CN114330491A - Method for optimizing quality of welding spot through analysis guidance of resistance spot welding curve - Google Patents
Method for optimizing quality of welding spot through analysis guidance of resistance spot welding curve Download PDFInfo
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Abstract
The invention discloses a method for analyzing and guiding to optimize the quality of a welding spot by a resistance spot welding curve, which comprises the following steps: storing welding data in the welding process of all the welding controllers to a data acquisition industrial personal computer; calculating a resistance value corresponding to each moment in the welding process of the welding data, describing a welding resistance curve, and processing the welding resistance curve; finding out characteristic data of a welding resistance curve by using a differential algorithm; screening out a plurality of welding resistance curves and fitting the welding resistance curves into a welding resistance standard curve; constructing a welding resistance curve recognition model according to the welding resistance standard curve and the corresponding characteristic values, training the model, and performing curve classification according to the change range of the preset characteristic values; inputting actual welding resistance data into a welding resistance curve recognition model for classification; and counting the classification result, and pushing the proportion of each type of characteristic variation to a quality engineer so as to optimize welding factors causing the variation according to the statistical result.
Description
Technical Field
The invention relates to the field of welding, in particular to a method and a system for optimizing the quality of a welding spot through analysis guidance of a resistance spot welding curve.
Background
The resistance spot welding technology is widely applied to the manufacturing process of the automobile body due to low cost and mature process technology, but the forming process of the welding spot nugget is complex and is related to a plurality of factors, and the related factors are as follows: the welding quality is unstable due to random variation of a plurality of factors, such as unreasonable setting of process parameters (welding current, welding time, welding pressure and the like), plate states (plate surface pollution, poor plate lap joint), equipment states (equipment faults), cooling (water flow and cooling temperature), welding postures (verticality and undercut) and the like. At present, the inspection of welding spot quality in actual working conditions mainly depends on manual identification, and the means for identifying the welding quality comprises: visual inspection, chiseling inspection, breaking inspection, ultrasonic detection, and the like. Meanwhile, a quality prediction method for self-learning aiming at the change of a welding resistance curve is researched.
Wherein, the visual inspection is that a welding quality inspector or a production worker judges the quality of the welding spot by visually inspecting the appearance of the welding spot; performing chiseling inspection on suspected welding spots and A-type welding spots by quality inspection personnel on the production line side, and judging the quality of the welding spots; the breaking inspection is that 10000 units are inspected every week or every month or every production, and quality inspection personnel randomly check a white vehicle body or a part sub-assembly for disassembly to know the quality and the overall appearance of welding spots of the vehicle body; the ultrasonic detection is to take the sub-assembly parts or the body-in-white off line, perform non-physical damage detection on the welding spots by using ultrasonic detection equipment, and estimate the quality state of the welding spots by detecting the metal structure characteristics of the welding spots. But the accuracy of visual detection cannot be guaranteed; the operation time required by detection such as visual inspection, chiseling inspection, breaking inspection, ultrasonic detection and the like is long; chiseling inspection, breaking inspection and ultrasonic detection belong to a spot inspection method, and the spot inspection proportion is low; labor costs are high in the modes of chiseling inspection, breaking inspection, ultrasonic detection and the like; the damage to the parts caused by the damage detection increases the detection cost.
In the prior art, a database is also constructed by collecting welding process data such as current, voltage, resistance, pressure and the like, intelligent algorithms such as logistic regression, support vector machine, random forest and the like are selected as basic classification learning models, data are trained and tested, prediction results are respectively output, and finally, most results are synthesized to be final prediction output. However, by adopting various AI algorithms, the operation logic design is too complex, which leads to unstable conclusion and increases the debugging difficulty; the AI model has no continuous optimization mechanism and has poor adaptability in actual production.
Disclosure of Invention
The invention mainly aims to provide a method and a system for discovering welding process disturbance and trend change thereof, guiding a quality engineer to accurately identify key factors and ensuring welding quality.
The technical scheme adopted by the invention is as follows:
the method for optimizing the quality of the welding spot through analyzing and guiding the welding curve of the resistance spot welding comprises the following steps of:
s1, storing welding data in the welding process of all the welding controllers to a data acquisition industrial personal computer, and binding the welding data with basic information of corresponding welding objects;
s2, calculating the resistance value corresponding to each moment in the welding process of the welding data, drawing a welding resistance curve, and processing the welding resistance curve;
s3, finding out characteristic data of the welding resistance curve by using a differential algorithm, wherein the characteristic data comprises upper and lower inflection points, stage duration, stage slope, stage energy consumption and spattering points; the upper inflection point and the lower inflection point correspond to resistance values at different stages in the resistance welding process;
s4, screening out a plurality of welding resistance curves and fitting the welding resistance curves into a welding resistance standard curve;
s5, constructing a welding resistance curve recognition model according to the welding resistance standard curve and the corresponding characteristic values, training the model, and carrying out curve classification according to the change range of the preset characteristic values;
s6, inputting actual welding resistance data into a welding resistance curve recognition model for classification;
and S7, counting the classification result, and pushing the proportion of each type of characteristic variation to a quality engineer so as to optimize welding factors causing the variation according to the statistical result.
In step S2, the processing of the welding resistance curve includes smoothing the welding resistance curve to eliminate short-term data jumps.
Following the above technical solution, the processing of the welding resistance curve in step S2 further includes performing a reverse curve correction.
According to the technical scheme, the curve reverse deviation correction specifically comprises the following steps:
1) screening the average value of the characteristic data of all welding resistance curves of the same welding tongs welding cap;
2) acquiring the average value of the characteristic data of the welding resistance curve of different welding point counts in different grinding times of the welding cap of the same welding tongs;
3) and fitting the average characteristic data of the welding point curves with different grinding times and different welding point counts to obtain the trend change rule of the characteristic value in the same welding cap life cycle.
According to the technical scheme, the preset variation range of the characteristic values specifically defines the deviation range of various characteristic values according to the data distribution rule obtained by experiments, and the threshold value defined by the range is optimized.
The invention also provides a system for analyzing and guiding the welding curve of resistance spot welding to optimize the quality of the welding spot, which comprises the following steps:
the data acquisition module is used for storing welding data in the welding process of all the welding controllers to the data acquisition industrial personal computer and binding the welding data with basic information of corresponding welding objects;
the data processing module is used for calculating the corresponding resistance value at each moment in the welding process of the welding data, describing a welding resistance curve and processing the welding resistance curve;
the characteristic value calculation module is used for finding out characteristic data of the welding resistance curve by using a differential algorithm, wherein the characteristic data comprises an upper inflection point, a lower inflection point, stage duration, stage slope, stage energy consumption and a spattering point; the upper inflection point and the lower inflection point correspond to resistance values at different stages in the resistance welding process;
the standard curve fitting module is used for screening a plurality of welding resistance curves and fitting the welding resistance curves into a welding resistance standard curve;
the model establishing module is used for establishing a welding resistance curve recognition model according to the welding resistance standard curve and the corresponding characteristic values, training the model and carrying out curve classification according to the change range of the preset characteristic values; inputting actual welding resistance data into a welding resistance curve recognition model for classification;
and the statistical module is used for counting the classification result and pushing the proportion of each type of characteristic variation to a quality engineer so as to optimize the welding factors causing the variation according to the statistical result.
According to the technical scheme, the data processing module is used for smoothing the welding resistance curve and eliminating short-term data jump.
According to the technical scheme, the data processing module is used for performing curve reverse correction on the welding resistance curve.
According to the technical scheme, the curve reverse correction of the data processing module specifically comprises the following steps:
1) screening the average value of the characteristic data of all welding resistance curves of the same welding tongs welding cap;
2) acquiring the average value of the characteristic data of the welding resistance curve of different welding point counts in different grinding times of the welding cap of the same welding tongs;
3) and fitting the average characteristic data of the welding point curves with different grinding times and different welding point counts to obtain the trend change rule of the characteristic value in the same welding cap life cycle.
The present invention also provides a computer storage medium, executable by a processor, having a computer program stored therein, the computer program executing the method for optimizing the quality of a weld spot through guidance of analysis of a resistance spot welding curve according to the above technical solution.
The invention has the following beneficial effects: the invention extracts characteristic values of a plurality of processes for analysis by analyzing the curve form of the resistance spot welding process, and grasps the microscopic information of the welding process so as to find the disturbance and the trend change of the welding process, and can guide a quality engineer to accurately identify key factors and ensure the welding quality.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a flow chart of a method of optimizing weld spot quality guided by analysis of a resistance spot welding curve according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a network structure for intelligently monitoring welding quality according to an embodiment of the present invention;
FIG. 3 is a flow chart of a method of optimizing weld spot quality guided by analysis of a resistance spot weld curve according to another embodiment of the present invention;
FIG. 4 is a schematic diagram before and after correction of resistance characteristic value data according to an embodiment of the present invention;
FIG. 5 is a schematic view of a weld resistance curve according to an embodiment of the present invention;
FIG. 6a is a distribution diagram of characteristic values T1 according to an embodiment of the present invention;
FIG. 6b is a diagram illustrating the distribution of the characteristic value T2 according to the embodiment of the present invention;
FIG. 7 is a statistical chart of variation ratios of various characteristics of carbon steel welding spots in accordance with the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in FIG. 1, the method for optimizing the quality of the welding spot through the analysis guidance of the resistance spot welding curve of the embodiment of the invention comprises the following steps:
s1, storing welding data in the welding process of all the welding controllers to a data acquisition industrial personal computer, and binding the welding data with basic information of corresponding welding objects;
s2, calculating the resistance value corresponding to each moment in the welding process of the welding data, drawing a welding resistance curve, and processing the welding resistance curve;
s3, finding out characteristic data of the welding resistance curve by using a differential algorithm, wherein the characteristic data comprises upper and lower inflection points, stage duration, stage slope, stage energy consumption and spattering points; the upper inflection point and the lower inflection point correspond to resistance values at different stages in the resistance welding process;
s4, screening out a plurality of welding resistance curves and fitting the welding resistance curves into a welding resistance standard curve;
s5, constructing a welding resistance curve recognition model according to the welding resistance standard curve and the corresponding characteristic values, training the model, and carrying out curve classification according to the change range of the preset characteristic values; the preset variation range of the characteristic values can specifically define the deviation range of various characteristic values according to the data distribution rule obtained by experiments, and optimize the threshold value defined by the range.
S6, inputting actual welding resistance data into a welding resistance curve recognition model for classification;
and S7, counting the classification result, and pushing the proportion of each type of characteristic variation to a quality engineer so as to optimize welding factors causing the variation according to the statistical result.
The processing of the welding resistance curve in step S2 includes smoothing the welding resistance curve to eliminate short-term data jumps.
Because the electrode cap as a consumable material can generate oxidation and pollution in the welding process to cause welding resistance curve change, when the change reaches a certain degree (a certain number of welding points), the electrode cap needs to be polished, the change has a certain rule, therefore, curve reverse correction is needed to the welding resistance curve, all the curves are restored to a new electrode cap state, inherent factors of polishing and point counting are eliminated, and analysis is facilitated.
In an embodiment of the present invention, the reverse curve rectification specifically includes:
1) screening the average value of the characteristic data of all welding resistance curves of the same welding tongs welding cap;
2) acquiring the average value of the characteristic data of the welding resistance curve of different welding point counts in different grinding times of the welding cap of the same welding tongs;
3) and fitting the average characteristic data of the welding point curves with different grinding times and different welding point counts to obtain the trend change rule of the characteristic value in the same welding cap life cycle.
The preset variation range of the characteristic values specifically defines the deviation range of various characteristic values according to the data distribution rule obtained by experiments, and optimizes the threshold value defined by the range.
In another embodiment of the present invention, a welding quality intelligent monitoring network is first established, as shown in fig. 2. And the welding quality intelligent monitoring network connects all welding controllers by an EtherNet protocol and stores welding process data to a data acquisition industrial personal computer. Then, collecting welding process data from the welding controller, identifying the form of the curve by using an algorithm, finding out an abnormal curve by taking the standard curve as a reference standard, and analyzing and counting.
As shown in fig. 3, the method for optimizing the quality of the welding spot guided by analyzing the welding curve of the resistance spot welding in this embodiment specifically includes the following steps:
1. data acquisition (network architecture, label, data collection, data temporary storage)
All welding controllers are connected by an EtherNet protocol, welding process data are stored in a data acquisition industrial personal computer, meanwhile, the current vehicle type and VIN code are read from an industrial control network and are bound with welding data, and information such as welding point numbers, plate combinations and the like are read from a welding program and are bound; and temporarily storing the processed data in a data acquisition industrial personal computer. The welding data is bound with the product, so that the product is convenient to trace.
2. Data go into lake (acquisition period')
The DMP platform assembly is called periodically, the FTP protocol is used, data are collected from the data collection industrial personal computer, cooperative operation among algorithms is completed, and finally, complete characteristic data are stored in a DMP platform data lake in a Hive data table form meeting engineering requirements for subsequent data analysis and mining. Meanwhile, a statistical analysis conclusion is given after comparison with the standard curve.
3. Data governance
And smoothing the welding resistance curve data to eliminate short-term data jump.
4. Deviation correction of curve
According to the characteristic that the welding resistance curve changes along with the increase of the welding times, the change rule is found out, the curve is reversely corrected, and the influence of the characteristic variation of the welding resistance curve caused by the normal consumption of the electrode cap is eliminated as much as possible. The specific treatment steps are as follows:
1) all characteristic data of the same welding cap are screened (T1, T2, T3, R1, R2, R3, up slope upSlope, down slope and phase energy). The method specifically comprises an upper inflection point and a lower inflection point: (R1, T1), (R2, T2), end point of welding: (R3, T3), phase duration (including T1-T2 phase duration, T2-T3 phase duration), ramp-down phase slope, phase energy consumption (including T1-T2 phase energy consumption, T2-T3 phase energy consumption), spatter point; the upper inflection point and the lower inflection point correspond to resistance values R1 and R2 at different stages in the resistance welding process;
2) acquiring the average value of the characteristic data of different grinding times in the data of the same welding cap;
3) fitting average characteristic data of different grinding times in the same welding cap;
and obtaining a trend rule of characteristic values (taking T2 as an example) of the same grinding times under the same electrode cap.
Taking the data before and after the correction of the resistance value of the characteristic point of the carbon steel welding spot as an example (the correction methods of other characteristic values are the same), as shown in fig. 4, under the optimization of the correction algorithm, the differences among the R1, R2 and R3 data are effectively reduced compared with the original data.
5. Feature point identification (splash, T1, T2, phase slope)
Based on the line type of the welding standard resistance curve, as shown in fig. 5, the characteristic value of the curve is found by using a differential algorithm, such as: the upper and lower inflection points, the stage duration, the stage slope, the stage energy consumption, the spatter and the like of the curve are taken as characteristic standard values of the welding resistance curve, wherein T1 is 6ms, and the rising slope is 0.8929; t2:24ms, falling slope-0.3744; t3:280 ms; the energy consumption of T1-T2 stages is 189.54J; r1:221.8 mu omega; r2:236.0 mu.omega; r3:140.9 mu omega; the energy consumption of T2-T3 stages is 2059.40J;
the number of coping times is 19; 126 times of welding. The whole welding process is divided into three stages, wherein R1 corresponds to a preheating and jointing stage and a resistor; r2 corresponds to the nugget formation stage, and R3 corresponds to the nugget enlargement forming stage. A current sensor and a voltage sensor are arranged on a clamp arm of the welding tongs, so that the current and the voltage passing through a welding spot in the welding process can be collected, and the resistance of the real-time welding spot can be calculated according to the real-time current and voltage values.
Method of identifying (R1, T1) points: within the range of +/-10 ms of the points of the standard curve (R1 and T1), the minimum resistance value and the corresponding time points are obtained, namely R1 and T1. If the minimum is at the boundary of the range, the curve is considered abnormal.
Method of identifying (R2, T2) points: within the range of +/-20 ms of the points of the standard curve (R2 and T2), the minimum resistance value and the corresponding time points are obtained, namely R2 and T2. If the minimum is at the boundary of the range, the curve is considered abnormal.
Method of identifying (R3, T3) points: the last time point is found in the resistance curve, which is t3, and the resistance corresponding to the time point is R3.
The method for identifying the spatter comprises the following steps: firstly, obtaining an approximate slope of each time point by adopting straight line fitting (selecting the current time point and the 3 rd time point spaced later for fitting); secondly, carrying out differential processing on the obtained slope curve so as to obtain the change situation of adjacent slopes; thirdly, in order to highlight the spatter characteristic data and reduce the normal point data, the following formulas are adopted to process a slope curve and a differential slope curve, wherein k (t) and Δ k (t) respectively represent the slope in the slope curve and the differential slope in the differential slope curve, and k × (t) takes 0 for the data with positive slope. f (t) is a curve function (spatter identification function) obtained by differentiating the slope curve.
f(t)＝k*(t)·Δk(t)
Wherein:
after the treatment of the method, the splash characteristics can be obviously distinguished from normal points.
6. Fitting of standard curve
Welding parameters are set by a welding engineer, welding is tried, welding spots with qualified quality are obtained, meanwhile, a welding resistance curve with the welding resistance curve form conforming to a theoretical value is used as a basic curve of a standard curve, and 3-10 parts of a standard curve are screened
And fitting a plurality of welding resistance curves into a welding resistance standard curve by adopting an average fitting algorithm according to the strip welding basic curve, and taking the standard curve as a system analysis reference.
7. Eigenvalue range definition optimization and curve classification
And defining deviation ranges of various characteristic values according to a data distribution rule (shown in fig. 6a and 6 b) obtained by an experiment, and optimizing a threshold value defined by the ranges to enable a judgment conclusion to be more accurate.
8. Statistics of solder joints with out-of-range characteristic values
And (3) defining value ranges of T1, T2, T3 and the like by combining physical characteristics of the welding process, and labeling and classifying curves with characteristic values exceeding the ranges.
Similarly, the characteristic values of R1, R2, R3, upper and lower slopes, stage energy consumption and the like are defined into ranges and labeled for classification.
And displaying the statistic result of the variation curve, and counting the distribution condition of the characteristic values of the welding curve in a defined time period, wherein the distribution condition is shown in figure 7 and is the statistics of various characteristic variation ratios of the carbon steel welding spots. The figure shows: and (4) statistical values such as time delay, splashing, energy abnormity, curve variation and the like, displaying and pushing statistical results to relevant quality engineers and process engineers, and searching and eliminating the variation factors.
According to the invention, curve classification is carried out according to the change range of the characteristic value of the welding resistance curve, so that the analysis is started from the microscopic data of the welding process, and the quality of the welding process is accurately mastered; the welding process quality of each welding spot can be comprehensively monitored, and the workload of quality inspection personnel is reduced; the damage of parts caused by quality inspection is reduced, and the abnormal disturbance in the welding process and the severity of disturbance factors are found in time.
The system for optimizing the quality of the welding spot through analyzing and guiding the welding curve of the resistance spot welding is characterized by comprising the following steps of:
the data acquisition module is used for storing welding data in the welding process of all the welding controllers to the data acquisition industrial personal computer and binding the welding data with basic information of corresponding welding objects;
the data processing module is used for calculating the corresponding resistance value at each moment in the welding process of the welding data, describing a welding resistance curve and processing the welding resistance curve;
the characteristic value calculation module is used for finding out characteristic data of the welding resistance curve by using a differential algorithm, wherein the characteristic data comprises an upper inflection point, a lower inflection point, stage duration, stage slope, stage energy consumption and a spattering point; the upper inflection point and the lower inflection point correspond to resistance values at different stages in the resistance welding process;
the standard curve fitting module is used for screening a plurality of welding resistance curves and fitting the welding resistance curves into a welding resistance standard curve;
the model establishing module is used for establishing a welding resistance curve recognition model according to the welding resistance standard curve and the corresponding characteristic values, training the model and carrying out curve classification according to the change range of the preset characteristic values; inputting actual welding resistance data into a welding resistance curve recognition model for classification;
and the statistical module is used for counting the classification result and pushing the proportion of each type of characteristic variation to a quality engineer so as to optimize the welding factors causing the variation according to the statistical result.
Further, the data processing module specifically performs smoothing processing on the welding resistance curve to eliminate short-term data jump.
Further, the data processing module is used for specifically carrying out curve reverse correction on the welding resistance curve.
Further, the curve reverse deviation rectification of the data processing module specifically comprises:
1) screening characteristic data of all welding resistance curves of the same welding cap;
2) obtaining the average value of the characteristic data of the same welding cap with different grinding times;
3) and performing linear fitting on the average characteristic data of different grinding times to obtain a characteristic value trend rule of the same grinding times under the same welding cap.
The system for optimizing the quality of the welding spot by analyzing and guiding the welding curve of the resistance spot welding is mainly used for realizing the method of the embodiment, and each module is mainly used for realizing the steps of the method and is not repeated herein.
The present invention also provides a computer-readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application mall, etc., on which a computer program is stored, which when executed by a processor implements corresponding functions. The computer readable storage medium of the present embodiment is configured to, when executed by a processor, implement the method of optimizing weld spot quality guided by analysis of a resistance spot welding curve of the above-described method embodiments.
In conclusion, the invention extracts a plurality of process characteristic values for analysis by analyzing the curve form of the resistance spot welding process, and grasps the microscopic information of the welding process so as to find the disturbance and the trend change of the welding process, guide a quality engineer to accurately identify key factors and ensure the welding quality.
It will be understood that modifications and variations can be made by persons skilled in the art in light of the above teachings and all such modifications and variations are intended to be included within the scope of the invention as defined in the appended claims.
Claims (10)
1. A method for optimizing the quality of a welding spot through analysis guidance of a resistance spot welding curve is characterized by comprising the following steps:
s1, storing welding data in the welding process of all the welding controllers to a data acquisition industrial personal computer, and binding the welding data with basic information of corresponding welding objects;
s2, calculating the resistance value corresponding to each moment in the welding process of the welding data, drawing a welding resistance curve, and processing the welding resistance curve;
s3, finding out characteristic data of a welding resistance curve by using a differential algorithm, wherein the characteristic data comprises upper and lower inflection points, a welding end point, stage duration, a slope of an ascending and descending stage, stage energy consumption and a spattering point; the upper inflection point and the lower inflection point correspond to resistance values at different stages in the resistance welding process;
s4, screening out a plurality of welding resistance curves and fitting the welding resistance curves into a welding resistance standard curve;
s5, constructing a welding resistance curve recognition model according to the welding resistance standard curve and the corresponding characteristic values, training the model, and carrying out curve classification according to the change range of the preset characteristic values;
s6, inputting actual welding resistance data into a welding resistance curve recognition model for classification;
and S7, counting the classification result, and pushing the proportion of each type of characteristic variation to a quality engineer so as to optimize welding factors causing the variation according to the statistical result.
2. The method for optimizing weld spot quality as defined in claim 1, wherein processing the weld resistance curve in step S2 includes smoothing the weld resistance curve to eliminate short-term data jumps.
3. The method for optimizing weld spot quality as defined in claim 1, wherein processing the weld resistance curve in step S2 further comprises performing a curve reverse rectification.
4. The method for optimizing weld spot quality through guidance on resistance spot welding curve analysis according to claim 3, wherein the curve reverse deviation correction specifically comprises:
1) screening the average value of the characteristic data of all welding resistance curves of the same welding tongs welding cap;
2) acquiring the average value of the characteristic data of the welding resistance curve of different welding point counts in different grinding times of the welding cap of the same welding tongs;
3) and fitting the average characteristic data of the welding point curves with different grinding times and different welding point counts to obtain the trend change rule of the characteristic value in the same welding cap life cycle.
5. The method for optimizing the quality of the welding spot through the analysis guidance of the resistance spot welding curve according to claim 1, wherein the preset variation range of the characteristic value defines the deviation range of each type of characteristic value according to the data distribution rule obtained by experiments, and optimizes the threshold value defined by the range.
6. A system for guiding optimization of weld spot quality through analysis of a resistance spot weld curve, comprising:
the data acquisition module is used for storing welding data in the welding process of all the welding controllers to the data acquisition industrial personal computer and binding the welding data with basic information of corresponding welding objects;
the data processing module is used for calculating the corresponding resistance value at each moment in the welding process of the welding data, describing a welding resistance curve and processing the welding resistance curve;
the characteristic value calculation module is used for finding out characteristic data of the welding resistance curve by using a differential algorithm, wherein the characteristic data comprises an upper inflection point, a lower inflection point, stage duration, stage slope, stage energy consumption and a spattering point; the upper inflection point and the lower inflection point correspond to resistance values at different stages in the resistance welding process;
the standard curve fitting module is used for screening a plurality of welding resistance curves and fitting the welding resistance curves into a welding resistance standard curve;
the model establishing module is used for establishing a welding resistance curve recognition model according to the welding resistance standard curve and the corresponding characteristic values, training the model and carrying out curve classification according to the change range of the preset characteristic values; inputting actual welding resistance data into a welding resistance curve recognition model for classification;
and the statistical module is used for counting the classification result and pushing the proportion of each type of characteristic variation to a quality engineer so as to optimize the welding factors causing the variation according to the statistical result.
7. The system for optimizing weld spot quality as defined in claim 6, wherein the data processing module is further configured to smooth the weld resistance curve to eliminate short-term data jumps.
8. The system for optimizing weld spot quality via guidance on resistance spot welding curve analysis of claim 6, wherein the data processing module performs a curve reverse rectification of the welding resistance curve.
9. The system for optimizing weld spot quality via guidance of resistance spot welding curve analysis according to claim 8, wherein the curve reverse rectification of the data processing module specifically comprises:
1) screening the average value of the characteristic data of all welding resistance curves of the same welding tongs welding cap;
2) acquiring the average value of the characteristic data of the welding resistance curve of different welding point counts in different grinding times of the welding cap of the same welding tongs;
3) and fitting the average characteristic data of the welding point curves with different grinding times and different welding point counts to obtain the trend change rule of the characteristic value in the same welding cap life cycle.
10. A computer storage medium, executable by a processor, having stored therein a computer program for performing the method of optimizing weld spot quality guided by analysis of a resistance spot weld curve according to any one of claims 1-5.
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