A method and system for evaluating the quality of a stainless steel surface coating process

Abstract

This invention discloses a method and system for evaluating the quality of stainless steel surface coating processes, relating to the field of coating process evaluation technology. This invention acquires physical performance data, including comprehensive hardness values, comprehensive adhesion values, comprehensive thickness values, and microscopic image data. During the acquisition of physical performance data, a pre-built acquisition and calculation logic is used for standardized operations. Based on the physical performance data and microscopic image data, physical evaluation values ​​and micro-evaluation values ​​are calculated respectively. This comprehensively considers the physical properties and microstructure of the parts, thus significantly improving the accuracy and reference value of the evaluation.

Description

Technical Field

[0001] This invention relates to the field of coating process evaluation technology, specifically to a method and system for evaluating the quality of stainless steel surface coating processes. Background Technology

[0002] Stainless steel, with its excellent corrosion resistance and mechanical properties, is widely used in high-end equipment manufacturing, marine engineering, automotive parts, aerospace and other industrial fields. To further improve the surface wear resistance, weather resistance and protective performance of stainless steel parts, organic or inorganic coatings are usually prepared on their surface. The quality of the coating process directly determines the service life, reliability and appearance of the parts. Therefore, coating quality assessment is an indispensable quality control link in the production process.

[0003] Currently, the following technical deficiencies exist in the quality assessment of stainless steel surface coatings: The existing evaluation system often uses independent testing methods for single performance characteristics. The lack of unified standardized data collection and calculation logic in each testing link leads to large dispersion of measurement results due to differences in operator habits and test point selection. This makes it difficult to form comprehensive quantitative evaluation indicators that can be compared horizontally, resulting in insufficient objectivity and consistency of the evaluation results. At the same time, the existing evaluation system generally focuses on the qualification of macroscopic physical performance and does not include defects such as micropores and microcracks inside the coating layer in the systematic quantitative evaluation. Some parts that meet the macroscopic performance standards but exceed the microscopic defect standards are prone to early failure problems such as coating peeling and corrosion penetration during service, resulting in insufficient comprehensiveness and predictability of quality evaluation. Furthermore, when the coating quality is deemed unqualified, the existing production model relies heavily on the manual experience of technicians to troubleshoot process abnormalities. It lacks an intelligent cause tracing mechanism based on historical data, resulting in long problem location cycles and low troubleshooting efficiency, making it difficult to adapt to the quality control needs of large-scale, high-speed production lines.

[0004] To address this, a method and system for evaluating the quality of stainless steel surface coating processes are proposed. Summary of the Invention

[0005] The purpose of this invention is to address the problems mentioned in the background art by proposing a method and system for evaluating the quality of stainless steel surface coating processes.

[0006] The objective of this invention can be achieved through the following technical solution: a method for evaluating the quality of stainless steel surface coating processes, comprising: Step 1: Use pre-built acquisition and calculation logic to obtain the physical property data of the stainless steel parts to be evaluated; the physical property data includes the comprehensive values ​​of hardness, adhesion, and thickness of the coating layer of the parts; Step 2: Collect microscopic image data of the coating layer of the stainless steel part to be evaluated; Step 3: Analyze and process the physical property data and microscopic image data of the stainless steel parts to be evaluated, so as to determine the physical and microscopic evaluation values ​​of the stainless steel parts to be evaluated. Step 4: Preset the passing thresholds corresponding to the physical evaluation value and micro evaluation value respectively. Compare the physical evaluation value and micro evaluation value with the corresponding preset passing thresholds. If any set of values ​​of the physical evaluation value and micro evaluation value is higher than the corresponding preset passing threshold, a corresponding non-compliance label is generated. The non-compliance label includes physical non-compliance label and micro non-compliance label.

[0007] In a preferred embodiment of the present invention, the comprehensive hardness value of the stainless steel part to be evaluated is obtained using a pre-built acquisition and calculation logic, specifically as follows: The stainless steel part to be evaluated is fixed on the test platform, and each test area is pre-marked. The indenter of the nanoindenter is aligned with the pre-marked test area, and force is applied at the set loading rate to gradually press the indenter into the coating layer of each test area. During the pressing process, the force and displacement data of each test area are recorded in real time. After loading to the set depth, the pressure is maintained for a set time, and then unloaded at the same rate. The force-displacement curve of each test area is recorded throughout the process. Based on the force-displacement curves of each test area, using the formula... Calculate the hardness H of each test area of ​​the stainless steel part to be evaluated; where The maximum value of the internal force in the force-displacement curve of each test area; A represents the projected area of ​​the indentation in each test area, expressed by the formula... The calculation shows that, among which For contact depth, Let S be the initial slope of the unloading curve, where S is the depth. This is the indenter shape factor; The average value of the calculated hardness H for each group of test areas is used to obtain the comprehensive hardness value in the physical property data of the stainless steel parts to be evaluated.

[0008] In a preferred embodiment of the present invention, the comprehensive adhesion value of the stainless steel part to be evaluated is obtained by utilizing a pre-built acquisition and calculation logic, specifically as follows: The grid spacing of the coating layer on the stainless steel part to be evaluated is preset. Two mutually perpendicular parallel lines are drawn on the surface of the coating layer with a set pressure and speed to form a grid area, which serves as the bonding area for the coating layer on the stainless steel part to be evaluated. The selected tape is pasted on the pasting area; after pressing evenly with a roller, the tape is torn off with a robotic arm at a set swing speed. After tearing, the area of ​​the coating layer that has peeled off in the pasting area is obtained, and the proportion of the peeled area in the pasting area is calculated as the peeling ratio; the above is repeated to form X grid areas as pasting areas, and the peeling ratio is calculated; where X>3. The average value of the peeling ratio calculated for each adhesive area is used to obtain the comprehensive adhesion value in the physical property data of the stainless steel part to be evaluated.

[0009] In a preferred embodiment of the present invention, the comprehensive thickness value of the stainless steel part to be evaluated is obtained using a pre-built acquisition and calculation logic, specifically as follows: The ultrasonic wave is emitted from the probe, enters the coating layer, and the reflected wave is received. The propagation time and speed of the ultrasonic wave in the coating layer are obtained. The thickness of the coating layer is calculated by multiplying the propagation time and speed by an integer 2. The thickness of the coating layer was measured repeatedly for Y times. After removing the highest and lowest thickness values, the average value of the remaining thickness values ​​was taken to obtain the comprehensive thickness value in the physical property data of the stainless steel part to be evaluated.

[0010] In a preferred embodiment of the present invention, the physical property data of the stainless steel parts to be evaluated are analyzed and processed using pre-built computational logic, specifically as follows: Extract the comprehensive values ​​of hardness, adhesion, and thickness of the coating layer on the part, and set the target values ​​and allowable deviations for the comprehensive values ​​of hardness, adhesion, and thickness respectively; The absolute values ​​of the differences between the overall hardness value, overall adhesion value, and overall thickness value and the corresponding target values ​​are calculated to obtain the hardness deviation value, adhesion deviation value, and thickness deviation value of the stainless steel part to be evaluated. The ratios of the hardness deviation, adhesion deviation, and thickness deviation values ​​to their respective allowable deviations are calculated. Each calculated ratio is multiplied by its corresponding weighting coefficient, and then summed to obtain the physical evaluation value of the stainless steel part to be evaluated.

[0011] In a preferred embodiment of the present invention, microscopic image data of the stainless steel part to be evaluated is analyzed and processed using pre-built computational logic, specifically as follows: The defect region in the microscopic image data is separated from the background region, and the defect region is classified into porosity defects and crack defects. After classification, the number of pixels of porosity defects and crack defects is counted, and the actual area is converted based on the image resolution to obtain the porosity area and crack area. Determine the set of evaluation factors and rating scale ,in These represent the pore area and crack area, respectively, with V representing the pore area. The corresponding set of evaluation levels, middle , or Pore ​​area The corresponding ranges of pore area values, where s represents the total number of pore area ranges; F represents the crack area. The corresponding set of evaluation levels, middle , or Crack area The corresponding range of crack area values ​​for each group, where p represents the total number of crack area value ranges; Evaluation factors and Each of these is matched with the corresponding evaluation level set, and the range of pore area and crack area values ​​in each group of evaluation level sets V and F are set to correspond to a pore defect quantification value and a crack defect quantification value, respectively. This allows us to determine the quantification values ​​of pore defects and crack defects in the corresponding microscopic image data of the stainless steel part to be evaluated. The quantification values ​​of pore defects and crack defects are then multiplied by the set weighting coefficients and summed to obtain the micro-evaluation value of the stainless steel part to be evaluated.

[0012] As a preferred embodiment of the present invention, it further includes: Step 5: When a stainless steel part generates either a physical non-conforming label or a microscopic non-conforming label, perform the corresponding steps to determine the estimated cause of the non-conforming label error.

[0013] In a preferred embodiment of the present invention, the step of determining the cause of the predicted error in the appearance of the non-conforming label specifically includes: M1: If the identified non-conforming label is a physical non-conforming label, extract the corresponding historical cases from the pre-built historical database and assign a unique label to each historical case. The label includes the part production number, production time, cause of non-conformity, and physical performance data. Extract the comprehensive hardness value, comprehensive adhesion value, and comprehensive thickness value corresponding to the part coating layer and label them as k1, k2, and k3. At the same time, extract the corresponding comprehensive hardness value, comprehensive adhesion value, and comprehensive thickness value from the physical performance data of each historical case and label them as t1, t2, and t3. Using formula Calculate the similarity index M between each group of historical cases and the current physical non-conformance label; sort each group of historical cases in ascending order of similarity index, select the top three groups of historical cases from the left after sorting, and extract the reasons for non-conformance from their labels as the predicted error reasons for the appearance of physical non-conformance labels. M2: If the identified non-conforming label is a micro-non-conforming label, then similarly to step M1, extract the quantified values ​​of porosity defects and crack defects corresponding to the part and mark them as g1 and g2. At the same time, extract the corresponding quantified values ​​of porosity defects and crack defects from each group of historical cases and record them as e1 and e2. Using formula Calculate the similarity index W between each group of historical cases and the current micro-nonconformity label; sort each group of historical cases in ascending order of similarity index, select the top three groups of historical cases from the left after sorting, and extract the causes of nonconformity from their labels as the predicted error causes of micro-nonconformity labels. A system for evaluating the quality of stainless steel surface coating processes, comprising: The data transmission module is used to transmit physical property data and microscopic image data of the stainless steel parts to be evaluated in real time. The data analysis module is used to receive physical property data and microscopic image data of the stainless steel parts to be evaluated, and to analyze and process them to determine the physical and microscopic evaluation values ​​of the stainless steel parts to be evaluated. The label generation module is used to preset the passing thresholds corresponding to the physical evaluation value and the micro evaluation value. The physical evaluation value and the micro evaluation value are compared with the corresponding preset passing thresholds. If either the physical evaluation value or the micro evaluation value is higher than the corresponding preset passing threshold, the corresponding non-compliance label is generated. The non-compliance label includes physical non-compliance label and micro non-compliance label. The cause tracing module is used to determine the estimated cause of the non-conforming label when a stainless steel part generates either a physical non-conforming label or a microscopic non-conforming label.

[0014] Compared with the prior art, the beneficial effects of the present invention are: This invention acquires physical performance data, including comprehensive hardness values, comprehensive adhesion values, comprehensive thickness values, and microscopic image data. During the acquisition of physical performance data, a pre-built acquisition and calculation logic is used for standardized operations. Based on the physical performance data and microscopic image data, physical evaluation values ​​and micro-evaluation values ​​are calculated respectively. This comprehensively considers the physical performance and microstructure of the parts, thus comprehensively improving the accuracy and reference value of the evaluation. This invention constructs a historical database. When a non-conforming label, i.e. a physical or microscopic non-conforming label, is generated, similar historical cases are extracted from the database. By calculating the similarity index, the historical cases are sorted according to the similarity index. The top three groups of cases are selected to extract the causes of non-conformity as the estimated causes of error. This can quickly provide possible causes and save investigation time. When troubleshooting by predicting the cause of the error, if the troubleshooting fails, the new cause of non-compliance will be updated to the database to continuously improve the traceability mechanism and improve the efficiency and accuracy of problem troubleshooting. Attached Figure Description

[0015] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0016] Figure 1 This is a flowchart of the present invention; Figure 2 This is a schematic diagram of the principle of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. 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.

[0018] Example 1 Please see Figure 1 As shown, a method for evaluating the quality of stainless steel surface coating processes includes: Step 1: Use pre-built acquisition and calculation logic to obtain the physical property data of the stainless steel parts to be evaluated; the physical property data includes the comprehensive values ​​of hardness, adhesion, and thickness of the coating layer of the parts; Equipped with hardness testers, adhesion testers, and coating thickness gauges, these devices measure the hardness, adhesion, and thickness of the coating layer. The hardness tester uses dynamic or static measurement methods to ensure accurate acquisition of coating hardness. The adhesion tester quantifies the bonding force between the coating and the substrate through methods such as cross-cut tests and tensile tests. The coating thickness gauge uses ultrasonic and magnetic principles to accurately measure the coating thickness. Specifically: S1: Fix the stainless steel part to be evaluated on the test platform, ensuring the surface is flat; pre-mark each group of test areas, align the indenter of the nanoindenter (using the Berkovich indenter) with the pre-marked test area, apply force at the set loading rate, and gradually press the indenter into the coating layer of each group of test areas; during the pressing process, record the force and displacement data of each group of test areas in real time, after loading to the set depth, hold for the set time, and then unload at the same rate, recording the force-displacement curve of each group of test areas throughout the process; It should be noted that the loading rate range is 0.05-10 mN / s, and the set time range is 5-30 seconds; Based on the force-displacement curves of each test area, using the formula... Calculate the hardness H of each test area of ​​the stainless steel part to be evaluated; where The maximum value of the internal force in the force-displacement curve of each test area; A represents the projected area of ​​the indentation in each test area, expressed by the formula... The calculation shows that, among which For contact depth, The depth is set, and S is the initial slope of the unloading curve, which is the contact stiffness. This can be obtained by linear fitting the initial part of the unloading curve. This is the indenter shape factor, which is 0.75 for the Berkovich indenter; The average value of the calculated hardness H for each group of test areas is used to obtain the comprehensive hardness value in the physical property data of the stainless steel parts to be evaluated. Using a nanoindenter and a Berkovich indenter for measurement, the loading rate, depth, and holding time during the indentation process can be precisely controlled, and force and displacement data can be recorded in real time to obtain accurate force-displacement curves. This high-precision measurement method helps to accurately assess the hardness of the coating layer and can capture the changes in the mechanical properties of the coating layer's microstructure, which is especially important for materials such as stainless steel parts that have high requirements for surface quality. Each test area is pre-marked and its hardness is measured and calculated separately. Finally, the average value is taken to obtain the comprehensive hardness value. This method takes into account the performance differences of different areas on the surface of the part and can more comprehensively reflect the overall hardness characteristics of the coating layer on the surface of the stainless steel part. Compared with the measurement of a single area, it reduces the measurement error caused by local differences and improves the reliability and representativeness of the evaluation results. S2: Based on the type of coating layer of the stainless steel part to be evaluated, preset the grid spacing of the coating layer of the stainless steel part to be evaluated; for example, 1mm, 2mm or 3mm; use a grid cutter to draw two mutually perpendicular parallel lines on the surface of the coating layer with the set pressure and speed to form a grid area and serve as the bonding area of ​​the coating layer of the stainless steel part to be evaluated. The selected tape is pasted on the pasting area; specifically, 3M tape is used; after pressing evenly with a roller, the tape is torn off with a robotic arm at a set swing speed. After tearing, the area of ​​the coating layer that has peeled off in the pasting area is obtained, and the proportion of the peeled area in the pasting area is calculated as the peeling ratio; the above is repeated to form X grid areas as pasting areas, and the peeling ratio is calculated; where X>3. The average value of the peeling ratio calculated for each adhesive area is used to obtain the comprehensive adhesion value in the physical property data of the stainless steel part to be evaluated. By setting the grid spacing, using specific tapes, and specifying the tearing operation, the adhesion of the coating layer can be quantified more accurately. The peeling ratio can be calculated and the average value can be taken to obtain the comprehensive adhesion value, making the evaluation results more objective and comparable, and avoiding the subjective judgment differences that may exist in the prior art. Standardized operation: The operation details, such as the use parameters of the cross-cutting knife (pressure and speed), the type of tape (3M tape), the pressing method (even pressing with a roller), and the tearing method (tearing with a robotic arm at a set swing speed), are specified in detail to ensure the standardization and repeatability of the evaluation process. S3: The ultrasonic probe emits an ultrasonic wave, which enters the coating layer and receives the reflected wave. The time and speed of the ultrasonic wave in the coating layer are obtained. The thickness of the coating layer is calculated by multiplying the propagation time and propagation speed and then dividing by an integer 2. The ultrasonic thickness gauge uses the principle that ultrasonic waves will be reflected when they encounter an interface in different media to measure the thickness of the coating. The thickness of the coating layer was measured repeatedly Y times. After removing the highest and lowest thickness values, the average of the remaining thickness values ​​was taken to obtain the comprehensive thickness value in the physical property data of the stainless steel part to be evaluated. The thickness of the coating layer was measured repeatedly Y times, specifically at different locations within the coating layer; Step 2: Use a scanning electron microscope (SEM) to acquire microscopic image data of the coating layer of the stainless steel part to be evaluated; The microstructure of the coating layer is analyzed using scanning electron microscopy (SEM). SEM can clearly present the micro morphology of the coating and detect internal pores, cracks and other micro-defects. By default, the micro image data is preprocessed after acquisition, including noise reduction and enhancement. Step 3: Analyze and process the physical property data and microscopic image data of the stainless steel parts to be evaluated, so as to determine the physical and microscopic evaluation values ​​of the stainless steel parts to be evaluated. Specifically: Threshold segmentation is used to separate the defect region from the background region in the microscopic image data. The defect region is classified into pore defects and crack defects. Classification is based on morphological features or grayscale features. After classification, the number of pixels of pore defects and crack defects is counted, and the actual area is converted based on the image resolution to obtain the pore area and crack area. Determine the set of evaluation factors and rating scale ,in These represent the pore area and crack area, respectively, with V representing the pore area. The corresponding set of evaluation levels, middle , or Pore ​​area The corresponding ranges of pore area values, where s represents the total number of pore area ranges; F represents the crack area. The corresponding set of evaluation levels, middle , or Crack area The corresponding range of crack area values ​​for each group, where p represents the total number of crack area value ranges; Evaluation factors and Each pore area and crack area within the evaluation level sets V and F are matched with the corresponding evaluation level sets. The pore area and crack area ranges within each evaluation level set V and F are set to correspond to a pore defect quantification value and a crack defect quantification value, respectively. The pore defect quantification value and crack defect quantification value ranges from 10 to 30, which are set by technical personnel. The larger the pore area and crack area, the higher the quantification value obtained by the matching. This allows us to determine the quantified values ​​of porosity defects and crack defects in the microscopic image data of the stainless steel part to be evaluated. The quantified values ​​of porosity defects and crack defects are then multiplied by the set weighting coefficients and summed to obtain the micro-evaluation value of the stainless steel part to be evaluated. The evaluation factor set and evaluation level set are determined, and the pore area and crack area are matched with the corresponding evaluation level set to obtain the quantitative value of pore defects and the quantitative value of crack defects. Finally, the micro-evaluation value is obtained by weighted summation. This method comprehensively considers the influence of different types of defects and evaluates the micro-quality of the part from multiple dimensions. Extract the comprehensive values ​​of hardness, adhesion, and thickness of the coating layer of the part. Based on the current quality requirements for the production of stainless steel parts, set the target values ​​and allowable deviations for the comprehensive values ​​of hardness, adhesion, and thickness respectively. For example, the target value for thickness needs to be set according to the load-bearing requirements of the part, the service environment (such as stress distribution), and the goal of lightweighting. The absolute values ​​of the differences between the overall hardness value, overall adhesion value, and overall thickness value and the corresponding target values ​​are calculated to obtain the hardness deviation value, adhesion deviation value, and thickness deviation value of the stainless steel part to be evaluated. The ratios of the hardness deviation, adhesion deviation, and thickness deviation values ​​to their corresponding allowable deviations are calculated. Specifically, the hardness deviation, adhesion deviation, and thickness deviation values ​​are used as the numerators, and the corresponding allowable deviations are used as the denominators for the ratio calculation. Each calculated ratio is then multiplied by its corresponding weighting coefficient, and the results are summed to obtain the physical evaluation value of the stainless steel part to be evaluated. The target values ​​and allowable deviations are set based on the current quality requirements for the production of stainless steel parts, taking into full account the actual usage needs of the parts; Step 4: Based on the quality standard requirements of the current batch of stainless steel parts, preset the passing thresholds corresponding to the physical assessment value and the micro assessment value respectively. Compare the physical assessment value and the micro assessment value with the corresponding preset passing thresholds. If either the physical assessment value or the micro assessment value is higher than the corresponding preset passing threshold, a corresponding non-conforming label is generated. The non-conforming label includes physical non-conforming label and micro non-conforming label. Step 5: When a stainless steel part generates either a physical non-conformance label or a microscopic non-conformance label, perform the corresponding steps to determine the cause of the predicted error in the occurrence of the non-conformance label. Specifically: M1: If the identified non-conforming label is a physical non-conforming label, extract the corresponding historical cases from the pre-built historical database and assign a unique label to each historical case. The label includes the part production number, production time, cause of non-conformity, and physical performance data. Extract the comprehensive hardness value, comprehensive adhesion value, and comprehensive thickness value corresponding to the part coating layer and label them as k1, k2, and k3. At the same time, extract the corresponding comprehensive hardness value, comprehensive adhesion value, and comprehensive thickness value from the physical performance data of each historical case and label them as t1, t2, and t3. Using formula Calculate the similarity index M between each group of historical cases and the current physical non-conformance label; sort each group of historical cases in ascending order of similarity index, select the top three groups of historical cases from the left after sorting, and extract the reasons for non-conformance from their labels as the predicted error reasons for the appearance of physical non-conformance labels. M2: If the identified non-conforming label is a micro-non-conforming label, extract the corresponding historical cases from the pre-built historical database and assign a unique label to each historical case. The label includes the part production number, production time, cause of non-conformity, quantified value of porosity defect, and quantified value of crack defect. Extract the quantified values ​​of porosity defect and crack defect corresponding to the part and mark them as g1 and g2. At the same time, extract the corresponding quantified values ​​of porosity defect and crack defect from each historical case and mark them as e1 and e2. Using formula Calculate the similarity index W between each group of historical cases and the current micro-nonconformity label; sort each group of historical cases in ascending order of similarity index, select the top three groups of historical cases from the left after sorting, and extract the causes of nonconformity from their labels as the predicted error causes of micro-nonconformity labels. By utilizing a pre-built historical database, historical cases of similar non-conforming labels can be directly extracted, avoiding the need to start analyzing from scratch every time a problem occurs. This significantly shortens the time for locating quality problems, making it particularly suitable for rapid quality response in assembly line production. If technicians fail to troubleshoot based on the estimated cause of the error, they can conduct a further investigation, using the identified non-conforming cause as new data and updating the historical database. Example

[0019] Please see Figure 2 As shown, based on the method for evaluating the quality of stainless steel surface coating process provided in Embodiment 1 of this application, Embodiment 2 of this application proposes a system for evaluating the quality of stainless steel surface coating process. Embodiment 2 is merely a preferred embodiment of Embodiment 1, and the implementation of Embodiment 2 will not affect the individual implementation of Embodiment 1.

[0020] Specifically, the difference in the stainless steel surface coating process quality evaluation system provided in Embodiment 2 of this application lies in that it includes: The data transmission module is used to transmit physical property data and microscopic image data of the stainless steel parts to be evaluated in real time. The data analysis module is used to receive physical property data and microscopic image data of the stainless steel parts to be evaluated, and to analyze and process them to determine the physical and microscopic evaluation values ​​of the stainless steel parts to be evaluated. The label generation module is used to preset the passing thresholds corresponding to the physical evaluation value and the micro evaluation value. The physical evaluation value and the micro evaluation value are compared with the corresponding preset passing thresholds. If either the physical evaluation value or the micro evaluation value is higher than the corresponding preset passing threshold, the corresponding non-compliance label is generated. The non-compliance label includes physical non-compliance label and micro non-compliance label. The cause tracing module is used to determine the estimated cause of the non-conforming label when a stainless steel part generates either a physical non-conforming label or a microscopic non-conforming label.

[0021] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for evaluating the quality of stainless steel surface coating processes, characterized in that, include: Step 1: Use pre-built acquisition and calculation logic to obtain the physical property data of the stainless steel parts to be evaluated; The physical performance data includes the overall hardness, overall adhesion, and overall thickness of the coating layer on the part. Step 2: Collect microscopic image data of the coating layer of the stainless steel part to be evaluated; Step 3: Analyze and process the physical property data and microscopic image data of the stainless steel parts to be evaluated, so as to determine the physical and microscopic evaluation values ​​of the stainless steel parts to be evaluated. Step 4: Preset the passing thresholds corresponding to the physical evaluation value and micro evaluation value respectively. Compare the physical evaluation value and micro evaluation value with the corresponding preset passing thresholds. If any set of values ​​of the physical evaluation value and micro evaluation value is higher than the corresponding preset passing threshold, a corresponding non-compliance label is generated. The non-compliance label includes physical non-compliance label and micro non-compliance label.

2. The method for evaluating the quality of stainless steel surface coating process according to claim 1, characterized in that, The comprehensive hardness value of the stainless steel part to be evaluated is obtained using pre-built acquisition and calculation logic, specifically: The stainless steel part to be evaluated is fixed on the test platform, and each group of test areas is pre-marked. The indenter of the nanoindenter is aligned with the pre-marked test area, and force is applied at the set loading rate so that the indenter is gradually pressed into the coating layer of each group of test areas. During the pressing process, the force and displacement data of each test area are recorded in real time. After being loaded to the set depth, it is held for a set time and then unloaded at the same rate. The force-displacement curves of each test area are recorded throughout the process. Based on the force-displacement curves of each test area, using the formula... Calculate the hardness H of each test area of ​​the stainless steel part to be evaluated; where The maximum value of the internal force in the force-displacement curve of each test area; A represents the projected area of ​​the indentation in each test area, calculated using the formula... The calculation shows that, among which For contact depth, Let S be the initial slope of the unloading curve, where S is the depth. This is the indenter shape factor; The average value of the calculated hardness H for each test area is used to obtain the comprehensive hardness value in the physical property data of the stainless steel part to be evaluated.

3. The method for evaluating the quality of stainless steel surface coating process according to claim 2, characterized in that, The overall adhesion value of the stainless steel part to be evaluated is obtained using pre-built acquisition and calculation logic, specifically: The grid spacing of the coating layer on the stainless steel part to be evaluated is preset. Two mutually perpendicular parallel lines are drawn on the surface of the coating layer with a set pressure and speed to form a grid area, which serves as the bonding area for the coating layer on the stainless steel part to be evaluated. The selected tape is pasted on the pasting area; after pressing evenly with a roller, the tape is torn off with a robotic arm at a set swing speed. After tearing, the area of ​​the coating layer that has peeled off in the pasting area is obtained, and the proportion of the peeled area in the pasting area is calculated as the peeling ratio; the above is repeated to form X grid areas as pasting areas, and the peeling ratio is calculated; where X>3. The average value of the peeling ratio calculated for each adhesive area is used to obtain the comprehensive adhesion value in the physical property data of the stainless steel part to be evaluated.

4. The method for evaluating the quality of stainless steel surface coating process according to claim 3, characterized in that, The comprehensive thickness value of the stainless steel part to be evaluated is obtained using pre-built acquisition and calculation logic, specifically: The ultrasonic wave is emitted from the probe, enters the coating layer, and the reflected wave is received. The propagation time and speed of the ultrasonic wave in the coating layer are obtained. The thickness of the coating layer is calculated by multiplying the propagation time and speed by an integer 2. The thickness of the coating layer was measured repeatedly for Y times. After removing the highest and lowest thickness values, the average value of the remaining thickness values ​​was taken to obtain the comprehensive thickness value in the physical property data of the stainless steel part to be evaluated.

5. The method for evaluating the quality of stainless steel surface coating process according to claim 4, characterized in that, The physical property data of the stainless steel parts to be evaluated are analyzed and processed using pre-built computational logic, specifically as follows: Extract the comprehensive values ​​of hardness, adhesion, and thickness of the coating layer on the part, and set the target values ​​and allowable deviations for the comprehensive values ​​of hardness, adhesion, and thickness respectively; The absolute values ​​of the differences between the overall hardness value, overall adhesion value, and overall thickness value and the corresponding target values ​​are calculated to obtain the hardness deviation value, adhesion deviation value, and thickness deviation value of the stainless steel part to be evaluated. The ratios of the hardness deviation, adhesion deviation, and thickness deviation values ​​to their respective allowable deviations are calculated. Each calculated ratio is multiplied by its corresponding weighting coefficient, and then summed to obtain the physical evaluation value of the stainless steel part to be evaluated.

6. The method for evaluating the quality of stainless steel surface coating process according to claim 5, characterized in that, The microscopic image data of the stainless steel part to be evaluated is analyzed and processed using pre-built computational logic, specifically as follows: The defect region in the microscopic image data is separated from the background region, and the defect region is classified into porosity defects and crack defects. After classification, the number of pixels of porosity defects and crack defects is counted, and the actual area is converted based on the image resolution to obtain the porosity area and crack area. Determine the set of evaluation factors and rating scale ,in These represent the pore area and crack area, respectively, with V representing the pore area. The corresponding rating scale set, middle , or Pore ​​area The corresponding ranges of pore area values, where s represents the total number of pore area ranges; and F represents the crack area. The corresponding rating scale set, middle , or Crack area The corresponding range of crack area values ​​for each group, where p represents the total number of crack area value ranges; Evaluation factors and Each of these is matched with the corresponding evaluation level set, and the range of pore area and crack area values ​​in each group of evaluation level sets V and F are set to correspond to a pore defect quantification value and a crack defect quantification value, respectively. This allows us to determine the quantification values ​​of pore defects and crack defects in the corresponding microscopic image data of the stainless steel part to be evaluated. The quantification values ​​of pore defects and crack defects are then multiplied by the set weighting coefficients and summed to obtain the micro-evaluation value of the stainless steel part to be evaluated.

7. The method for evaluating the quality of stainless steel surface coating process according to claim 6, characterized in that, Also includes: Step 5: When a stainless steel part generates either a physical non-conformance label or a microscopic non-conformance label, perform the corresponding steps to determine the estimated cause of the non-conformance label error.

8. The method for evaluating the quality of stainless steel surface coating process according to claim 7, characterized in that, The steps to determine the cause of the predicted error in the non-compliant label are as follows: M1: If the identified non-conforming label is a physical non-conforming label, extract the corresponding historical cases from the pre-built historical database and assign a unique label to each historical case. The label includes the part production number, production time, cause of non-conformity, and physical performance data. Extract the comprehensive hardness value, comprehensive adhesion value, and comprehensive thickness value corresponding to the part coating layer and label them as k1, k2, and k3. At the same time, extract the corresponding comprehensive hardness value, comprehensive adhesion value, and comprehensive thickness value from the physical performance data of each historical case and label them as t1, t2, and t3. Using formula Calculate the similarity index M between each group of historical cases and the current physical non-compliance label; The historical cases were sorted from smallest to largest according to similarity index. The top three historical cases were selected from the left and the reasons for non-compliance were extracted from their labels as the predicted error reasons for the appearance of physical non-compliance labels. M2: If the identified non-conforming label is a micro-non-conforming label, then similarly to step M1, extract the quantified values ​​of porosity defects and crack defects corresponding to the part and mark them as g1 and g2. At the same time, extract the corresponding quantified values ​​of porosity defects and crack defects from each group of historical cases and record them as e1 and e2. Using formula Calculate the similarity index W between each group of historical cases and the current micro-nonconformity label; sort each group of historical cases in ascending order of similarity index, select the top three groups of historical cases from the left, and extract the causes of nonconformity from their labels as the predicted error causes of micro-nonconformity labels.

9. A system for evaluating the quality of stainless steel surface coating processes, applied to the method for evaluating the quality of stainless steel surface coating processes according to any one of claims 1-8, characterized in that, include: The data transmission module is used to transmit physical property data and microscopic image data of the stainless steel parts to be evaluated in real time. The data analysis module is used to receive physical property data and microscopic image data of the stainless steel parts to be evaluated, and to analyze and process them to determine the physical and microscopic evaluation values ​​of the stainless steel parts to be evaluated. The label generation module is used to preset the passing thresholds corresponding to the physical evaluation value and the micro evaluation value. The physical evaluation value and the micro evaluation value are compared with the corresponding preset passing thresholds. If either the physical evaluation value or the micro evaluation value is higher than the corresponding preset passing threshold, the corresponding non-compliance label is generated. The non-compliance label includes physical non-compliance label and micro non-compliance label. The cause tracing module is used to determine the estimated cause of the non-conforming label when a stainless steel part generates either a physical non-conforming label or a microscopic non-conforming label.

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