A quality detection and analysis method for an ultrathin carbon-coated aluminum foil coating

Abstract

The application discloses a quality detection and analysis method for an ultrathin carbon-coated aluminum foil coating, relates to the technical field of aluminum foil coatings, and synchronously collects three types of data, namely, vibration, spacing and angle, and then fuses the three types of data into vibration factors and offset factors to cross-generate normalized abnormal offset coefficients; secondary progressive determination of abnormal offset fluctuation values is realized through qualitative determination of whether abnormal offset exists or not and then variance method analysis, automatic identification and grading early warning of occasional interference and continuous fluctuation are realized, vibration false reports in ultrathin coating area density measurement are effectively inhibited while the detection rhythm is ensured. In the application, the vibration, spacing and angle multi-source heterogeneous data are fused into dimensionless abnormal offset coefficients, so that weak real signals are prevented from being submerged by noise; secondary progressive determination of abnormal offset fluctuation values is realized through qualitative comparison of a threshold value and then variance method analysis, so that continuous fluctuation pollution of data is prevented and frequent false reports caused by occasional jumps are eliminated.

Description

Technical Field

[0001] This invention relates to the field of aluminum foil coating technology, specifically to a quality testing and analysis method for ultra-thin carbon-coated aluminum foil coatings. Background Technology

[0002] The quality inspection of ultra-thin carbon-coated aluminum foil uses an X-ray areal density meter to perform non-contact scanning of the carbon coating's areal density. Utilizing the principle that X-ray intensity attenuates as it penetrates the carbon coating and aluminum foil substrate, and that the attenuation is strictly proportional to the coating's mass per unit area, the areal density of the carbon layer is deduced by accurately measuring the X-ray intensity before and after penetration. This non-contact measurement method does not scratch the extremely thin carbon layer and allows for high-speed continuous scanning during roll-to-roll conveying, outputting a real-time areal density distribution map. This accurately identifies anomalies such as coating thinness, thickness, or striped unevenness, providing immediate data support for coating uniformity control.

[0003] However, when the X-ray surface density meter performs non-contact scanning of the surface density of the carbon coating, it is subjected to vibrations caused by the working impact of the surrounding slitting equipment. These vibrations are transmitted through the frame and foundation, causing the X-ray measuring head to produce slight displacement and angular sway, which instantaneously changes the path length and incident angle of the rays through the aluminum foil. This causes fluctuations in the intensity received by the detector that are not related to the coating, which are misinterpreted as changes in surface density. The true signal of the ultrathin coating is weak, and the vibration noise can easily drown it out, causing the readings to fluctuate irregularly and triggering false alarms. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a quality inspection and analysis method for ultra-thin carbon-coated aluminum foil coatings. This method solves the problem that the impact vibration of surrounding slitting equipment is transmitted through the frame foundation, causing slight displacement and sway of the X-ray measuring head, which disturbs the X-ray path and incident angle, leading to the detector signal being misread as a coating change. The true signal of the ultra-thin coating is easily submerged, causing false alarms.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a quality inspection and analysis method for ultra-thin carbon-coated aluminum foil coatings, comprising the following specific steps: Step 1: Real-time acquisition of vibration data, spacing data, and angle data, and preprocessing thereof; Step 2: Calculation of vibration factor based on vibration data, calculation of offset factor based on spacing and angle data and normalization thereof, calculation of abnormal offset coefficient based on vibration factor and offset factor and normalization thereof, analysis of whether abnormal offset has occurred based on abnormal offset coefficient, if no abnormal offset is found, return directly to Step 1; if abnormal offset is found, proceed to Step 3; Step 3: Further analysis of volatility based on abnormal offset coefficient, if volatility is found, an early warning is issued; if no volatility is found, no early warning is issued, and the process returns to Step 1 or terminates directly.

[0006] Furthermore, the vibration factor is calculated as follows: a preset vibration amplitude threshold is set, and under a time series, the vibration data value at any time is calculated with the vibration amplitude threshold to obtain the vibration anomaly difference. The number of vibration anomaly differences is counted to obtain the vibration factor.

[0007] Furthermore, the method for obtaining the vibration anomaly difference is as follows: ;in, Indicates the abnormal vibration difference. Indicates the first One vibration data value, This indicates the vibration amplitude threshold.

[0008] Furthermore, the statistical method for the number of vibration abnormality differences is as follows: compare the vibration abnormality difference with zero. If the vibration abnormality difference is greater than zero, it is recorded; if the vibration abnormality difference is less than or equal to zero, it is not recorded. Finally, the recorded vibration abnormality differences are statistically analyzed.

[0009] Furthermore, the offset factor is calculated as follows: the spacing factor is calculated based on the spacing data, the angle factor is calculated based on the angle data, and the offset factor is obtained by multiplying the spacing factor and the angle factor. ;in, Indicates the offset factor. Indicates the spacing factor. Represents the angle factor.

[0010] Furthermore, the spacing factor is calculated as follows: in the time series, the difference between the spacing data value of the next moment and the spacing data value of the previous moment is calculated sequentially, and then the summation is performed to obtain the spacing factor.

[0011] Furthermore, the angle factor is calculated as follows: in the time series, the angle data value at the next moment is calculated by subtracting the angle data value at the previous moment, and then the results are summed to obtain the angle factor.

[0012] Furthermore, the specific method for obtaining the abnormal offset coefficient is as follows: set a normal vibration factor and a normal offset factor, compare the vibration factor with the normal vibration factor, if the vibration factor is greater than the normal vibration factor, then assign a value of 1 to the vibration factor, otherwise do not assign a value; compare the offset factor with the normal offset factor, if the offset factor is greater than the normal offset factor, then assign a value of 1 to the offset factor, otherwise do not assign a value; add the vibration factor and the offset factor together to obtain the abnormal offset coefficient.

[0013] Furthermore, the method for analyzing whether an abnormal offset has occurred based on the abnormal offset coefficient is as follows: compare the abnormal offset coefficient with 2. If the abnormal offset coefficient is equal to 2, it indicates that an abnormal offset has occurred; if the abnormal offset coefficient is not equal to 2, it indicates that it is normal.

[0014] Furthermore, the abnormal offset coefficient is further analyzed for volatility as follows: in the time series, the abnormal offset coefficient is calculated using the variance method to obtain the abnormal offset volatility value. The average of the historical abnormal offset volatility values ​​is calculated to obtain the abnormal offset volatility threshold. The abnormal offset volatility value is compared with the abnormal offset volatility threshold. If the abnormal offset volatility value is greater than the abnormal offset volatility threshold, it indicates volatility; if the abnormal offset volatility value is less than or equal to the abnormal offset volatility threshold, it indicates no volatility.

[0015] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: 1. By extracting vibration data into vibration factors, fusing spacing and angle data into offset factors, and then generating abnormal offset coefficients through cross-assignment of the two, this transformation of multi-source heterogeneous data into normalized factors unifies the physical quantities of vibration intensity and geometric misalignment into dimensionless evaluation criteria, effectively solving the problem of false alarms caused by the weak true surface density signal of ultrathin coatings being easily submerged by vibration noise.

[0016] 2. The first level quickly determines whether there is an abnormal offset by comparing the threshold; the second level uses the variance method to analyze whether the abnormal offset fluctuation value exceeds the historical abnormal offset fluctuation threshold. This design avoids continuous dynamic fluctuations from polluting the entire measurement data and prevents occasional single-point jumps from triggering frequent false alarms.

[0017] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0018] Figure 1 This is a flowchart of the quality inspection and analysis method for the ultra-thin carbon-coated aluminum foil coating of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0021] Example 1: like Figure 1 As shown, this embodiment of the invention provides a method for quality testing and analysis of ultra-thin carbon-coated aluminum foil coatings, including the following specific steps: Step 1: The vibration data of the X-ray measuring head is collected in real time using an IEPE type axial piezoelectric accelerometer. This sensor is directly and rigidly fixed to the mounting plate of the X-ray measuring head with bolts. The three measuring axes are respectively aligned with the vertical, horizontal and longitudinal directions of the measuring head coordinate system. The distance data between the X-ray measuring head and the aluminum foil is collected in real time by a laser triangular reflection displacement sensor. This sensor is installed on the side of the measuring head and rigidly fixed to the measuring head. The laser spot is perpendicular to the surface of the aluminum foil and the distance value is collected in real time at a frequency of several kilohertz. The device, mounted on the X-ray measuring head housing, collects instantaneous angle data around the horizontal and vertical axes in real time using a MEMS tilt meter. The vibration data, spacing data, and angle data are cleaned to remove redundant values ​​and improve the data quality.

[0022] Step 2: Calculate the vibration factor based on the vibration data, and calculate the offset factor based on the spacing and angle data. At the same time, normalize the spacing and angle data to eliminate dimensional differences and convert values ​​of different orders of magnitude into a unified numerical range. Calculate the abnormal offset coefficient based on the vibration factor and offset factor, and normalize it as well. Analyze whether abnormal offset has occurred based on the abnormal offset coefficient. If no abnormal offset is found, return directly to Step 1; if an abnormal offset is found, proceed to Step 3.

[0023] Step 3: Further analyze the volatility based on the abnormal offset coefficient. If volatility is detected, issue an early warning. If no volatility is detected, do not issue an early warning and return to Step 1 or end directly. Further analyzing volatility after detecting abnormal offset is to distinguish between occasional single-point impacts and continuous dynamic jitter in engineering.

[0024] Example 2 differs from Example 1 in that: The vibration factor is calculated as follows: The vibration amplitude threshold is preset by establishing a μ+kσ statistical baseline according to frequency bands using long-term vibration data under interference-free conveyor conditions. Then, the threshold is tightened at the resonant frequency based on the modal test results of the measuring head, and verified with real impact data to form the vibration amplitude threshold. In the time series, the vibration data value at any time is calculated with the vibration amplitude threshold to obtain the vibration anomaly difference. The number of vibration anomaly differences is counted to obtain the vibration factor.

[0025] The method for obtaining the vibration anomaly difference is as follows: ; in, Indicates the abnormal vibration difference. Indicates the first One vibration data value, This indicates the vibration amplitude threshold.

[0026] The statistical method for counting the number of vibration anomaly differences is as follows: The vibration anomaly difference is compared with zero. If the vibration anomaly difference is greater than zero, it is recorded; if the vibration anomaly difference is less than or equal to zero, it is not recorded. Finally, the recorded vibration anomaly differences are statistically analyzed.

[0027] The offset factor is calculated as follows: The spacing factor is calculated based on the spacing data, the angle factor is calculated based on the angle data, and the offset factor is obtained by multiplying the spacing factor and the angle factor. ; in, Indicates the offset factor. Indicates the spacing factor. Represents the angle factor.

[0028] The spacing factor is calculated as follows: In the time series, the difference between the spacing data value at the next moment and the spacing data value at the previous moment is calculated and then summed to obtain the spacing factor. That is, the total fluctuation of the distance between the measuring head and the aluminum foil during this time period is statistically analyzed. A single large jump or frequent high-frequency small jitter will increase the cumulative difference value, so that the spacing factor can comprehensively reflect the total intensity of the change in path length, avoiding the failure to detect continuous small jitter by relying solely on a single-point threshold.

[0029] The angle factor is calculated as follows: In the time series, the angle data value at the next moment is successively calculated by subtracting from the angle data value at the previous moment, and then summed to obtain the angle factor. That is, by continuously subtracting and summing the angle data at adjacent moments, the repeated small deviations of the measuring head around the horizontal and vertical axes can be magnified into a macroscopic cumulative oscillation index. Whether it is a steep angle jump caused by a violent impact or a continuous angle oscillation caused by structural resonance, it will be included in the angle factor, thereby accurately assessing the overall degree of interference of the incident angle and ensuring the integrity of the identification of geometric misalignment.

[0030] The specific method for obtaining the abnormal offset coefficient is as follows: Set a normal vibration factor and a normal offset factor. Compare the vibration factor with the normal vibration factor. If the vibration factor is greater than the normal vibration factor, assign a value of 1 to the vibration factor; otherwise, do not assign a value. Compare the offset factor with the normal offset factor. If the offset factor is greater than the normal offset factor, assign a value of 1 to the offset factor; otherwise, do not assign a value. Add the vibration factor and the offset factor together to calculate the abnormal offset coefficient.

[0031] The method for analyzing whether an abnormal offset has occurred based on the abnormal offset coefficient is as follows: Compare the abnormal offset coefficient with 2. If the abnormal offset coefficient is equal to 2, it indicates that an abnormal offset has occurred. If the abnormal offset coefficient is not equal to 2, it indicates that it is normal.

[0032] Further analysis of volatility using the abnormal offset coefficients is as follows: In time series analysis, the abnormal offset coefficient is calculated using the variance method to obtain the abnormal offset fluctuation value. The average of historical abnormal offset fluctuation values ​​is then used to calculate the abnormal offset fluctuation threshold. The abnormal offset fluctuation value is compared with the abnormal offset fluctuation threshold. If the abnormal offset fluctuation value is greater than the abnormal offset fluctuation threshold, it indicates fluctuation; if the abnormal offset fluctuation value is less than or equal to the abnormal offset fluctuation threshold, it indicates no fluctuation. In engineering, variance is a core indicator for measuring the degree of dispersion of a data sequence around its mean. In the interference scenario of ultra-thin coating measurement, the geometric misalignment caused by instantaneous impact often manifests as an isolated pulse spike, and the subsequent data will immediately return to stability, so the variance of the entire data segment does not change much. However, the interference caused by equipment resonance or loose connection is a continuous and repeated violent oscillation, which manifests as the abnormal offset coefficient fluctuating violently back and forth in a short period of time. This directly leads to a sharp amplification of the variance value. Therefore, introducing the variance method to analyze the abnormal offset coefficient essentially utilizes the statistical difference between the low dispersion of a single impact and the high dispersion of continuous resonance to establish a clear numerical criterion, ensuring that the system only issues warnings for truly dangerous vibrations that will repeatedly contaminate the data.

[0033] The preferred embodiments disclosed in this invention are merely illustrative examples of feasible implementation methods and are not intended to exhaustively cover all technical details of the invention, nor do they constitute a limitation on the scope of protection of this invention. In practical applications, those skilled in the art can make appropriate adjustments, combinations, or substitutions to the methods or systems described in these embodiments based on specific production conditions, equipment configurations, and process requirements, without departing from the core concept of this invention. For example, the acquisition method, data processing algorithm, control threshold, or specific implementation form of the execution unit can all be reasonably modified according to the actual situation.

[0034] Furthermore, the core concept of the technical solution disclosed in this specification lies in establishing a universal interference identification and suppression framework based on multi-source heterogeneous signal factorization fusion and two-level progressive discrimination for occasional mechanical vibration interference in precision online measurement. This framework itself possesses technical universality independent of specific application scenarios. Its technical core can be abstracted into four orderly and interconnected processing levels: the first level is the multi-source physical field synchronous sensing layer, which requires the synchronous acquisition of a first type of signal directly representing the physical intensity of vibration at the measuring head and its mounting base, as well as a second type of signal and a third type of signal directly representing the degree of geometric misalignment between the measuring head and the measured object. The second type of signal corresponds to the change in the normal distance between the measuring head and the surface of the measured object, and the third type of signal corresponds to the angular deflection of the measuring head axis relative to the normal of the surface of the measured object. The three types of signals are strictly aligned in the time domain, constituting the basic data source for all subsequent discriminations. The second layer is the heterogeneous data standardization mapping layer. Through a pre-set statistical model or feature extraction algorithm, the original signals with different physical dimensions are transformed into dimensionless or uniformly scaled first and second characterization factors, thereby eliminating the dimensional and numerical differences between physical quantities such as acceleration, displacement, and angle, and providing calculable standardized input for cross-source fusion. The third layer is the causal coupling joint judgment layer, which constructs a joint judgment rule or model to forcibly bind the first and second characterization factors causally, generating a composite interference risk coefficient. The engineering significance of this coefficient is that an interference event is confirmed only when the vibration intensity and geometric misalignment both exceed their respective normal baselines, thus eliminating false interference represented by only single-source factor anomalies from a mechanistic perspective. The fourth level is the time-domain distribution characteristic hierarchical response layer, which implements at least two progressive discriminations on the risk coefficient of composite interference. The first discrimination determines whether there are unacceptable geometric misalignment events caused by vibration. The second discrimination further analyzes the time-domain distribution characteristics of the confirmed events to distinguish between occasional isolated impacts and continuous dynamic fluctuations, and triggers early warning or feedback control only for continuous dynamic fluctuations.

[0035] The technical framework comprised of the aforementioned four-layer architecture possesses a high degree of abstraction and versatility in its underlying logic, thus naturally possessing the ability to be extended to similar technical fields and related industrial processes. Specifically, any industrial online inspection scenario involving precision non-contact online thickness, areal density, profile, or dimensional measurements, where there are micron or sub-micron level precision geometric gaps between the measuring probe and the surface of the measured object, and where the measurement environment faces intermittent mechanical shock interference from the operation of nearby equipment, can solve the problem of abnormal fluctuations in measurement readings and false alarms caused by vibration by analogy with the core framework of this invention. Typical scalable application scenarios include, but are not limited to: online laser thickness or X-ray areal density measurement of separators or electrodes in the lithium-ion battery manufacturing field; online spectral or X-ray monitoring of coating thickness during vacuum evaporation or magnetron sputtering of optical functional thin films; online closed-loop control system for thickness during metal foil rolling; non-contact surface profile scanning measurement in semiconductor wafer manufacturing; and online optical inspection of various wet or dry film thicknesses in precision coating processes. In the above scenario, the technology transfer can be completed simply by adapting the vibration sensor corresponding to the first type of signal to the mounting base of the corresponding measuring head, adapting the spacing and angle sensing methods corresponding to the second and third types of signals to the geometric relationship between the corresponding measuring head and the object being measured, and adjusting the thresholds and parameters in each level of the algorithm according to the specific process cycle and interference characteristics.

[0036] Therefore, the descriptions in this specification are merely illustrative. Any adjustments to implementation methods, equivalent substitutions of technical features, or further applications based on the concept of this invention, as long as they do not depart from the overall technical approach described in this invention, should be included within the scope of protection of this invention. We encourage those skilled in the art to innovate and optimize based on their understanding of the core of this invention and in conjunction with specific practices, so as to jointly promote the progress and development of related technologies.

Claims

1. A method for quality inspection and analysis of ultra-thin carbon-coated aluminum foil coating, characterized in that: The specific steps include the following: Step 1: Collect vibration data, spacing data, and angle data in real time, and perform preprocessing; Step 2: Calculate the vibration factor based on the vibration data, calculate the offset factor based on the spacing and angle data and normalize it, calculate the abnormal offset coefficient based on the vibration factor and offset factor and normalize it, analyze whether abnormal offset has occurred based on the abnormal offset coefficient. If no abnormal offset is found, return directly to Step 1; if an abnormal offset is found, proceed to Step 3. Step 3: Further analyze the volatility based on the abnormal offset coefficient. If volatility is detected, issue an early warning. If no volatility is detected, do not issue an early warning and return to Step 1 or end the process directly.

2. The method for quality inspection and analysis of ultra-thin carbon-coated aluminum foil according to claim 1, characterized in that: The vibration factor is calculated as follows: A preset vibration amplitude threshold is set. In a time series, the vibration data value at any time is calculated with the vibration amplitude threshold to obtain the vibration anomaly difference. The number of vibration anomaly differences is counted to obtain the vibration factor.

3. The method for quality inspection and analysis of ultra-thin carbon-coated aluminum foil according to claim 2, characterized in that: The method for obtaining the vibration anomaly difference is as follows: ； in, Indicates the abnormal vibration difference. Indicates the first One vibration data value, This indicates the vibration amplitude threshold.

4. The method for quality inspection and analysis of ultra-thin carbon-coated aluminum foil according to claim 3, characterized in that: The statistical method for the number of vibration anomaly differences is as follows: The vibration anomaly difference is compared with zero. If the vibration anomaly difference is greater than zero, it is recorded; if the vibration anomaly difference is less than or equal to zero, it is not recorded. Finally, the recorded vibration anomaly differences are statistically analyzed.

5. The method for quality inspection and analysis of ultra-thin carbon-coated aluminum foil according to claim 4, characterized in that: The offset factor is calculated as follows: The spacing factor is calculated based on the spacing data, the angle factor is calculated based on the angle data, and the offset factor is obtained by multiplying the spacing factor and the angle factor. ； in, Indicates the offset factor. Represents the spacing factor. Represents the angle factor.

6. The method for quality inspection and analysis of ultra-thin carbon-coated aluminum foil according to claim 5, characterized in that: The spacing factor is calculated as follows: In a time series, the difference between the spacing data value at the next moment and the spacing data value at the previous moment is calculated, and then the difference is summed to obtain the spacing factor.

7. The method for quality inspection and analysis of ultra-thin carbon-coated aluminum foil according to claim 6, characterized in that: The angle factor is calculated as follows: In a time series, the angle data value at the next moment is successively calculated by subtracting the angle data value at the previous moment, and then the results are summed to obtain the angle factor.

8. The method for quality inspection and analysis of ultra-thin carbon-coated aluminum foil according to claim 7, characterized in that: The specific method for obtaining the abnormal offset coefficient is as follows: Set a normal vibration factor and a normal offset factor. Compare the vibration factor with the normal vibration factor. If the vibration factor is greater than the normal vibration factor, assign a value of 1 to the vibration factor; otherwise, do not assign a value. Compare the offset factor with the normal offset factor. If the offset factor is greater than the normal offset factor, assign a value of 1 to the offset factor; otherwise, do not assign a value. Add the vibration factor and the offset factor together to calculate the abnormal offset coefficient.

9. The method for quality inspection and analysis of ultra-thin carbon-coated aluminum foil according to claim 8, characterized in that: The method for analyzing whether an abnormal offset has occurred based on the aforementioned abnormal offset coefficient is as follows: Compare the abnormal offset coefficient with 2. If the abnormal offset coefficient is equal to 2, it indicates that an abnormal offset has occurred. If the abnormal offset coefficient is not equal to 2, it indicates that it is normal.

10. The method for quality detection and analysis of ultra-thin carbon-coated aluminum foil according to claim 9, characterized in that: The method for further analyzing volatility using the abnormal offset coefficient is as follows: In the time series, the abnormal offset coefficient is calculated using the variance method to obtain the abnormal offset fluctuation value. The average of the historical abnormal offset fluctuation values ​​is calculated to obtain the abnormal offset fluctuation threshold. The abnormal offset fluctuation value is compared with the abnormal offset fluctuation threshold. If the abnormal offset fluctuation value is greater than the abnormal offset fluctuation threshold, it indicates fluctuation; if the abnormal offset fluctuation value is less than or equal to the abnormal offset fluctuation threshold, it indicates no fluctuation.

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