A method and device for quickly screening and generating a standard resistance curve of resistance spot welding

Abstract

This invention provides a method and apparatus for rapidly generating standard resistance curves for resistance spot welding, relating to the field of resistance spot welding of steel plates. The method includes the following steps: acquiring a set of resistance data during the resistance spot welding process; constructing an original set of resistance curves based on the resistance data set; filtering the original set of resistance curves for effective resistance curves to obtain a set of effective resistance curves; calculating and filtering the effective resistance curves in the set of effective resistance curves to obtain a first candidate set of resistance curves; calculating and filtering the effective resistance curves in the first candidate set of resistance curves for Pearson coefficients to obtain a second candidate set of resistance curves; and constructing standard resistance curves based on the effective resistance curves in the second candidate set of resistance curves using a median statistical algorithm.

Description

Technical Field

[0001] This invention relates to the field of resistance spot welding of steel plates, and in particular to a method and apparatus for rapidly generating standard resistance curves for resistance spot welding. Background Technology

[0002] With the automotive industry's trend towards lightweighting and high strength, resistance spot welding, as a highly efficient and reliable joining technology, is widely used in the assembly of body structural components made of high-strength steel and ultra-high-strength steel. In the field of resistance spot welding quality control, dynamic resistance curves, because they can reflect the physical changes during weld formation in real time, are gradually becoming an important basis for evaluating the stability of the welding process and the quality of the weld. Generating a representative "standard resistance curve" from a large amount of welding data is of great significance for achieving online evaluation and intelligent control of welding quality.

[0003] Currently, common methods for generating standard curves in the industry mainly rely on manual or semi-automatic data collection and curve selection. For example, some methods involve conducting numerous welding experiments, combined with destructive or non-destructive testing methods, manually verifying the weld quality to select "high-quality curves," and then constructing a standard curve using mathematical fitting. This approach is not only costly and time-consuming in acquiring data, but also relies on specialized equipment and human experience, making it difficult to meet the demands for efficiency and real-time performance in modern manufacturing. Furthermore, due to the limited initial sample size, the resulting standard curves suffer from significant deficiencies in representativeness and adaptability.

[0004] On the other hand, traditional methods often use fixed-point features (such as resistance extremes, curve inflection points, etc.) to construct fitting models. When faced with drastic curve fluctuations and complex interference signals in actual production, these methods are prone to problems such as fitting distortion and weak noise resistance. In addition, the selection process of standard curves often lacks a systematic assessment of both trend and amplitude consistency, resulting in deviations between the final standard resistance curve and the actual working conditions, which affects the judgment effect of the welding monitoring system. Summary of the Invention

[0005] To address the above problems, this invention provides a method for rapidly selecting and generating standard resistance curves for resistance spot welding, comprising the following steps:

[0006] Obtain the resistance data set during the resistance spot welding process, construct the original resistance curve set based on the resistance data set, and filter the original resistance curve set to obtain the effective resistance curve set.

[0007] The Kendall coefficient of the effective resistance curves in the effective resistance curve set is calculated and filtered to obtain the first candidate resistance curve set.

[0008] The effective resistance curves in the first candidate resistance curve set are calculated and screened using the Pearson coefficient to obtain the second candidate resistance curve set.

[0009] Based on the effective resistance curves in the second candidate resistance curve set, a standard resistance curve is constructed using the median statistical algorithm.

[0010] Optionally, the step of constructing an original resistance curve set based on the resistance data set, and filtering the original resistance curve set for effective resistance curves to obtain an effective resistance curve set specifically includes:

[0011] S11: Construct multiple original resistance curves from each resistance data in the resistance data set in chronological order, and form an original resistance curve set from all the original resistance curves.

[0012] S12: Select the i-th original resistance curve from the set of original resistance curves, divide the original resistance curve into multiple sub-curves according to a preset time interval, and calculate the slope and differential slope of each sub-curve; if the product of the slope and differential slope of a sub-curve is greater than a preset value, then remove the i-th original resistance curve; otherwise, retain the i-th original resistance curve as a valid resistance curve.

[0013] S13: Repeat step S12 until all the original resistance curves have been traversed, and the set of effective resistance curves is formed by the retained effective resistance curves.

[0014] Optionally, the step of calculating and filtering the Kendall coefficients of the effective resistance curves in the effective resistance curve set to obtain the first candidate resistance curve set specifically includes:

[0015] Calculate the average Kendall coefficient between each effective resistance curve in the effective resistance curve set and the other effective resistance curves in turn. Take the effective resistance curve with the largest average Kendall coefficient as the first trend benchmark curve. Set the Kendall coefficient threshold according to the average Kendall coefficient of the first trend benchmark curve.

[0016] Calculate the Kendall coefficient between the first trend baseline curve and the remaining effective resistance curves in sequence. Retain the effective resistance curves whose Kendall coefficient with the first trend baseline curve is greater than the Kendall coefficient threshold. The retained effective resistance curves constitute the first candidate resistance curve set.

[0017] Optionally, the step of calculating and filtering the effective resistance curves in the first candidate resistance curve set to obtain the second candidate resistance curve set specifically includes:

[0018] The average Pearson coefficient between each effective resistance curve in the first candidate resistance curve set and the other effective resistance curves is calculated sequentially. The effective resistance curve with the largest average Pearson coefficient is taken as the second trend benchmark curve. The Pearson coefficient threshold is set according to the average Pearson coefficient of the second trend benchmark curve.

[0019] The Pearson coefficients between the second trend baseline curve and the remaining effective resistance curves are calculated sequentially. The effective resistance curves whose Pearson coefficients with the second trend baseline curve are greater than the Pearson coefficient threshold are retained, and the retained effective resistance curves constitute the second candidate resistance curve set.

[0020] Optionally, the step of constructing a standard resistance curve based on the effective resistance curves in the second candidate resistance curve set using a median statistical algorithm specifically includes:

[0021] S21: Filter each effective resistance curve in the second candidate resistance curve set to obtain each filtered effective resistance curve.

[0022] S22: Obtain the resistance data of each filtered effective resistance curve at the j-th time point, calculate the median resistance at the j-th time point based on the resistance data at the j-th time point, and use the median resistance at the j-th time point as the resistance data of the standard resistance curve at the j-th time point.

[0023] S23: Repeat step S22 until all time points are traversed. Based on the resistance data of the standard resistance curve at all time points, construct the standard resistance curve in chronological order.

[0024] Optional:

[0025] By repeatedly constructing standard resistance curves using a resistance dataset, a comprehensive score for each standard resistance curve is calculated. The Kendall coefficient threshold and Pearson coefficient threshold of the standard resistance curve corresponding to the lowest comprehensive score are taken as the optimal Kendall coefficient threshold and optimal Pearson coefficient threshold. Based on the optimal Kendall coefficient threshold and optimal Pearson coefficient threshold, the final standard resistance curve is constructed using the resistance dataset.

[0026] Optionally, the calculation process of the comprehensive score specifically includes:

[0027] Obtain the current number N of valid resistance curves in the set of second candidate resistance curves corresponding to the standard resistance curve, set the target number N0, and calculate the penalty function f(N) based on the current number N and the target number N0.

[0028] Calculate the average (avg) of the fitting error between the standard resistance curve and all valid resistance curves in the second candidate resistance curve set. mse ,Will As part of the overall score.

[0029] This invention also provides a device for rapidly selecting and generating standard resistance curves for resistance spot welding, used to implement the aforementioned method for rapidly selecting and generating standard resistance curves for resistance spot welding. The device includes:

[0030] The effective resistance curve set acquisition module is used to acquire the resistance data set during the resistance spot welding process, construct the original resistance curve set based on the resistance data set, and filter the original resistance curve set to obtain the effective resistance curve set.

[0031] The first candidate resistance curve set acquisition module is used to calculate and filter the Kendall coefficient of the effective resistance curves in the effective resistance curve set to obtain the first candidate resistance curve set.

[0032] The second candidate resistance curve set acquisition module is used to calculate and filter the effective resistance curves in the first candidate resistance curve set to obtain the second candidate resistance curve set.

[0033] The standard resistance curve construction module is used to construct a standard resistance curve based on the effective resistance curves in the second candidate resistance curve set using a median statistical algorithm.

[0034] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for rapidly generating standard resistance curves for resistance spot welding.

[0035] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method for rapidly generating standard resistance curves for resistance spot welding.

[0036] The present invention has the following beneficial effects:

[0037] 1. The original resistance curve set constructed from the resistance data set was screened based on slope and differential slope to remove abnormal resistance curves with abrupt slope changes, thus improving the stability of the effective resistance curves. A second screening was performed based on the Kendall coefficient of the effective resistance curves to ensure that the retained effective resistance curves maintain a high degree of consistency in their overall trend. A third screening was performed based on the Pearson coefficient of the effective resistance curves to improve the linear correlation between the effective resistance curves. Based on the finally retained effective resistance curves, a standard resistance curve was constructed using a median statistical algorithm. Through the above process, high-precision standard resistance curves can be automatically selected without manual screening, which has the advantages of high efficiency, automation, and adaptability, and is suitable for the rapid construction of resistance spot welding standard curves for various vehicle models and materials.

[0038] 2. Calculate the comprehensive score of multiple sets of Kendall coefficient thresholds and Pearson coefficient thresholds. Take the Kendall coefficient threshold and Pearson coefficient threshold of the standard resistance curve corresponding to the minimum comprehensive score as the optimal Kendall coefficient threshold and optimal Pearson coefficient threshold. Based on the optimal Kendall coefficient threshold and optimal Pearson coefficient threshold, construct the final standard resistance curve through the resistance data set. The comprehensive score realizes automatic optimization of multiple sets of Kendall coefficient thresholds and Pearson coefficient thresholds, eliminating the need for engineers to adjust parameters based on experience, thus improving the accuracy of the standard resistance curve. Attached Figure Description

[0039] Figure 1 This is a flowchart of a method according to an embodiment of the present invention;

[0040] Figure 2 This is a structural diagram of the device according to an embodiment of the present invention;

[0041] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0042] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0043] Reference Figure 1 This invention provides a method for rapidly selecting and generating standard resistance curves for resistance spot welding, comprising the following steps:

[0044] Obtain the resistance data set during the resistance spot welding process, construct the original resistance curve set based on the resistance data set, and filter the original resistance curve set to obtain the effective resistance curve set.

[0045] In some embodiments, the step of constructing an original resistance curve set based on a resistance data set, and then filtering the original resistance curve set for effective resistance curves to obtain an effective resistance curve set, specifically includes:

[0046] S11: Construct multiple original resistance curves from each resistance data in the resistance data set in chronological order, and form an original resistance curve set from all the original resistance curves.

[0047] In some embodiments, for example, if the power-on time is 240ms and a resistance value is extracted every 1ms, then the length of the original resistance curve is 240 data points.

[0048] S12: Select the i-th original resistance curve from the set of original resistance curves, divide the original resistance curve into multiple sub-curves according to a preset time interval, and calculate the slope and differential slope of each sub-curve; if the product of the slope and differential slope of a sub-curve is greater than a preset value, then remove the i-th original resistance curve; otherwise, retain the i-th original resistance curve as a valid resistance curve.

[0049] In some embodiments, the original resistance curve is smoothed, and four time points are selected to approximate the slope. Four points are selected because fewer time points cannot well reflect the trend of slope change, and data jitter has a large impact. More time points will hide some detailed data. The slope is calculated by difference because the slope fluctuation of some feature points with small slope fluctuation is not significantly different from the slope fluctuation of normal points. Therefore, the slope is calculated using the following formula.

[0050]

[0051] in, This represents the slope at time t. Data with a slope greater than or equal to 0 will be processed to be zero;

[0052] The product of the slope and the difference slope The expression is:

[0053]

[0054] in, The slope is differential. When the value of f(t) is greater than the preset value, it is determined to be an abnormal resistance curve with violent fluctuations or splashing characteristics and is removed. This method can effectively remove most of the original resistance curves with abrupt slope changes, thereby improving the stability and representativeness of subsequent standard curve screening.

[0055] S13: Repeat step S12 until all the original resistance curves have been traversed, and the set of effective resistance curves is formed by the retained effective resistance curves.

[0056] The Kendall coefficient of the effective resistance curves in the effective resistance curve set is calculated and filtered to obtain the first candidate resistance curve set.

[0057] In some embodiments, the calculation and screening of Kendall coefficients for the effective resistance curves in the effective resistance curve set to obtain a first candidate resistance curve set specifically includes:

[0058] Calculate the average value of Kendall's coefficients between each effective resistance curve in the set of effective resistance curves and the remaining effective resistance curves in sequence. Take the effective resistance curve with the largest average value of Kendall's coefficients as the first trend reference curve, and set the Kendall's coefficient threshold according to the average value of Kendall's coefficients of the first trend reference curve.

[0059] In some embodiments, perform time alignment processing on the effective resistance curves in the set of effective resistance curves, and calculate the Kendall's coefficients between each effective resistance curve and all the remaining effective resistance curves in sequence to quantify the consistency of each effective resistance curve in terms of the rising and falling trends. According to the calculated Kendall's coefficients, calculate the average value of Kendall's coefficients between each effective resistance curve and the remaining effective resistance curves.

[0060] Kendall's coefficient is a non-parametric statistical index based on the sorting relationship of data, mainly used to measure the correlation between two ordered variables or the consistency among evaluators. Suppose for curves x and y, x1 = 1, x2 = 2, y1 = 1, y2 = 2, then both curves are rising at this stage, so the trend consistency of the curves is judged. Calculating the Kendall's coefficient for the entire curve can reflect the trend consistency of the curve. The Kendall's coefficient has the following calculation formula:

[0061]

[0062] where C represents the number of pairs where (x i -x j ) and (y i -y<00​​​​​​​​​​​​​​​​​​​​​​​​​​In some embodiments, the average Kendall coefficient of the first trend baseline curve with two decimal places is used as the Kendall coefficient threshold. Assuming that the average Kendall coefficient of the first trend baseline curve is 0.857, the Kendall coefficient threshold is set to 0.85. Effective resistance curves with a Kendall coefficient greater than 0.85 of the first trend baseline curve are selected, and the selected effective resistance curves are used to construct a first candidate resistance curve set that maintains a high degree of consistency in the overall trend.

[0065] The effective resistance curves in the first candidate resistance curve set are calculated and screened using the Pearson coefficient to obtain the second candidate resistance curve set.

[0066] In some embodiments, the calculation and screening of Pearson coefficients on the effective resistance curves in the first candidate resistance curve set to obtain the second candidate resistance curve set specifically includes:

[0067] The average Pearson coefficient between each effective resistance curve in the first candidate resistance curve set and the other effective resistance curves is calculated sequentially. The effective resistance curve with the largest average Pearson coefficient is taken as the second trend benchmark curve. The Pearson coefficient threshold is set according to the average Pearson coefficient of the second trend benchmark curve.

[0068] In some embodiments, the effective resistance curves in the first candidate resistance curve set are time-aligned, and the Pearson coefficient between each effective resistance curve and all other effective resistance curves is calculated in turn to quantify their linear consistency in the resistance amplitude change process. Based on the calculated Pearson coefficient, the average Pearson coefficient between each effective resistance curve and the other effective resistance curves is calculated.

[0069] The Pearson correlation coefficient is a core statistic for measuring the linear correlation between two continuous variables. When two curves have the same trend, if there is a large difference in their amplitudes at a certain point, for example, for curves x and y, x1=1, y1=1.1, x2=1, y2=2, then at the first point the value of y is 1.1 times that of x, but at the second point the value of y is twice that of x. The linear consistency during the amplitude change process will decrease, and the value of the Pearson coefficient will decrease. By setting an appropriate threshold, effective resistance curves with poor linear correlation during the amplitude change process can be eliminated. The formula for calculating the Pearson coefficient r is as follows:

[0070]

[0071] Where, x i Let y represent the i-th value of curve x. i This represents the i-th value of the curve y. This represents the average value of curve x. This represents the average value of the curve y;

[0072] The Pearson coefficients between the second trend baseline curve and the remaining effective resistance curves are calculated sequentially. The effective resistance curves whose Pearson coefficients with the second trend baseline curve are greater than the Pearson coefficient threshold are retained, and the retained effective resistance curves constitute the second candidate resistance curve set.

[0073] In some embodiments, the average Pearson coefficient of the second trend baseline curve with three decimal places is used as the Pearson coefficient threshold. Assuming that the average Pearson coefficient of the second trend baseline curve is 0.9903, the Pearson coefficient threshold is set to 0.990. Then, effective resistance curves with a Pearson coefficient greater than 0.990 of the second trend baseline curve are selected, and the selected effective resistance curves constitute the second candidate resistance curve set.

[0074] Based on the effective resistance curves in the second candidate resistance curve set, a standard resistance curve is constructed using the median statistical algorithm.

[0075] In some embodiments, constructing a standard resistance curve based on the effective resistance curves in the second candidate resistance curve set using a median statistical algorithm specifically includes:

[0076] S21: Filter each effective resistance curve in the second candidate resistance curve set to obtain each filtered effective resistance curve.

[0077] In some embodiments, although the original resistance curve has undergone preliminary smoothing to improve the accuracy of slope and differential slope determination, this processing is geared towards feature extraction of a single curve and cannot directly replace the smoothness guarantee required for global fitting in generating the standard resistance curve. Therefore, smoothing and filtering processing needs to be performed before constructing the standard resistance curve. By smoothing and filtering the effective resistance curve, high-frequency disturbances during effective resistance curve fusion can be effectively reduced, and the median calculation process can be prevented from being affected by abnormal cusps.

[0078] Each effective resistance curve in the second candidate resistance curve set is selected, and a first-order polynomial Savitzky-Golay filter with a window length of 5 and an order of 1 is uniformly used to achieve signal smoothing and filtering, and eliminate high-frequency noise interference.

[0079] S22: Obtain the resistance data of each filtered effective resistance curve at the j-th time point, calculate the median resistance at the j-th time point based on the resistance data at the j-th time point, and use the median resistance at the j-th time point as the resistance data of the standard resistance curve at the j-th time point.

[0080] S23: Repeat step S22 until all time points are traversed. Based on the resistance data of the standard resistance curve at all time points, construct the standard resistance curve in chronological order.

[0081] In some embodiments, standard resistance curves are repeatedly constructed using a resistance dataset, and a comprehensive score for each standard resistance curve is calculated. The Kendall coefficient threshold and Pearson coefficient threshold of the standard resistance curve corresponding to the minimum comprehensive score are taken as the optimal Kendall coefficient threshold and optimal Pearson coefficient threshold. Based on the optimal Kendall coefficient threshold and optimal Pearson coefficient threshold, the final standard resistance curve is constructed using the resistance dataset.

[0082] In some embodiments, several combinations of Kendall coefficient and Pearson coefficient thresholds are set. For each combination, effective resistance curve screening, standard resistance curve generation, and fitting error evaluation are performed. By constructing a comprehensive scoring function that combines fitting error, the number of screened curves, and representativeness deviation, all combinations are evaluated and ranked. The threshold combination with the smallest score is selected. The smaller the score, the better the selection of Kendall coefficient and Pearson coefficient thresholds. Finally, the entire process is re-executed with this set of thresholds to output a final standard curve with high fitting accuracy and representativeness.

[0083] In some embodiments, the calculation process of the comprehensive score specifically includes:

[0084] Obtain the current number N of valid resistance curves in the set of second candidate resistance curves corresponding to the standard resistance curve, set the target number N0, and calculate the penalty function f(N) based on the current number N and the target number N0.

[0085] Calculate the average (avg) of the fitting error between the standard resistance curve and all valid resistance curves in the second candidate resistance curve set. mse ,Will As part of the overall score.

[0086] In some embodiments, a comprehensive score is constructed based on a Bayesian optimization algorithm, and the expression for the comprehensive score Score is as follows:

[0087]

[0088]

[0089] Here, f(N) is the penalty function for the number of deviations from the target curve, and |N-N0| represents the deviation between the current quantity N and the target quantity N0. The larger the deviation, the larger f(N), indicating a heavier penalty. Assuming the target quantity N0 is 100 and the current quantity N is 40, the penalty function f(N) has a value of 1.6. Similarly, when the target quantity N0 is 100 and the current quantity N is 160, the penalty function f(N) also has a value of 1.6. However, in small sample sizes, local optima in the standard resistance curve can easily occur, leading to curve distortion and insufficient representativeness. Therefore, a penalty function is added... To prevent the occurrence of extremely small samples, when the sample size is less than 100, small samples will be penalized again, so that the comprehensive score can more comprehensively control the balance between representativeness and quality.

[0090] refer to Figure 2 The present invention also provides a rapid screening and generation device 20 for standard resistance curves of resistance spot welding, used to implement the aforementioned rapid screening and generation method for standard resistance curves of resistance spot welding, the device comprising:

[0091] The effective resistance curve set acquisition module 21 is used to acquire the resistance data set during the resistance spot welding process, construct the original resistance curve set based on the resistance data set, and filter the effective resistance curves in the original resistance curve set to obtain the effective resistance curve set.

[0092] The first candidate resistance curve set acquisition module 22 is used to calculate and filter the Kendall coefficient of the effective resistance curves in the effective resistance curve set to obtain the first candidate resistance curve set.

[0093] The second candidate resistance curve set acquisition module 23 is used to calculate and filter the effective resistance curves in the first candidate resistance curve set to obtain the second candidate resistance curve set.

[0094] The standard resistance curve construction module 24 is used to construct a standard resistance curve based on the effective resistance curves in the second candidate resistance curve set using a median statistical algorithm.

[0095] This application provides an electronic device, including a processor and a memory; the memory stores a computer program, wherein the computer program, when executed by the processor, implements the method for rapidly generating standard resistance curves for resistance spot welding according to any of the above schemes.

[0096] Specifically, the processor may include, for example, a general-purpose microprocessor, an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor may also include onboard memory for caching purposes. The processor may be a single processing unit or multiple processing units for performing different actions of the method flow according to embodiments of this application.

[0097] Memory can be any medium capable of containing, storing, transmitting, propagating, or transmitting instructions. For example, memory can include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, instruments, or propagation media. Specific examples of memory include: magnetic storage devices such as magnetic tape or hard disk drives (HDDs); optical storage devices such as optical discs (CD-ROMs); and also random access memory (RAM) or flash memory; and / or wired / wireless communication links.

[0098] This application also provides a computer-readable medium storing a computer program that, when executed by a processor, implements the method for rapidly generating standard resistance curves for resistance spot welding according to any of the above-described schemes. This computer-readable medium may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into that device / apparatus / system. The aforementioned computer-readable medium carries one or more programs, which, when executed, implement the method as described in the embodiments of this application.

[0099] According to embodiments of this application, a computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, 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 device, magnetic storage device, or any suitable combination thereof. In this application, a 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, apparatus, or device. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wired, optical fiber, radio frequency signals, etc., or any suitable combination thereof.

[0100] Those skilled in the art will understand that the features described in the various embodiments and / or claims of this application can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this application. In particular, the features described in the various embodiments and / or claims of this application can be combined and / or combined in various ways without departing from the spirit and teachings of this application. All such combinations and / or combinations fall within the scope of this application. Therefore, the scope of this application should not be limited to the above embodiments, but should be defined not only by the appended claims, but also by their equivalents. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for rapidly selecting and generating standard resistance curves for resistance spot welding, characterized in that, Including the following steps: Obtain the resistance data set during the resistance spot welding process, construct the original resistance curve set based on the resistance data set, and filter the original resistance curve set to obtain the effective resistance curve set. The Kendall coefficient of the effective resistance curves in the effective resistance curve set is calculated and filtered to obtain the first candidate resistance curve set. The effective resistance curves in the first candidate resistance curve set are calculated and screened using the Pearson coefficient to obtain the second candidate resistance curve set. Based on the effective resistance curves in the second candidate resistance curve set, a standard resistance curve is constructed using the median statistical algorithm. By repeatedly constructing standard resistance curves using a resistance dataset, a comprehensive score for each standard resistance curve is calculated. The Kendall coefficient threshold and Pearson coefficient threshold of the standard resistance curve corresponding to the lowest comprehensive score are taken as the optimal Kendall coefficient threshold and optimal Pearson coefficient threshold. Based on the optimal Kendall coefficient threshold and optimal Pearson coefficient threshold, the final standard resistance curve is constructed using the resistance dataset.

2. The method for rapidly selecting and generating standard resistance curves for resistance spot welding according to claim 1, characterized in that, The process of constructing an original resistance curve set based on the resistance data set, and then filtering the original resistance curve set for effective resistance curves to obtain an effective resistance curve set, specifically includes: S11: Construct multiple original resistance curves from each resistance data in the resistance data set in chronological order, and form an original resistance curve set from all the original resistance curves. S12: Select the i-th original resistance curve from the set of original resistance curves, divide the original resistance curve into multiple sub-curves according to a preset time interval, and calculate the slope and differential slope of each sub-curve; if the product of the slope and differential slope of a sub-curve is greater than a preset value, then remove the i-th original resistance curve; otherwise, retain the i-th original resistance curve as a valid resistance curve. S13: Repeat step S12 until all the original resistance curves have been traversed, and the set of effective resistance curves is formed by the retained effective resistance curves.

3. The method for rapidly selecting and generating standard resistance curves for resistance spot welding according to claim 1, characterized in that, The calculation and screening of Kendall coefficients for effective resistance curves in the effective resistance curve set to obtain the first candidate resistance curve set specifically includes: The average Kendall coefficient between each effective resistance curve in the effective resistance curve set and the other effective resistance curves is calculated sequentially. The effective resistance curve with the largest average Kendall coefficient is taken as the first trend benchmark curve. The Kendall coefficient threshold is set according to the average Kendall coefficient of the first trend benchmark curve. Calculate the Kendall coefficient between the first trend baseline curve and the remaining effective resistance curves in sequence. Retain the effective resistance curves whose Kendall coefficient with the first trend baseline curve is greater than the Kendall coefficient threshold. The retained effective resistance curves constitute the first candidate resistance curve set.

4. The method for rapidly selecting and generating standard resistance curves for resistance spot welding according to claim 1, characterized in that, The step of calculating and filtering the effective resistance curves in the first candidate resistance curve set to obtain the second candidate resistance curve set specifically includes: The average Pearson coefficient between each effective resistance curve in the first candidate resistance curve set and the other effective resistance curves is calculated sequentially. The effective resistance curve with the largest average Pearson coefficient is taken as the second trend benchmark curve. The Pearson coefficient threshold is set according to the average Pearson coefficient of the second trend benchmark curve. The Pearson coefficients between the second trend baseline curve and the remaining effective resistance curves are calculated sequentially. The effective resistance curves whose Pearson coefficients with the second trend baseline curve are greater than the Pearson coefficient threshold are retained, and the retained effective resistance curves constitute the second candidate resistance curve set.

5. The method for rapidly selecting and generating standard resistance curves for resistance spot welding according to claim 1, characterized in that, The step of constructing a standard resistance curve based on the effective resistance curves in the second candidate resistance curve set using a median statistical algorithm specifically includes: S21: Filter each effective resistance curve in the second candidate resistance curve set to obtain each filtered effective resistance curve. S22: Obtain the resistance data of each filtered effective resistance curve at the j-th time point, calculate the median resistance at the j-th time point based on the resistance data at the j-th time point, and use the median resistance at the j-th time point as the resistance data of the standard resistance curve at the j-th time point. S23: Repeat step S22 until all time points are traversed. Based on the resistance data of the standard resistance curve at all time points, construct the standard resistance curve in chronological order.

6. The method for rapidly selecting and generating standard resistance curves for resistance spot welding according to claim 1, characterized in that, The calculation process for the comprehensive score specifically includes: Obtain the current number N of valid resistance curves in the set of second candidate resistance curves corresponding to the standard resistance curve, set the target number N0, and calculate the penalty function f(N) based on the current number N and the target number N0. Calculate the average (avg) of the fitting error between the standard resistance curve and all valid resistance curves in the second candidate resistance curve set. mse ,Will As part of the overall score.

7. A device for rapidly selecting and generating standard resistance curves for resistance spot welding, used to implement the method for rapidly selecting and generating standard resistance curves for resistance spot welding as described in any one of claims 1 to 6, characterized in that, The device includes: The effective resistance curve set acquisition module is used to acquire the resistance data set during the resistance spot welding process, construct the original resistance curve set based on the resistance data set, and filter the original resistance curve set to obtain the effective resistance curve set. The first candidate resistance curve set acquisition module is used to calculate and filter the Kendall coefficient of the effective resistance curves in the effective resistance curve set to obtain the first candidate resistance curve set. The second candidate resistance curve set acquisition module is used to calculate and filter the effective resistance curves in the first candidate resistance curve set to obtain the second candidate resistance curve set. The standard resistance curve construction module is used to construct a standard resistance curve based on the effective resistance curves in the second candidate resistance curve set using a median statistical algorithm.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method for rapidly generating standard resistance curves for resistance spot welding as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for rapidly generating standard resistance curves for resistance spot welding as described in any one of claims 1 to 6.

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