total length of effective microcracks is obtained based on the statistics of effective microcrack areas retained through multi-source cross-validation. Shadows, dust-attached areas, and locally reflective areas identified as candidate areas for image interference are not included in the calculation of local shrinkage risk values ​​or microcrack risk values.

[0292] The vibration evaluation data and intelligent state assessment results for each sample are shown in Table 2.

[0293] Table 2: Vibration evaluation data and condition assessment results for each sample

[0294] It should be noted that the environmental normalized residuals of B1 and B2 are 18.6% and 19.3%, respectively. According to the aforementioned standard response confidence judgment rules, both belong to low confidence, and the corresponding standard response pairing data before and after coating are marked as data to be reviewed.

[0295] Therefore, this embodiment does not use the data to be verified in B1 and B2 to generate the final true damping contribution value, nor does it generate the final contribution deviation rate, contribution deviation direction or film formation contribution deviation level based on the data to be verified. Instead, it outputs a prompt that the test data is not reliable and that the vibration response test should be repeated.

[0296] The auxiliary calculated values ​​of the actual damping contribution of B1 and B2, already generated in the original test records, can only be used as the basis for data verification before retesting, and are not the final intelligent state assessment results of this embodiment. After the re-acquired vibration response data meets the validity conditions, the actual damping contribution values ​​and intelligent state assessment results will be regenerated according to steps S5 and S6.

[0297] Furthermore, for sample A1, the predicted damping contribution value is 0.82, the actual damping contribution value is 0.79, and the contribution deviation value is: ; Contribution bias rate: ; That is, 3.7%. Since the absolute difference between the predicted damping contribution value and the actual damping contribution value of A1 does not exceed 0.05, the direction of contribution deviation is determined to be consistent with the prediction and measurement; the contribution deviation rate does not exceed 10%, and the film formation contribution deviation level is determined to be normal fluctuation.

[0298] The film formation status of A1 is good, the actual damping contribution value of 0.79 corresponds to medium to high contribution, and the film formation contribution deviation level is normal fluctuation. Therefore, the abnormality cause type is determined to be no obvious abnormality, and the intelligent status assessment result is qualified.

[0299] The predicted damping contribution value of A2 is 0.80, the actual damping contribution value is 0.76, the contribution deviation value is 0.04, and the contribution deviation rate is 5.0%. The film formation status of A2 is good, the actual damping contribution level is medium-high contribution, and the film formation contribution deviation level is normal fluctuation. Therefore, the abnormality cause type is also determined to be no obvious abnormality, and the intelligent status assessment result is qualified.

[0300] The evaluation results of A1 and A2 are consistent with the results of manual review, indicating that the present invention will not generate obvious excessive alarms for standard film-forming samples.

[0301] For B1 and B2, the coefficients of variation for film thickness were 21.7% and 19.8%, respectively; the maximum temperature differences were 5.4°C and 6.1°C, respectively; the percentages of local shrinkage area were 5.8% and 6.5%, respectively; and the total effective microcrack lengths were 37 mm and 42 mm, respectively. Based on the above film formation process data, the film formation status of both B1 and B2 is poor.

[0302] However, since the environmental normalized residuals of B1 and B2 are both greater than 15% but not exceeding 25%, their standard response confidence level is low, and the vibration response data needs to be verified. Therefore, this invention will not directly output the conclusion of low true damping contribution based solely on low-confidence vibration response data, but will prioritize outputting a prompt for retesting due to insufficient confidence in the test data.

[0303] The results show that, in addition to identifying risks of uneven film thickness, abnormal thermal state, local shrinkage, and microcracks formed under high temperature and low humidity conditions, the present invention can also prevent low-confidence vibration response data from directly entering the final contribution deviation judgment through standard response confidence and validity marking, thereby reducing the risk of forming incorrect evaluation conclusions due to test errors.

[0304] For sample C1, the predicted damping contribution is 0.71, the actual damping contribution is 0.62, and the contribution deviation is: ; Contribution bias rate: ; That is, 12.7%. Since the actual damping contribution value is lower than the predicted damping contribution value, and the absolute difference between the two exceeds 0.05, the contribution deviation direction is determined to be "actual is lower than predicted"; the contribution deviation rate is greater than 10% but not more than 25%, and the film formation contribution deviation level is determined to be "general deviation".

[0305] C1's film formation status is in the deviation category, with a true damping contribution value of 0.62 corresponding to a medium-high contribution. Meanwhile, its film formation quality data indicates the presence of curing delay and residual moisture anomalies. Therefore, the cause of the anomalies is determined to be film formation quality defects.

[0306] The aforementioned film quality defects indicate that a clear film formation anomaly has been detected, but this anomaly has not yet reduced the overall true damping contribution of C1 to a low-to-medium or low level. The smart state assessment result of C1 is deemed acceptable and requires process review. It is recommended to extend the curing time and adjust the film formation conditions under low temperature and high humidity.

[0307] For sample C2, the predicted damping contribution is 0.73, the actual damping contribution is 0.66, the contribution deviation is 0.07, and the contribution deviation rate is: ; That is, 9.6%. The contribution deviation rate of C2 does not exceed 10%, and the film formation contribution deviation level is determined to be normal fluctuation.

[0308] Although the film formation process data of C2 indicates that its curing time is prolonged and there is a slight risk of residual moisture, its film formation status is generally good. The actual damping contribution value of 0.66 corresponds to a medium-high contribution, and the deviation between the predicted damping contribution value and the actual damping contribution value is within the normal fluctuation range. Therefore, the abnormality cause type is determined to be no obvious abnormality.

[0309] The slight residual moisture risk is not considered an independent cause of the anomaly, but rather retained as a key anomaly indicator in the smart condition assessment results. The smart condition assessment result for C2 is determined to be acceptable, and further observation of the post-curing quality stability is recommended.

[0310] By comparing A1, A2, B1, B2, C1, and C2, it can be seen that the traditional evaluation method mainly relies on surface appearance, film thickness fluctuation, and changes in vibration amplitude after coating to form a qualified or basically qualified conclusion. It is difficult to distinguish abnormal film formation state, changes in actual damping contribution, and insufficient reliability of test data at the same time.

[0311] This invention establishes a continuous data processing chain by incorporating latent state characteristics of film formation, key resonance frequency bands, standard operating condition normalization, predicted damping contribution values, actual damping contribution values, and film formation contribution deviation results, enabling the output of: Are there any abnormalities in the film formation state? Does the vibration response data meet the reliability requirements? Whether the actual damping contribution reaches the preset level; Whether the predicted damping contribution matches the actual damping contribution; The anomalies mainly stem from film formation quality, actual performance, or the reliability of test data.

[0312] In summary, the experimental results of this embodiment show that: First, by generating latent state characteristics of film formation through time-series data of film formation quality, film thickness distribution, infrared thermograms and surface images, it is possible to identify residual moisture, uneven film thickness, abnormal surface thermal state, local shrinkage and microcrack risks. Second, by determining the key resonance frequency band and frequency band weights through the vibration response data of the target substrate before coating, the predicted damping contribution and the actual damping contribution can be concentrated to reflect the damping effect of the frequency band where the main vibration energy of the target substrate is located. Third, by normalizing the standard operating conditions, standard response confidence level and validity labeling, we can avoid low-confidence vibration response data from directly entering the final contribution deviation judgment. Fourth, by analyzing the deviation between the predicted damping contribution value and the actual damping contribution value, and combining the characteristics of the film-forming latent state and the reliability of the data, it is possible to output intelligent state assessment results with causal explanations and retesting basis.

[0313] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A smart state assessment method for water-based damping materials, characterized in that, include: S1: Obtain film formation process data and film formation environment parameters of the water-based damping material to be evaluated on the target substrate, and obtain vibration response data of the target substrate before coating, vibration response data after coating, and vibration response test environment parameters. S2: Generate latent state characteristics of film formation based on the film formation process data and the film formation environment parameters; S3: Determine the key resonant frequency band of the target substrate based on the vibration response data before coating, and generate frequency band weights based on the vibration energy distribution within the key resonant frequency band; S4: Based on the film formation environment parameters and the vibration response test environment parameters, the vibration response data before coating and the vibration response data after coating are normalized to standard operating conditions to obtain standard vibration response data before coating and standard vibration response data after coating. Step S4 specifically includes: A film formation environment correction coefficient is generated based on the deviation of the film formation environment parameters relative to the preset standard film formation conditions, and a test environment correction coefficient is generated based on the deviation of the vibration response test environment parameters relative to the preset standard test conditions. An environmental mapping relationship is constructed based on the film formation environment correction coefficient and the test environment correction coefficient; Based on the test environment correction coefficient, the vibration acceleration, resonance peak amplitude and half-power bandwidth in the pre-coating vibration response data are normalized to standard operating conditions to obtain the pre-coating standard vibration response data. Based on the environmental mapping relationship, the vibration acceleration, resonance peak amplitude, half-power bandwidth and composite loss factor in the post-coating vibration response data are normalized according to standard operating conditions to obtain the post-coating standard vibration response data. Based on the key resonance frequency band, the standard vibration response data before coating and the standard vibration response data after coating are mapped to the same frequency sampling points to generate standard response pairing data before and after coating. Calculate the first response correction difference between the vibration response data before coating and the standard vibration response data before coating at the same frequency sampling point, and the second response correction difference between the vibration response data after coating and the standard vibration response data after coating at the same frequency sampling point, and generate environmental normalized residual values ​​based on the first response correction difference and the second response correction difference. A standard response confidence level is generated based on the environmental normalized residual value, and the pairwise standard response data before and after coating is marked for validity based on the standard response confidence level. S5: Generate a predicted damping contribution value based on the film-forming latent state characteristics, and generate a true damping contribution value based on the standard vibration response data before coating, the standard vibration response data after coating, and the frequency band weight; S6: Generate a film formation contribution deviation result based on the deviation between the predicted damping contribution value and the actual damping contribution value, and output the intelligent state evaluation result of the waterborne damping material to be evaluated based on the film formation latent state characteristics, the actual damping contribution value and the film formation contribution deviation result.

2. The intelligent state assessment method for water-based damping materials as described in claim 1, characterized in that, Step S1 specifically includes: The initial coating mass, film formation quality time series data, mass decay rate, film thickness distribution, surface image, infrared thermogram, surface temperature and curing time of the waterborne damping material to be evaluated on the target substrate are obtained as the film formation process data. The film-forming temperature and humidity during the film-forming process of the water-based damping material to be evaluated are obtained as the film-forming environment parameters; The excitation frequency, vibration acceleration, resonance peak frequency, resonance peak amplitude, and half-power bandwidth of the target substrate before coating are obtained as the vibration response data before coating. The excitation frequency, vibration acceleration, resonance peak frequency, resonance peak amplitude, half-power bandwidth, and composite loss factor of the target substrate after coating are obtained as the vibration response data after coating. The test temperature and test humidity during the acquisition of the vibration response data before and after coating are used as the vibration response test environment parameters. The damping layer coverage and damping layer thickness of the water-based damping material to be evaluated on the target substrate are obtained as damping layer structural parameters. The film formation process data, film formation environment parameters, pre-coating vibration response data, post-coating vibration response data, vibration response test environment parameters, and damping layer structure parameters are associated according to the same target substrate number and the same coating area number.

3. The intelligent state assessment method for water-based damping materials as described in claim 2, characterized in that, Step S2 specifically includes: A moisture evaporation curve is constructed based on the initial coating quality, the film formation quality time series data, the quality decay rate, the film formation temperature, the film formation humidity, and the curing time. A residual moisture risk value is generated based on the degree of deviation of the moisture evaporation curve from the preset standard evaporation curve. The film thickness variation coefficient is calculated based on the film thickness distribution, and a film thickness uniformity value is generated based on the film thickness variation coefficient. The temperature difference between different sub-regions of the damping layer within the coating area is determined based on the infrared thermal image and the surface temperature, and a surface thermal uniformity value is generated based on the temperature difference. Based on the surface image, the local shrinkage region and microcrack region on the surface of the damping layer formed by the water-based damping material to be evaluated are determined, and a local shrinkage risk value is generated based on the local shrinkage region, and a microcrack risk value is generated based on the microcrack region. The latent state characteristics of film formation are generated based on the residual moisture risk value, the film thickness uniformity value, the surface thermal uniformity value, the local shrinkage risk value, and the microcrack risk value.

4. The intelligent state assessment method for water-based damping materials as described in claim 3, characterized in that, Step S3 specifically includes: Based on the excitation frequency and vibration acceleration in the vibration response data before coating, the frequency response curve and vibration energy distribution curve of the target substrate before coating are generated. The key resonant frequency band of the target substrate is determined based on the resonant peak frequency, resonant peak amplitude, and half-power bandwidth in the frequency response curve. The vibration energy within each of the key resonant frequency bands is determined based on the vibration energy distribution curve. Based on the proportion of vibration energy in each key resonance frequency band to the total vibration energy of all key resonance frequency bands, the frequency band weights corresponding to each key resonance frequency band are generated.

5. The intelligent state assessment method for water-based damping materials as described in claim 3, characterized in that, Step S5, which involves generating the predicted damping contribution value based on the film-forming latent state characteristics, specifically includes: The film integrity index is generated by weighting the residual moisture risk value, the film thickness uniformity value, the surface thermal uniformity value, the local shrinkage risk value, and the microcrack risk value. Obtain the standard loss factor curve of the waterborne damping material to be evaluated under standard film-forming conditions, and perform attenuation correction on the standard loss factor curve according to the comprehensive index of film integrity to obtain the predicted loss factor curve. Based on the integral value of the loss factor curve in each of the key resonance frequency bands and the corresponding frequency band weights of each key resonance frequency band, a normalized predicted damping contribution value is generated.

6. The intelligent state assessment method for water-based damping materials as described in claim 5, characterized in that, Step S5, which involves generating the true damping contribution value based on the pre-coating standard vibration response data, the post-coating standard vibration response data, and the frequency band weight, specifically includes: Based on the validity marker, extract valid response pairing data from the standard response pairing data before and after coating; Based on the effective response pairing data, determine the decrease in resonance peak amplitude, the increase in half-power bandwidth, and the decrease in vibration energy within each of the key resonance frequency bands; Based on the frequency band weights corresponding to each of the key resonance frequency bands, the decrease in resonance peak amplitude, the increase in half-power bandwidth, and the decrease in vibration energy are weighted and fused to generate an initial true damping contribution value. The initial true damping contribution value is corrected based on the damping layer coverage and the damping layer thickness to generate a normalized true damping contribution value.

7. The intelligent state assessment method for water-based damping materials as described in claim 6, characterized in that, Step S6, which involves generating a film formation contribution deviation result based on the deviation between the predicted damping contribution value and the actual damping contribution value, specifically includes: The contribution deviation value is calculated based on the predicted damping contribution value and the actual damping contribution value; The contribution deviation value is normalized based on the predicted damping contribution value to generate the contribution deviation rate; Based on the relationship between the actual damping contribution value and the predicted damping contribution value, a contribution deviation direction marker is generated; A film formation contribution deviation level is generated based on the contribution deviation rate and a preset deviation level threshold. The film formation contribution deviation result is generated based on the contribution deviation value, the contribution deviation rate, the contribution deviation direction marker, and the film formation contribution deviation level.

8. The intelligent state assessment method for water-based damping materials as described in claim 7, characterized in that, Step S6, which involves outputting the intelligent state assessment result of the waterborne damping material to be evaluated based on the film-forming latent state characteristics, the true damping contribution value, and the film-forming contribution deviation result, specifically includes: The film formation state level is determined based on the residual moisture risk value, film thickness uniformity value, surface thermal uniformity value, local shrinkage risk value, and microcrack risk value in the film formation latent state characteristics. The actual damping contribution level is determined based on the comparison between the actual damping contribution value and the preset contribution level threshold. The type of abnormal cause is determined based on the matching relationship between the film formation state level, the actual damping contribution level, the film formation contribution deviation level, and the contribution deviation direction mark. The intelligent state assessment result is generated based on the film formation state level, the actual damping contribution level, the film formation contribution deviation level, the contribution deviation direction marker, and the anomaly cause type.

9. A smart state assessment system for water-based damping materials, used to implement the smart state assessment method for water-based damping materials as described in any one of claims 1 to 8, characterized in that, include: Data acquisition module: acquires film formation process data and film formation environment parameters of the water-based damping material to be evaluated on the target substrate, and acquires vibration response data of the target substrate before coating, vibration response data after coating, and vibration response test environment parameters. Film-forming latent state generation module: Generates film-forming latent state characteristics based on the film-forming process data and the film-forming environment parameters; Frequency band weight generation module: Determines the key resonant frequency band of the target substrate based on the vibration response data before coating, and generates frequency band weights based on the vibration energy distribution within the key resonant frequency band; Standard operating condition normalization module: Based on the film formation environment parameters and the vibration response test environment parameters, the vibration response data before coating and the vibration response data after coating are normalized according to standard operating conditions to obtain standard vibration response data before coating and standard vibration response data after coating. Damping contribution value generation module: Generates predicted damping contribution value based on the film-forming latent state characteristics, and generates actual damping contribution value based on the standard vibration response data before coating, the standard vibration response data after coating, and the frequency band weight; Intelligent state assessment module: Generates film formation contribution deviation result based on the deviation between the predicted damping contribution value and the actual damping contribution value, and outputs the intelligent state assessment result of the waterborne damping material to be evaluated based on the film formation latent state characteristics, the actual damping contribution value and the film formation contribution deviation result.

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