Coating material detection method, device and equipment, storage medium and computer program product

Abstract

The invention discloses a coating material detection method, device and equipment, a storage medium and a computer program product, and relates to the technical field of material detection.The method comprises the steps that real-time process data of a coating material is collected; calculating an anti-rust score and a coating adhesion score of the coating material according to the real-time process data; if the anti-rust score and the coating adhesion score both meet the preset non-defective product condition, it is judged that the coating material passes non-defective product detection; and if the anti-rust score and / or the coating adhesion score do not meet the preset good product condition, judging that the coating material does not pass the good product detection. According to the method, the two quantitative indexes of the anti-rust score and the coating adhesion score of the coating material are calculated according to the real-time process data, and the scoring result is compared with the preset good product condition, so that the problems of detection delay and non-uniform standard caused by dependence on lagged laboratory analysis or subjective manual judgment in the prior art are solved; therefore, real-time and reliable good product detection on the automobile coating material in the production process is realized.

Description

Technical Field

[0001] This application relates to the field of materials testing technology, and in particular to a method, apparatus, equipment, storage medium, and computer program product for testing coating materials. Background Technology

[0002] In the automotive manufacturing industry, the performance of coating materials directly determines the corrosion resistance and appearance durability of the vehicle body. Rust prevention and coating adhesion are two crucial core quality indicators. Currently, on-site quality control of automotive coating materials relies primarily on post-production laboratory sampling analysis or manual judgment at the production line end. While laboratory testing is accurate, it is time-consuming and infrequent, failing to reflect real-time fluctuations in the production process. Manual judgment, on the other hand, suffers from strong subjectivity, inconsistent standards, and difficulty in quantification, especially for properties like rust prevention that require long-term verification, making it impossible to provide rapid and objective quality assessments during production. Therefore, the industry urgently needs a method for real-time and reliable quality control of automotive coating materials during the production process. Summary of the Invention

[0003] The main objective of this application is to provide a method, apparatus, equipment, storage medium, and computer program product for testing coating materials, aiming to solve the technical problem that existing technologies cannot perform real-time and reliable good quality testing of automotive coating materials during the production process.

[0004] To achieve the above objectives, this application provides a method for testing coating materials, the method comprising the following steps: Collect real-time process data of coating materials, including sensor data, manual inspection data and equipment detection data; The rust prevention score and coating adhesion score of the coating material are calculated based on the real-time process data. The rust prevention score is used to quantify the rust prevention performance of the coating material, and the coating adhesion score is used to quantify the coating adhesion performance of the coating material. If both the rust prevention score and the coating adhesion score meet the preset good product conditions, then the coating material is determined to have passed the good product test; If the rust prevention score and / or the coating adhesion score do not meet the preset good product conditions, the coating material is determined to have failed the good product test.

[0005] In one embodiment, the step of calculating the rust prevention score and coating adhesion score of the coating material based on the real-time process data includes: The health index of the coating material during the pretreatment and electrophoresis process is determined based on the real-time process data, and the rust prevention score of the coating material is calculated based on the health index. The film thickness, curing conditions, and solvent content of the coating material are determined based on the real-time process data, and the coating adhesion score of the coating material is calculated based on the film thickness, curing conditions, and solvent content.

[0006] In one embodiment, the step of determining the health index of the coating material during the pretreatment and electrophoresis process based on the real-time process data, and calculating the rust prevention score of the coating material based on the health index, includes: The temperature stability factor, conductivity change inhibition factor, apparent defect deduction factor, and phosphating ratio of the coating material are determined based on the real-time process data. Based on the temperature stability factor, the conductivity change inhibition factor, and the apparent defect deduction factor, the health index of the coating material during the stability process of pretreatment and electrophoresis is calculated. The rust resistance score of the coating material is calculated based on the phosphating ratio and the health index.

[0007] In one embodiment, the step of calculating the coating adhesion score of the coating material based on the film thickness data, the curing conditions, and the solvent content includes: Based on the film thickness data, the curing conditions, and the solvent content, the film thickness uniformity index and the curing uniformity index of the coating material are determined. The coating adhesion score of the coating material is calculated based on the film thickness uniformity index and the curing uniformity index.

[0008] In one embodiment, after the step of calculating the rust prevention score and coating adhesion score of the coating material based on the real-time process data, the method further includes: Based on the material type of the coating material, determine the corresponding good product judgment threshold for the coating material; If both the rust prevention score and the coating adhesion score are greater than or equal to the good product determination threshold, then both the rust prevention score and the coating adhesion score are determined to meet the preset good product conditions. If the rust prevention score and / or the coating adhesion score are less than the good product determination threshold, then the rust prevention score and / or the coating adhesion score are determined not to meet the preset good product conditions.

[0009] In one embodiment, after the step of determining that the coating material has failed the good product test if the rust prevention score and / or the coating adhesion score do not meet the preset good product conditions, the method further includes: The historical process flow of the coating material is traced, and abnormal process steps are located from the historical process flow; Based on the aforementioned abnormal process steps, the coating process of the coating workshop to which the coating material belongs is visualized.

[0010] Furthermore, to achieve the above objectives, this application also proposes a coating material testing device, which includes: The data acquisition module is used to collect real-time process data of coating materials, including sensor data, manual inspection data and equipment detection data. The scoring calculation module is used to calculate the rust prevention score and coating adhesion score of the coating material based on the real-time process data. The rust prevention score is used to quantify the rust prevention performance of the coating material, and the coating adhesion score is used to quantify the coating adhesion performance of the coating material. The first determination module is used to determine that the coating material passes the good product test if both the rust prevention score and the coating adhesion score meet the preset good product conditions. The second determination module is used to determine that the coating material has failed the good product test if the rust prevention score and / or the coating adhesion score do not meet the preset good product conditions.

[0011] In addition, to achieve the above objectives, this application also proposes a coating material testing device, the device comprising: a memory, a processor, and a coating material testing program stored in the memory and executable on the processor, the coating material testing program being configured to implement the steps of the coating material testing method described above.

[0012] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, storing a coating material detection program thereon, which, when executed by a processor, implements the steps of the coating material detection method as described above.

[0013] In addition, to achieve the above objectives, the present invention also provides a computer program product, the computer program product including a coating material detection program, which, when executed by a processor, implements the steps of the coating material detection method as described above.

[0014] This application collects real-time process data of coating materials, including sensor data, manual inspection data, and equipment testing data; it calculates rust prevention scores and coating adhesion scores for the coating materials based on the real-time process data, whereby the rust prevention score quantifies the rust prevention performance of the coating materials and the coating adhesion score quantifies the coating adhesion performance of the coating materials; if both the rust prevention score and the coating adhesion score meet preset good product conditions, the coating materials are determined to have passed the good product test; if the rust prevention score and / or the coating adhesion score do not meet the preset good product conditions, the coating materials are determined to have failed the good product test. The method described in this application first integrates multi-source real-time process data, including sensor data, manual inspection data, and equipment testing data, to achieve continuous monitoring and immediate evaluation of the coating material production process. Then, it calculates two quantitative indicators—rust prevention score and coating adhesion score—based on the real-time process data, thereby providing an objective numerical characterization of rust prevention performance and coating adhesion performance. Finally, the scoring results are compared with preset good product conditions, effectively overcoming the problems of detection delays and inconsistent standards caused by existing technologies relying on lagging laboratory analysis or subjective manual judgment. This enables real-time and reliable good product testing of automotive coating materials during the production process. Attached Figure Description

[0015] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating the first embodiment of the coating material testing method of this application; Figure 2 This is a flowchart illustrating the second embodiment of the coating material testing method of this application; Figure 3 This is a flowchart illustrating the third embodiment of the coating material testing method of this application; Figure 4 This is a first visual example of the coating material testing method of this application; Figure 5 This is a second visual example of the coating material testing method of this application; Figure 6 This is a structural block diagram of the first embodiment of the coating material testing device of this application; Figure 7This is a schematic diagram of the coating material testing equipment of this application.

[0018] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of this application and are not intended to limit this application.

[0020] It should be noted that the executing entity of the embodiments of this application can be a computing service device with data processing, network communication, and program execution functions, such as a smart wearable device, a personal computer, or a mobile phone, or an electronic device capable of performing the above functions, such as the aforementioned coating material testing device. The following embodiments will be described using the coating material testing device as an example.

[0021] This application provides a method for testing coating materials, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the coating material testing method of this application.

[0022] In this embodiment, the coating material testing method includes the following steps: Step S10: Collect real-time process data of the coating material, including sensor data, manual inspection data and equipment detection data.

[0023] It should be noted that the aforementioned coating materials refer to composite materials formed by the paint and the metal substrate of the vehicle body, which undergo pretreatment, electrophoresis, spraying, and drying processes in the painting workshop to form a protective or decorative layer. For example, in automotive painting, this composite material includes a pretreated steel sheet (substrate) that has undergone phosphating or other pretreatments, an electrophoretic layer formed by electrophoretic coating, and a mid-coat layer formed by intermediate coating. The aforementioned real-time process data refers to a dynamic set of data collected during the painting production process that reflects the material state, environmental conditions, equipment operation, and process parameters, such as sensor data, manual inspection data, and equipment testing data. The sensor data may include, but is not limited to, pressure values ​​collected by pressure sensors, pH values ​​collected by pH meters, temperatures collected by thermometers, flow rates collected by flow meters, and conductivity data collected by conductivity sensors. Manual inspection data may include, but is not limited to, operators using handheld mobile terminals to record the on / off status of the humidifier in the pretreatment leveling section (text messages such as "on / atomization normal" or "not on") during line inspections, or recording manual measurements of conductivity. Equipment testing data may include, but is not limited to, the analysis results of chemical components such as zirconium ions, free alkali, and total alkali in the bath solution by automatic titration equipment, or the identification results of instrument pointer positions and coating appearance by a visual imaging system.

[0024] It should be understood that in the automotive painting workshop, the system connects various data acquisition terminals through the workshop's local area network, thereby achieving comprehensive coverage of all production condition data in the workshop. First, sensor networks (such as temperature, humidity, and conductivity sensors) are deployed in key processes such as electrophoresis and pretreatment to monitor environmental and bath parameters in real time. Second, operators use mobile terminals (such as tablets) to manually enter data on items that cannot be automatically collected (such as equipment on / off status and manual measurements) according to preset inspection routes and frequencies. At the same time, automated testing equipment (such as automatic titrators and vision imaging systems) are integrated into the production line to perform online analysis of chemical composition and appearance indicators and output data.

[0025] Step S20: Calculate the rust prevention score and coating adhesion score of the coating material based on the real-time process data. The rust prevention score is used to quantify the rust prevention performance of the coating material, and the coating adhesion score is used to quantify the coating adhesion performance of the coating material.

[0026] In practical implementation, the above-mentioned rust prevention score can be calculated by combining real-time process data such as the conductivity, temperature, and pH value of the bath solution in the pretreatment process, and the voltage and film thickness uniformity in the electrophoresis process, through built-in algorithms (such as weighted calculation, comparison with benchmark values ​​and deduction). The higher the rust prevention score, the better the predicted rust prevention performance. The above-mentioned coating adhesion score can be calculated by combining real-time process data such as the cleanliness-related parameters after pretreatment (such as degreasing temperature and water washing conductivity), the curing oven temperature curve after electrophoresis or spraying, and the coating film thickness and uniformity, through built-in algorithms. The higher the coating adhesion score, the better the predicted coating adhesion performance.

[0027] Step S30: If both the rust prevention score and the coating adhesion score meet the preset good product conditions, then the coating material is determined to have passed the good product test.

[0028] It should be noted that the above-mentioned preset good product conditions refer to the quantitative standards or thresholds set in the system in advance for judging whether the coating material is qualified for the rust prevention score and coating adhesion score.

[0029] It should be understood that the preset good product conditions are: rust prevention score ≥ 80 points and coating adhesion score ≥ 85 points. If the rust prevention score (e.g., 82 points) and coating adhesion score (e.g., 88 points) of the coating material are calculated based on the latest real-time process data, the judgment process will be automatically triggered. The system compares these two scores with the preset good product conditions. In this example, since 82 points > 80 points and 88 points > 85 points, both conditions are met. Therefore, the system automatically generates and records the judgment result of "passed good product inspection," which can be updated to the production dashboard or relevant management interface in real time.

[0030] Step S40: If the rust prevention score and / or the coating adhesion score do not meet the preset good product conditions, the coating material is determined to have failed the good product test.

[0031] It should be understood that the preset good product conditions are: rust prevention score ≥ 80 points and coating adhesion score ≥ 85 points. If, based on the latest real-time process data, the rust prevention score of the coating material is calculated to be 78 points and the coating adhesion score is 88 points, during automatic judgment, the system identifies that the rust prevention score of 78 points < 80 points, failing to meet the preset conditions; while the coating adhesion score of 88 points ≥ 85 points, meeting the preset conditions. Because the rust prevention score does not meet the condition, the system automatically determines that the coating material has failed the good product inspection. At this time, the system can immediately associate this judgment result with the corresponding process information (such as pretreatment-electrophoresis process), mark the process with an abnormal color (such as red) on the visualization interface, and generate an alarm message containing specific non-conforming items and values ​​to notify relevant personnel for handling.

[0032] This embodiment collects real-time process data of the coating material, including sensor data, manual inspection data, and equipment testing data. Based on the real-time process data, a rust prevention score and a coating adhesion score are calculated for the coating material. The rust prevention score quantifies the rust prevention performance of the coating material, and the coating adhesion score quantifies the coating adhesion performance of the coating material. If both the rust prevention score and the coating adhesion score meet preset good product conditions, the coating material is determined to have passed the good product test. If the rust prevention score and / or the coating adhesion score do not meet the preset good product conditions, the coating material is determined to have failed the good product test. The method described in this embodiment first integrates multi-source real-time process data, including sensor data, manual inspection data, and equipment testing data, to achieve continuous monitoring and immediate evaluation of the coating material production process. Then, it calculates two quantitative indicators—rust prevention score and coating adhesion score—based on the real-time process data, thereby providing an objective numerical characterization of rust prevention performance and coating adhesion performance. Finally, the scoring results are compared with preset good product conditions, effectively overcoming the problems of detection delays and inconsistent standards caused by existing technologies that rely on lagging laboratory analysis or subjective manual judgment. This enables real-time and reliable good product testing of automotive coating materials during the production process.

[0033] Reference Figure 2 , Figure 2 This is a flowchart illustrating the second embodiment of the coating material testing method of this application.

[0034] In one feasible implementation, step S20 may include: Step S201: Determine the health index of the coating material during the stability process of pretreatment and electrophoresis based on the real-time process data, and calculate the rust prevention score of the coating material based on the health index.

[0035] It should be noted that the stability of the pretreatment and electrophoresis processes mentioned above refers to the degree of fluctuation and controlled state of the process parameters (such as temperature, concentration, conductivity, time, etc.) of the two core processes of pretreatment (such as degreasing, phosphating, and washing) and electrophoresis over time in the coating production process. The health index refers to a comprehensive indicator obtained after quantitatively evaluating the stability of the pretreatment and electrophoresis processes.

[0036] Step S202: Determine the film thickness data, curing conditions and solvent content of the coating material based on the real-time process data, and calculate the coating adhesion score of the coating material based on the film thickness data, the curing conditions and the solvent content.

[0037] It should be noted that the above film thickness data refers to the measured or statistical value of the thickness of the solid coating formed by the coating material on the vehicle body substrate; the above curing conditions refer to the sum of environmental and energy parameters required to transform the coating from the construction state to a solid film with final physicochemical properties during the coating process, such as the actual temperature change curve experienced by the electrophoretic coating or topcoat coating as it passes through the drying oven or curing oven, including parameters such as heating rate, target temperature, holding time, and oven humidity; the above solvent content refers to the relative amount of volatile organic compounds (solvents) contained in the coating system or related physicochemical parameters during coating construction or in the early stage of coating formation, such as the solvent concentration in the bath liquid or coating analyzed by automatic titration equipment, or the concentration of volatile organic compounds in the spraying operation area monitored by environmental sensors.

[0038] In one feasible implementation, step S201 may include: Step S2011: Determine the temperature stability factor, conductivity change inhibition factor, apparent defect deduction factor, and phosphating ratio of the coating material based on the real-time process data.

[0039] It should be noted that the temperature stability factor mentioned above is used to quantify the evaluation value of the degree of temperature fluctuation or deviation from the target value during the coating process; the conductivity change inhibition factor mentioned above is used to quantify the indicator of the ability or degree to effectively control the conductivity change of the bath solution within the preset management range; the apparent defect deduction factor mentioned above is used to quantify the negative evaluation value of the degree of coating quality reduction caused by visible defects (such as particles, pinholes, runs, color difference, etc.) on the coating surface; and the phosphating ratio mentioned above represents the ratio of the total acidity to the free acidity of the phosphating bath solution.

[0040] Step S2012: Based on the temperature stability factor, the conductivity change inhibition factor, and the apparent defect deduction factor, calculate the health index of the coating material during the stability process of pretreatment and electrophoresis.

[0041] In practice, the above health index can be calculated based on the following formula: H(t) = [W1 * F1(t) + W2 * F2(t)] * F3(t); F1(t) = exp( -| (T_phos(t) - T_phos_set) / σ_T | ); F2(t) = 2 / ( 1 + exp( |ΔC_cond(t)| ) ); F3(t) = ( 1 - D_defect(t) ); Where H(t) is the health index, F1(t) is the temperature stability factor, F2(t) is the conductivity change inhibition factor, F3(t) is the apparent defect deduction factor, W1 and W2 are weighting coefficients determined in advance through regression analysis of historical good product data, and satisfy W1 + W2 = 1; T_phos(t) is the temperature of the phosphating bath collected at time t, T_phos_set is the standard temperature value set by the phosphating process, σ_T is the allowable standard deviation of the phosphating temperature control process, exp() is the exponential function; ΔC_cond(t) is the difference between the current time t and the previous sampling time (t-Δt), that is, ΔC_cond(t) = C_cond(t) - C_cond(t-Δt) is the conductivity of the electrophoresis tank liquid collected at time t, and Δt is the preset fixed data sampling time interval; D_defect(t) is the proportion of the frequency of appearance defect descriptions to the total number of inspection items, with a value range of [0, 1].

[0042] Step S2013: Calculate the rust prevention score of the coating material based on the phosphating ratio and the health index.

[0043] In practical implementation, the above rust prevention score can be calculated based on the following formula: R(t) = 100 * [ α * H(t) + β * log10( TA(t) / FA(t) + δ ) + γ * ( C_cond(t) / C_cond_ref ) ] / (α + β + γ); Where R(t) is the rust prevention score, TA(t) / FA(t) is the phosphating ratio, TA(t) is the total acidity of the phosphating bath measured by an automatic titrator at time t, FA(t) is the free acidity of the phosphating bath measured by an automatic titrator at time t, C_cond_ref is the baseline value of the electrophoretic solution conductivity determined according to the electrophoretic coating process specification; α, β, and γ are adjustment coefficients used to balance the contribution of process health, phosphating quality, and electrophoretic parameters to rust prevention performance, which are calibrated jointly by process expert experience and historical data; δ is a small normal number added to prevent TA(t) or FA(t) from being too small and causing invalid logarithmic calculations, usually taken as 0.01.

[0044] In one feasible implementation, step S202 may include: Step S2021: Determine the film thickness uniformity index and curing uniformity index of the coating material based on the film thickness data, the curing conditions and the solvent content.

[0045] It should be noted that the above-mentioned film thickness uniformity index is a numerical indicator used to quantify the uniformity and degree of fluctuation of the coating thickness distribution across the entire coated surface; the above-mentioned curing uniformity index is a numerical indicator used to quantify the uniformity of heat energy (or other curing energy) transfer and distribution on the surface and inside of the coated workpiece during the curing process, as well as the uniformity of the curing reaction completion.

[0046] Step S2022: Calculate the coating adhesion score of the coating material based on the film thickness uniformity index and the curing uniformity index.

[0047] In practical implementation, the above coating adhesion score can be calculated based on the following formula: A(t) = [ Film_uniformity(t) * Cure_index(t) ] / [ 1 + |NV(t) - NV_target| ]; Film_uniformity(t) = 1 / [ 1 + CV( Film_thk(t, loc) ) ]; Cure_index(t) = Σ_z [ exp( - (T_oven(t, z) - T_cure_set)^2 / (2 * σ_cure^2) ) ] / N_z; Where A(t) is the coating adhesion score, Film_uniformity(t) is the film thickness uniformity index, Cure_index(t) is the curing uniformity index, NV(t) is the solid content of the electrophoresis bath liquid obtained by laboratory analysis or online sensors at time t, and NV_target is the target value of solid content specified in the electrophoretic coating technical parameters; Film_thk(t,loc) represents the dry film thickness of the coating measured at a specific location loc on the vehicle body at time t (loc=1,2,...,L, representing preset measurement points such as doors, hoods, and roofs, where L is the total number of preset measurement points); the CV() function is used to calculate the coefficient of variation (i.e., the ratio of standard deviation to mean) of all film thickness values ​​in Film_thk(t, loc) to quantify the degree of film thickness dispersion; T_oven(t, z) represents the temperature value collected at time t and position z (z=1,2,...,Z, representing different temperature zones along the vehicle's travel direction) inside the drying oven. T_cure_set is the theoretical optimal curing temperature required by the coating technical specifications. σ_cure is the allowable process fluctuation range parameter for the curing temperature. N_z is the total number of temperature monitoring points z inside the drying oven. Σ_z represents the summation of the measured values ​​of all temperature monitoring points z (z=1 to N_z) inside the drying oven.

[0048] This embodiment determines the health index of the coating material during the stability process of pretreatment and electrophoresis based on the real-time process data, and calculates the rust prevention score of the coating material based on the health index. The step of determining the health index of the coating material during the stability process of pretreatment and electrophoresis based on the real-time process data, and calculating the rust prevention score of the coating material based on the health index, includes: determining the temperature stability factor, conductivity change inhibition factor, apparent defect deduction factor, and phosphating ratio of the coating material based on the real-time process data; and calculating the rust prevention score of the coating material based on the temperature stability factor, conductivity change inhibition factor, apparent defect deduction factor, and phosphating ratio. The method calculates the health index of the coating material during the pretreatment and electrophoresis process using the inhibition factor and the apparent defect deduction factor; calculates the rust prevention score of the coating material based on the phosphating ratio and the health index; and calculates the coating adhesion score of the coating material based on the film thickness data, the curing conditions, and the solvent content, including: determining the film thickness uniformity index and the curing uniformity index of the coating material based on the film thickness data, the curing conditions, and the solvent content; and calculating the coating adhesion score of the coating material based on the film thickness uniformity index and the curing uniformity index. In this embodiment, the method calculates the rust prevention score based on the health index, which comprehensively reflects process stability, and the phosphating ratio, which reflects the fundamental quality of the pretreatment. This makes the rust prevention score more reflective of long-term rust prevention potential, rather than a single-point parameter state. Calculating the coating adhesion score based on the film thickness uniformity index and the curing uniformity index allows the coating adhesion score to directly relate to key uniformity factors affecting adhesion, thus providing a more refined evaluation.

[0049] Reference Figure 3 , Figure 3 This is a flowchart illustrating the third embodiment of the coating material testing method of this application.

[0050] In one feasible implementation, after step S20, the following may also be included: Step S21: Determine the good product judgment threshold corresponding to the coating material according to the material type of the coating material.

[0051] It should be noted that the above-mentioned material types refer to the categories of coating materials based on their chemical composition, function, application process, or coating system. For example, in automotive coating, they can be classified into types such as "electrophoretic coatings (used to form an anti-corrosion undercoat)," "intermediate coatings (used to fill and increase adhesion)," "color paints (used to provide color)," and "clear coats (used to provide gloss and protection)"; or further subdivided into "epoxy cathodic electrophoretic paints," "polyurethane topcoats," etc.

[0052] It should be understood that the "good product" threshold refers to the preset minimum passing scores for rust prevention and coating adhesion scores that a specific type of coating material must achieve in the good product inspection process. For example, for an electrophoretic coating, the preset good product threshold for rust prevention might be 88 points, and the preset good product threshold for coating adhesion might be 82 points; while for a mid-coat spray coating, the preset good product threshold for rust prevention might be 80 points, and the preset good product threshold for coating adhesion might be 85 points.

[0053] Step S22: If both the rust prevention score and the coating adhesion score are greater than or equal to the good product determination threshold, then it is determined that both the rust prevention score and the coating adhesion score meet the preset good product conditions.

[0054] Step S23: If the rust prevention score and / or the coating adhesion score are less than the good product determination threshold, then it is determined that the rust prevention score and / or the coating adhesion score do not meet the preset good product conditions.

[0055] In the specific implementation, a "material type-threshold mapping table" can be pre-configured. This mapping table contains the mapping relationship between several material types and good product judgment thresholds (including the good product judgment threshold corresponding to the rust prevention score and the good product judgment threshold corresponding to the coating adhesion score).

[0056] In one feasible implementation, after step S40, the following may also be included: Step S50: Trace the historical process flow of the coating material and locate the abnormal process step from the historical process flow.

[0057] It should be noted that the aforementioned historical process flow refers to a complete set of records of all process steps, operating conditions, equipment status, and related test data that the coating material undergoes in chronological order throughout its entire production cycle. For example, for a specific car body, its historical process flow may include: the time of entry into the painting workshop, the process parameters (temperature, concentration, time) of each pretreatment tank (degreasing, phosphating, water washing), the voltage, current, tank solution composition, film thickness measurement value of the electrophoresis process, the robot path parameters for the intermediate coat and topcoat spraying, paint batches, temperature curves of the curing oven, and the results records of manual and automatic inspections for each process. The aforementioned abnormal process links indicate specific processes where, through rule comparison or model analysis, process parameters, equipment status, or inspection results deviated from the preset good product conditions.

[0058] Step S60: Visualize the coating process of the coating workshop to which the coating material belongs based on the abnormal process steps.

[0059] In the specific implementation, you can refer to Figure 4 , Figure 4 This is a first visual example of the coating material testing method of this application. Figure 4 As shown, the process conditions for achieving good products in each step are visualized. When there are no abnormalities in the good product conditions, green is displayed (e.g., ...). Figure 4 The third water wash, fourth DIP water wash, pure water wash, pretreatment leveling chamber, and UF1 water wash are all examples of processes that typically produce good products. An abnormal condition will cause a yellow tint (e.g., ...). Figure 4 (In the pure water room), and if the conditions for rust prevention products are abnormal, a red indicator will be displayed (e.g., ...). Figure 4 (The formation and electrophoresis tanks in the process). Additionally, you can refer to... Figure 5 , Figure 5 This is a second visual example of the coating material testing method of this application. Figure 5 As shown, for film thickness data of various vehicle models, the system allows selection of different vehicle models, times, and shifts to display the current film thickness of various parts. When the film thickness is lower than the benchmark, the area is displayed in red; when the film thickness reaches the benchmark, it is displayed in yellow. The color transparency gradually changes according to the film thickness value to indicate the relative size of the film thickness in different areas. Furthermore, different vehicle models and shifts can be selected, and by entering a date period, the corresponding average film thickness shift can be displayed, allowing for a direct view of film thickness changes caused by parameter fluctuations over different periods.

[0060] This embodiment determines the good product judgment threshold corresponding to the coating material based on the material type. If both the rust prevention score and the coating adhesion score are greater than or equal to the good product judgment threshold, then both the rust prevention score and the coating adhesion score are determined to meet the preset good product conditions. If the rust prevention score and / or the coating adhesion score are less than the good product judgment threshold, then the rust prevention score and / or the coating adhesion score are determined to not meet the preset good product conditions. The historical process flow of the coating material is traced, and abnormal process links are located from the historical process flow. Based on the abnormal process links, the coating process of the coating workshop to which the coating material belongs is visualized. The above method of this embodiment first dynamically determines the good product judgment threshold according to the material type, so that the judgment standard can adapt to the characteristics and quality requirements of different coating materials, improving the pertinence and accuracy of the judgment. Secondly, by automatically comparing the score with the threshold to determine whether the coating material passes the good product test, when the coating material fails the good product test, the historical process flow of the coating material is traced, and the specific process link where the abnormality occurs is automatically located. Finally, based on the abnormal process steps, the coating process of the coating workshop to which the coating material belongs is visualized, which enables managers to intuitively and quickly understand the process location, context and related data of the abnormality, thereby improving the efficiency of abnormal response.

[0061] Reference Figure 6 , Figure 6 This is a structural block diagram of the first embodiment of the coating material testing device of this application.

[0062] like Figure 6 As shown, the coating material testing device proposed in this application includes: Data acquisition module 601 is used to acquire real-time process data of coating materials, including sensor data, manual inspection data and equipment detection data; The scoring calculation module 602 is used to calculate the rust prevention score and coating adhesion score of the coating material based on the real-time process data. The rust prevention score is used to quantify the rust prevention performance of the coating material, and the coating adhesion score is used to quantify the coating adhesion performance of the coating material. The first determination module 603 is used to determine that the coating material passes the good product test if both the rust prevention score and the coating adhesion score meet the preset good product conditions. The second determination module 604 is used to determine that the coating material has failed the good product test if the rust prevention score and / or the coating adhesion score do not meet the preset good product conditions.

[0063] This embodiment collects real-time process data of the coating material, including sensor data, manual inspection data, and equipment testing data. Based on the real-time process data, a rust prevention score and a coating adhesion score are calculated for the coating material. The rust prevention score quantifies the rust prevention performance of the coating material, and the coating adhesion score quantifies the coating adhesion performance of the coating material. If both the rust prevention score and the coating adhesion score meet preset good product conditions, the coating material is determined to have passed the good product test. If the rust prevention score and / or the coating adhesion score do not meet the preset good product conditions, the coating material is determined to have failed the good product test. The method described in this embodiment first integrates multi-source real-time process data, including sensor data, manual inspection data, and equipment testing data, to achieve continuous monitoring and immediate evaluation of the coating material production process. Then, it calculates two quantitative indicators—rust prevention score and coating adhesion score—based on the real-time process data, thereby providing an objective numerical characterization of rust prevention performance and coating adhesion performance. Finally, the scoring results are compared with preset good product conditions, effectively overcoming the problems of detection delays and inconsistent standards caused by existing technologies that rely on lagging laboratory analysis or subjective manual judgment. This enables real-time and reliable good product testing of automotive coating materials during the production process.

[0064] Based on the first embodiment of the coating material testing device described in this application, a second embodiment of the coating material testing device of this application is proposed.

[0065] In this embodiment, the scoring calculation module 602 is further configured to determine the health index of the coating material during the stability process of pretreatment and electrophoresis based on the real-time process data, and calculate the rust prevention score of the coating material based on the health index; determine the film thickness data, curing conditions and solvent content of the coating material based on the real-time process data, and calculate the coating adhesion score of the coating material based on the film thickness data, the curing conditions and the solvent content.

[0066] Furthermore, the scoring calculation module 602 is also used to determine the temperature stability factor, conductivity change inhibition factor, apparent defect deduction factor, and phosphating ratio of the coating material based on the real-time process data; calculate the health index of the coating material during the stability process of pretreatment and electrophoresis based on the temperature stability factor, the conductivity change inhibition factor, and the apparent defect deduction factor; and calculate the rust prevention score of the coating material based on the phosphating ratio and the health index.

[0067] Furthermore, the scoring calculation module 602 is also used to determine the film thickness uniformity index and curing uniformity index of the coating material based on the film thickness data, the curing conditions and the solvent content; and to calculate the coating adhesion score of the coating material based on the film thickness uniformity index and the curing uniformity index.

[0068] Furthermore, the scoring calculation module 602 is also used to determine the good product judgment threshold corresponding to the coating material according to the material type of the coating material; if the rust prevention score and the coating adhesion score are both greater than or equal to the good product judgment threshold, then it is determined that the rust prevention score and the coating adhesion score both meet the preset good product conditions; if the rust prevention score and / or the coating adhesion score are less than the good product judgment threshold, then it is determined that the rust prevention score and / or the coating adhesion score do not meet the preset good product conditions.

[0069] Furthermore, the second determination module 604 is also used to trace the historical process flow of the coating material and locate abnormal process links from the historical process flow; and to visualize the coating process of the coating workshop to which the coating material belongs based on the abnormal process links.

[0070] Other embodiments or specific implementations of the coating material testing device of this application can be found in the above-described method embodiments, and will not be repeated here.

[0071] This application provides a coating material testing device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the coating material testing method in the first embodiment described above.

[0072] The following reference Figure 7 The diagram illustrates a structural schematic of a coating material testing device suitable for implementing embodiments of this application. The coating material testing device in these embodiments may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 7 The coating material testing equipment shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0073] like Figure 7 As shown, the coating material inspection equipment may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the coating material inspection equipment. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the coating material inspection equipment to communicate wirelessly or wiredly with other equipment to exchange data. Although the figures show coating material inspection equipment with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0074] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0075] The coating material testing equipment provided in this application, employing the coating material testing method described in the above embodiments, can solve the technical problem that existing technologies cannot perform real-time and reliable good-quality testing of automotive coating materials during the production process. Compared with the prior art, the beneficial effects of the coating material testing equipment provided in this application are the same as those of the coating material testing method provided in the above embodiments, and other technical features of this coating material testing equipment are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0076] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0077] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0078] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the coating material detection method in the above embodiments.

[0079] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0080] The aforementioned computer-readable storage medium may be included in the coating material testing equipment; or it may exist independently and not assembled into the coating material testing equipment.

[0081] The aforementioned computer-readable storage medium carries one or more programs that, when executed by the coating material inspection device, enable the coating material inspection device to write computer program code for performing the operations of this application in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++; and also conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, such as a local area network (LAN) or a wide area network (WAN), or connected to an external computer (e.g., via the Internet using an Internet service provider).

[0082] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0083] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0084] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described coating material detection method. This solves the technical problem that existing technologies cannot perform real-time and reliable good-quality inspection of automotive coating materials during the production process. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the coating material detection method provided in the above embodiments, and will not be repeated here.

[0085] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the coating material detection method described above.

[0086] The computer program product provided in this application can solve the technical problem of coating material detection. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the coating material detection method provided in the above embodiments, and will not be repeated here.

[0087] The above description is only a part of the embodiments of this application and does not limit the scope of protection of this application. All equivalent structural transformations made under the technical concept of this application and using the content of this application specification and drawings, or direct / indirect applications in other related technical fields, are included in the scope of protection of this application.

Claims

1. A method for testing coating materials, characterized in that, The method includes the following steps: Collect real-time process data of coating materials, including sensor data, manual inspection data and equipment detection data; The rust prevention score and coating adhesion score of the coating material are calculated based on the real-time process data. The rust prevention score is used to quantify the rust prevention performance of the coating material, and the coating adhesion score is used to quantify the coating adhesion performance of the coating material. If both the rust prevention score and the coating adhesion score meet the preset good product conditions, then the coating material is determined to have passed the good product test; If the rust prevention score and / or the coating adhesion score do not meet the preset good product conditions, the coating material is determined to have failed the good product test.

2. The coating material testing method as described in claim 1, characterized in that, The step of calculating the rust prevention score and coating adhesion score of the coating material based on the real-time process data includes: The health index of the coating material during the pretreatment and electrophoresis process is determined based on the real-time process data, and the rust prevention score of the coating material is calculated based on the health index. The film thickness, curing conditions, and solvent content of the coating material are determined based on the real-time process data, and the coating adhesion score of the coating material is calculated based on the film thickness, curing conditions, and solvent content.

3. The coating material testing method as described in claim 2, characterized in that, The step of determining the health index of the coating material during the stability process of pretreatment and electrophoresis based on the real-time process data, and calculating the rust prevention score of the coating material based on the health index, includes: The temperature stability factor, conductivity change inhibition factor, apparent defect deduction factor, and phosphating ratio of the coating material are determined based on the real-time process data. Based on the temperature stability factor, the conductivity change inhibition factor, and the apparent defect deduction factor, the health index of the coating material during the stability process of pretreatment and electrophoresis is calculated. The rust resistance score of the coating material is calculated based on the phosphating ratio and the health index.

4. The coating material testing method as described in claim 2, characterized in that, The step of calculating the coating adhesion score of the coating material based on the film thickness data, the curing conditions, and the solvent content includes: Based on the film thickness data, the curing conditions, and the solvent content, the film thickness uniformity index and the curing uniformity index of the coating material are determined. The coating adhesion score of the coating material is calculated based on the film thickness uniformity index and the curing uniformity index.

5. The coating material testing method as described in claim 1, characterized in that, After the step of calculating the rust prevention score and coating adhesion score of the coating material based on the real-time process data, the method further includes: Based on the material type of the coating material, determine the corresponding good product judgment threshold for the coating material; If both the rust prevention score and the coating adhesion score are greater than or equal to the good product determination threshold, then both the rust prevention score and the coating adhesion score are determined to meet the preset good product conditions. If the rust prevention score and / or the coating adhesion score are less than the good product determination threshold, then the rust prevention score and / or the coating adhesion score are determined not to meet the preset good product conditions.

6. The coating material testing method as described in claim 1, characterized in that, After the step of determining that the coating material has failed the good product test if the rust prevention score and / or the coating adhesion score do not meet the preset good product conditions, the method further includes: The historical process flow of the coating material is traced, and abnormal process steps are located from the historical process flow; Based on the aforementioned abnormal process steps, the coating process of the coating workshop to which the coating material belongs is visualized.

7. A coating material testing device, characterized in that, The coating material testing device includes: The data acquisition module is used to collect real-time process data of coating materials, including sensor data, manual inspection data and equipment detection data. The scoring calculation module is used to calculate the rust prevention score and coating adhesion score of the coating material based on the real-time process data. The rust prevention score is used to quantify the rust prevention performance of the coating material, and the coating adhesion score is used to quantify the coating adhesion performance of the coating material. The first determination module is used to determine that the coating material passes the good product test if both the rust prevention score and the coating adhesion score meet the preset good product conditions. The second determination module is used to determine that the coating material has failed the good product test if the rust prevention score and / or the coating adhesion score do not meet the preset good product conditions.

8. A coating material testing device, characterized in that, The device includes: a memory, a processor, and a coating material detection program stored in the memory and executable on the processor, the coating material detection program being configured to implement the steps of the coating material detection method as described in any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and the storage medium stores a coating material detection program, which, when executed by a processor, implements the steps of the coating material detection method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a coating material detection program, which, when executed by a processor, implements the steps of the coating material detection method as described in any one of claims 1 to 6.

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