Intelligent argon arc welding method and system for automobile parts

Abstract

The invention relates to the technical field of welding methods, and particularly discloses an intelligent argon arc welding method and system for automobile parts. Calculating a welding abnormal coefficient; whether the welding quality of the weld joint in the detection period is qualified or not is evaluated, and a period quality evaluation signal is generated; generating an abnormal welding alarm based on the periodic quality disqualification signal, and stopping the welding operation; the completed time period in the welding process is analyzed, whether fluctuation of the welding parameters is related to the welding quality or not during welding is evaluated, and a related evaluation signal is generated; on the basis of the associated signals and the fluctuation of the welding parameters during welding, the duration of the detection period is regulated and controlled; according to the method, the production efficiency and the product quality are improved, the cost and the error rate of manual intervention are reduced, in addition, the system can respond to welding quality abnormities more timely through the function of dynamically regulating and controlling the detection period, and therefore the stability and the reliability of the overall welding quality are further improved.

Description

Technical Field

[0001] The invention relates to the technical field of welding methods, and in particular to an intelligent argon arc welding method and system for automobile accessories. Background Art

[0002] Argon arc welding is a welding technology that uses argon as a shielding gas. It is also called argon gas shielded welding. It is to pass argon shielding gas around the arc welding to isolate the air outside the welding area to prevent oxidation of the welding area. Argon arc welding technology is based on the principle of ordinary arc welding. It uses argon gas to protect metal welding materials. Through high current, the welding materials are melted into liquid on the base material to form a molten pool, so that the welded metal and welding materials can achieve metallurgical bonding. Because argon gas is continuously supplied during high-temperature molten welding, the welding materials cannot come into contact with oxygen in the air, thereby preventing oxidation of the welding materials. Therefore, stainless steel and iron hardware metals can be welded.

[0003] However, the existing technology usually only focuses on a single indicator of the weld, such as appearance, size, etc., and ignores the overall difference between the weld and the preset standard, resulting in inaccurate evaluation results. In addition, the existing technology does not consider the correlation between the fluctuation of welding parameters and the quality of the weld, which makes it difficult to accurately identify potential problems in the welding process, resulting in slow response speed, affecting production efficiency and product quality. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent argon arc welding method and system for automobile parts to solve the technical problems in the above background.

[0005] The purpose of the present invention can be achieved through the following technical solutions: In a first aspect, the present invention provides an intelligent argon arc welding method for automobile parts, comprising the following steps: Step 1: During the welding process, the welding process is monitored in real time to obtain the welding data of the weld within the detection cycle; Among them, welding data include: edge difference ratio and abnormal situation value; Step 2: Calculate welding abnormality coefficient based on welding data; Step 3: Based on the welding abnormality coefficient, evaluate whether the quality of the weld in the inspection cycle is qualified, and generate a cycle quality evaluation signal; The periodic quality assessment signal includes: a periodic quality unqualified signal and a periodic quality qualified signal; Step 4: Based on the cycle quality failure signal, a welding abnormality alarm is generated and the welding operation is stopped; Step 5: Analyze the completed time period during the welding process, evaluate whether the fluctuation of welding parameters during welding is related to the welding quality, and generate a related evaluation signal; The associated evaluation signal includes: an associated signal and an unassociated signal; Step 6: Based on the relevant signal and the fluctuation of welding parameters during welding, the duration of the detection cycle is adjusted.

[0006] As a further solution of the present invention: the process of obtaining welding data is as follows: Identify the edge of the weld within the detection cycle and obtain the cycle edge line; Then, the periodic edge line is compared and analyzed with the preset edge line corresponding to the detection period, and the edge difference ratio and the abnormal situation value are calculated respectively.

[0007] As a further solution of the present invention: the process of obtaining the edge difference ratio is: The difference between the periodic edge line length and the preset edge line length is calculated, and the absolute value is taken to obtain the edge difference, and then the ratio of the edge difference to the preset edge line length is calculated to obtain the edge difference ratio.

[0008] As a further solution of the present invention: the process of obtaining the abnormal situation value is: Evenly divide the periodic edge line within the detection period to obtain multiple edge sub-segments; Respectively obtain the area of ​​the region enclosed by each edge sub-segment and the corresponding edge preset sub-segment to obtain the deviation sub-area; Preset a deviation sub-area threshold, and compare and analyze all deviation sub-areas with the deviation sub-area threshold respectively; If the deviation sub-area is less than or equal to the deviation sub-area threshold, a deviation-no-effect signal is generated; If the deviation sub-area is greater than the deviation sub-area threshold, a deviation-influential signal is generated, and the edge sub-segment corresponding to the deviation sub-area is marked as a deviation-abnormal sub-segment; Merge all the consecutive deviation abnormal sub-segments to obtain one or more edge abnormal segments; The total number of edge abnormal segments is calculated by ratio with the total number of edge sub-segments to obtain an abnormal number representation value; At the same time, the ratio of the total length of all edge abnormal segments to the length of the periodic edge line is calculated to obtain the abnormality degree characterization value; The abnormality value is obtained by multiplying the abnormality quantity representation value and the abnormality degree representation value.

[0009] As a further solution of the present invention: the process of obtaining the welding abnormality coefficient is: By formula: , calculate the welding abnormality coefficient YC, where a1 and a2 are preset proportional factors, and both are greater than 0, CB is the edge difference ratio, and QK is the abnormal situation value.

[0010] As a further solution of the present invention: the process of generating a periodic quality assessment signal is: Preset a welding abnormality coefficient threshold, and compare and analyze the welding abnormality coefficient with the welding abnormality coefficient threshold; If the welding abnormality coefficient is less than or equal to the welding abnormality coefficient threshold, a cycle quality qualified signal is generated; If the welding abnormality coefficient is greater than the welding abnormality coefficient threshold, a cycle quality failure signal is generated.

[0011] As a further solution of the present invention: the process of generating the associated evaluation signal is: Based on the completed time period during the welding process, obtain the real-time parameters of the welding parameters and obtain the abnormal time period of the parameters; Extract the welding time of all edge abnormal segments respectively to obtain edge abnormal time period; Perform overlap analysis on the parameter abnormal period and the edge abnormal period to obtain the overlap period; By formula: , calculate and obtain the overlap period degree ratio CD, where CH is the total duration of the overlap period, CS is the total duration of the parameter abnormal period, BY is the total duration of the edge abnormal period, b is a constant, and b>0; Extract the number of overlapping time periods to obtain the total number of overlapping time periods, and calculate the ratio with the mean number of total abnormal time periods to obtain the overlapping number ratio; Among them, the mean of total abnormal periods is the average of the total number of parameter abnormal periods and the total number of edge abnormal periods; Then, the overlap period degree ratio and the overlap period quantity ratio are multiplied to obtain a first correlation value; At the same time, the parameter abnormality period and edge abnormality period corresponding to each overlap period are marked as parameter abnormality overlap section and edge abnormality overlap section respectively; By formula: , calculate and obtain the overlap period degree sub-ratio CZ, where TCi represents the duration of the i-th overlap period, TSi represents the duration of the i-th parameter abnormal overlap section, TBi represents the duration of the i-th edge abnormal overlap section, and the value of i is 1, 2, 3, ..., n, where n represents the total number of overlap periods; The total number of parameter abnormality overlap segments and the total number of parameter abnormality time periods are calculated to obtain the parameter abnormality overlap ratio; The total number of edge anomaly overlap segments is calculated to be proportional to the total number of edge anomaly time periods to obtain the edge anomaly overlap ratio; Then, the parameter anomaly coincidence ratio and the edge anomaly coincidence ratio are multiplied to obtain the quantum ratio of the number of coincidence periods; Then, the product of the overlap period degree sub-ratio and the overlap period quantity sub-ratio is calculated to obtain a second correlation value; Then the first correlation value and the second correlation value are summed to obtain a correlation coefficient; Preset a correlation coefficient threshold, and compare and analyze the correlation coefficient with the correlation coefficient threshold; If the correlation coefficient is less than or equal to the correlation coefficient threshold, a no-correlation signal is generated; If the correlation coefficient is greater than the correlation coefficient threshold, a correlation signal is generated.

[0012] As a further solution of the present invention: the process of obtaining the parameter abnormal period is: Based on a two-dimensional coordinate system with time as the X-axis and the real-time parameter as the Y-axis, a real-time parameter curve is drawn, and at the same time, a parameter range interval of the welding parameter is drawn in the two-dimensional coordinate system; Extract all curve segments of the real-time parameter curve that are outside the parameter range to obtain the parameter deviation period, and extract the area enclosed by the real-time parameter curve and the parameter range in each parameter deviation period to obtain the parameter deviation area of ​​each parameter deviation period; Preset parameter deviation area threshold, and compare and analyze all parameter deviation areas with the parameter deviation area threshold; If the parameter deviation area is less than or equal to the parameter deviation area threshold, a parameter fluctuation normal signal is generated; If the parameter deviation area is greater than the parameter deviation area threshold, a parameter fluctuation abnormality signal is generated, and the parameter deviation period corresponding to the parameter deviation area is marked as a parameter abnormality period.

[0013] As a further solution of the present invention: the process of regulating the duration of the detection cycle is: Based on the relevant signal, the parameter deviation area PC of each parameter abnormal period and the total value PZ of the deviation sub-area of ​​each edge abnormal period are extracted; The parameter deviation area PC of each parameter abnormal period and the total deviation sub-area PZ of the corresponding edge abnormal period are packaged into a set of point data (PC, PZ); Based on the two-dimensional coordinate system with the parameter deviation area PC as the X-axis and the total deviation sub-area PZ as the Y-axis, all point data are marked, and then the least squares method is used to obtain a fitting straight line and the fitting straight line equation: , where k represents the slope of the fitted line, b represents the intercept of the fitted line, and k and b are constants; Substitute Y=PZY into the fitted line equation: In the equation, X = PCJ is obtained, where PZY is the deviation sub-area threshold and PCJ is the parameter deviation area warning value; In the subsequent process of the welding process, in a new round of detection cycle, when the total value of the parameter deviation area in the detection cycle is equal to the parameter deviation area warning value, the detection cycle is terminated, so as to detect the weld quality and generate a periodic quality assessment signal in time; Among them, the total value of the parameter deviation area is the total value of all parameter deviation areas in a new round of detection cycle.

[0014] In a second aspect, the present invention provides an intelligent argon arc welding system for automobile parts, the system comprising: Welding data acquisition module: During the welding process, the welding process is monitored in real time to obtain the welding data of the weld within the detection cycle; Among them, welding data include: edge difference ratio and abnormal situation value; Welding data processing module: calculate welding abnormality coefficient based on welding data; Welding quality assessment module: Based on the welding abnormality coefficient, it assesses whether the quality of the weld within the inspection cycle is qualified and generates a periodic quality assessment signal; The periodic quality assessment signal includes: a periodic quality unqualified signal and a periodic quality qualified signal; Abnormal alarm module: based on the cycle quality failure signal, it generates a welding abnormal alarm and stops the welding operation; Correlation evaluation module: Analyzes the completed time period during the welding process, evaluates whether the fluctuation of welding parameters during welding is related to the welding quality, and generates a correlation evaluation signal; The associated evaluation signal includes: an associated signal and an unassociated signal; Cycle control module: Based on the relevant signals and the fluctuation of welding parameters during welding, the duration of the detection cycle is controlled.

[0015] Beneficial effects of the present invention: The present invention realizes accurate management of weld quality through real-time monitoring, comprehensive evaluation and automatic control. This method not only improves production efficiency and product quality, but also reduces the cost and error rate of manual intervention. The present invention enables the system to respond to abnormal welding quality more promptly through the function of dynamically regulating the detection cycle, thereby further improving the stability and reliability of the overall welding quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present invention will be further described below in conjunction with the accompanying drawings.

[0017] Figure 1 It is a flowchart of the present invention; Figure 2 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Example 1: Please refer to Figure 1 As shown, the intelligent argon arc welding method for automobile parts according to the embodiment of the present invention comprises the following steps: Step 1: During the welding process, the welding process is monitored in real time to obtain the welding data of the weld within the detection cycle; Among them, welding data include: edge difference ratio and abnormal situation value; In some embodiments, an edge detection algorithm is used to identify the edge of the weld within the detection cycle to obtain a cycle edge line; The difference between the periodic edge line length and the preset edge line length is calculated, and the absolute value is taken to obtain the edge difference, and then the ratio between the edge difference and the preset edge line length is calculated to obtain the edge difference ratio; It should be explained that the preset edge line length in the difference calculation between the period edge line length and the preset edge line length is: the preset edge line length of the same detection period as the period edge line; wherein the preset edge line is a period edge line preset by a person skilled in the art based on the material type, thickness, shape and other characteristics of the fitting to be welded; It needs to be further explained that the edge difference ratio is expressed as the degree of difference between the weld edge and the preset edge. The larger the value of the edge difference ratio is, the greater the difference between the weld edge and the preset edge is, that is, the greater the deviation in the weld formation process is. At the same time, the periodic edge lines within the detection period are evenly divided to obtain multiple edge sub-segments; Respectively obtain the area of ​​the region enclosed by each edge sub-segment and the corresponding edge preset sub-segment to obtain the deviation sub-area; It should be explained that the corresponding edge preset sub-segments are the sub-segments of the preset edge line corresponding to the edge sub-segments at different positions of the weld respectively; Preset a deviation sub-area threshold, and compare and analyze all deviation sub-areas with the deviation sub-area threshold respectively; If the deviation sub-area is less than or equal to the deviation sub-area threshold, it means that the area enclosed by the edge sub-segment corresponding to the deviation sub-area and the edge preset sub-segment is small, that is, the difference between the weld at this position and the preset standard is small, and a deviation no-effect signal is generated; If the deviation sub-area is greater than the deviation sub-area threshold, it means that the area enclosed by the edge sub-segment corresponding to the deviation sub-area and the edge preset sub-segment is larger, that is, the difference between the weld at this position and the preset standard is larger, then a deviation influence signal is generated, and the edge sub-segment corresponding to the deviation sub-area is marked as a deviation abnormal sub-segment; Merge all the consecutive deviation abnormal sub-segments to obtain one or more edge abnormal segments; It should be explained that the edge abnormal segment is obtained by merging consecutive deviation abnormal sub-segments and marking them as an edge abnormal segment; if the deviation abnormal sub-segment has no other adjacent deviation abnormal sub-segments, it is also marked as an edge abnormal segment; The total number of edge abnormal segments is calculated by ratio with the total number of edge sub-segments to obtain an abnormal number representation value; At the same time, the ratio of the total length of all edge abnormal segments to the length of the periodic edge line is calculated to obtain the abnormality degree characterization value; The abnormality value is obtained by multiplying the abnormality quantity representation value and the abnormality degree representation value; It should be explained that the abnormality value represents the overall difference between the weld and the preset standard during the inspection period. The larger the abnormality value, the greater the difference between the weld and the preset standard. Step 2: Calculate welding abnormality coefficient based on welding data; In some embodiments, by the formula: , calculate the welding abnormality coefficient YC, where a1 and a2 are preset proportional factors, both greater than 0, CB is the edge difference ratio, and QK is the abnormal situation value; Step 3: Based on the welding abnormality coefficient, evaluate whether the quality of the weld in the inspection cycle is qualified, and generate a cycle quality evaluation signal; The periodic quality assessment signal includes: a periodic quality unqualified signal and a periodic quality qualified signal; In some implementation schemes, a welding abnormality coefficient threshold is preset, and the welding abnormality coefficient is compared and analyzed with the welding abnormality coefficient threshold; If the welding abnormality coefficient is less than or equal to the welding abnormality coefficient threshold, it means that the difference between the weld and the preset standard in the current detection cycle is small, and a cycle quality qualified signal is generated; If the welding abnormality coefficient is greater than the welding abnormality coefficient threshold, it means that the weld in the current detection cycle is significantly different from the preset standard, and a cycle quality failure signal is generated; Step 4: Based on the periodic quality failure signal, a welding abnormality alarm is generated and the welding operation is stopped to avoid the production of unqualified products; The technical solution of the embodiment of the present invention is mainly as follows: real-time monitoring of the welding process to obtain the edge difference ratio and abnormal situation value of the weld, the edge difference ratio is calculated by comparing the length difference between the actual welding edge line and the preset edge line, reflecting the deviation of the weld shape from the expected standard, and the abnormal situation value is determined based on the deviation area of ​​multiple sub-segments of the weld edge from the preset standard, and the overall difference between the weld and the preset standard is quantified by calculating these deviation areas and identifying abnormal areas; the welding abnormality coefficient is obtained by formula using the edge difference ratio and the abnormal situation value, reflecting the overall condition of the weld quality; the welding abnormality coefficient is compared with the preset threshold value to evaluate whether the quality of the weld is qualified, if the welding abnormality coefficient is lower than or equal to the threshold value, the weld quality is considered to be qualified; if it is higher than the threshold value, it is considered to be unqualified; finally, once the weld quality is detected to be unqualified, a welding abnormality alarm is immediately generated, and the welding operation is automatically stopped to ensure that only welds with qualified quality can continue to be produced; the embodiment of the present invention realizes accurate management of the weld quality through real-time monitoring, comprehensive evaluation and automatic control, and the method not only improves production efficiency and product quality, but also reduces the cost and error rate of manual intervention.

[0020] Example 2: Based on Example 1, please refer to Figure 1 As shown, the intelligent argon arc welding method for automobile parts according to the embodiment of the present invention further includes real-time regulation of the duration of the detection cycle in order to detect the abnormality of welding quality more timely. The specific method is the following steps: Step 5: Analyze the completed time period during the welding process, evaluate whether the fluctuation of welding parameters during welding is related to the welding quality, and generate a related evaluation signal; Among them, welding parameters include but are not limited to: welding current, welding voltage, welding speed; The associated evaluation signal includes: an associated signal and an unassociated signal; In some embodiments, based on the completed time period in the welding process, the real-time parameters of the welding parameters are obtained, and based on a two-dimensional coordinate system with time as the X-axis and the real-time parameters as the Y-axis, a real-time parameter curve is drawn, and at the same time, the parameter range interval of the welding parameters is drawn in the two-dimensional coordinate system; It should be explained that the upper limit and the lower limit of the parameter range are preset by those skilled in the art, and the parameter range represents the boundary between normal welding parameters and abnormal welding parameters; Extract all curve segments of the real-time parameter curve that are outside the parameter range to obtain the parameter deviation period, and extract the area enclosed by the real-time parameter curve and the parameter range in each parameter deviation period to obtain the parameter deviation area of ​​each parameter deviation period; Preset parameter deviation area threshold, and compare and analyze all parameter deviation areas with the parameter deviation area threshold; If the parameter deviation area is less than or equal to the parameter deviation area threshold, it means that the welding parameter exceeds the preset range by a small amount, and a parameter fluctuation normal signal is generated; If the parameter deviation area is greater than the parameter deviation area threshold, it means that the welding parameter exceeds the preset range by a large margin, and a parameter fluctuation abnormality signal is generated, and the parameter deviation period corresponding to the parameter deviation area is marked as a parameter abnormality period; Extract the welding time of all edge abnormal segments respectively to obtain edge abnormal time period; Perform overlap analysis on the parameter abnormal period and the edge abnormal period to obtain the overlap period; By formula: , calculate and obtain the overlap period degree ratio CD, where CH is the total duration of the overlap period, CS is the total duration of the parameter abnormal period, BY is the total duration of the edge abnormal period, b is a constant, and b>0; Extract the number of overlapping time periods to obtain the total number of overlapping time periods, and calculate the ratio with the mean number of total abnormal time periods to obtain the overlapping number ratio; Among them, the mean of total abnormal periods is the average of the total number of parameter abnormal periods and the total number of edge abnormal periods; Then, the overlap period degree ratio and the overlap period quantity ratio are multiplied to obtain a first correlation value; At the same time, the parameter abnormality period and edge abnormality period corresponding to each overlap period are marked as parameter abnormality overlap section and edge abnormality overlap section respectively; By formula: , calculate and obtain the overlap period degree sub-ratio CZ, where TCi represents the duration of the i-th overlap period, TSi represents the duration of the i-th parameter abnormal overlap section, TBi represents the duration of the i-th edge abnormal overlap section, and the value of i is 1, 2, 3, ..., n, where n represents the total number of overlap periods; The total number of parameter abnormality overlap segments and the total number of parameter abnormality time periods are calculated to obtain the parameter abnormality overlap ratio; The total number of edge anomaly overlap segments is calculated to be proportional to the total number of edge anomaly time periods to obtain the edge anomaly overlap ratio; Then, the parameter anomaly coincidence ratio and the edge anomaly coincidence ratio are multiplied to obtain the quantum ratio of the number of coincidence periods; Then, the product of the overlap period degree sub-ratio and the overlap period quantity sub-ratio is calculated to obtain a second correlation value; Then the first correlation value and the second correlation value are summed to obtain a correlation coefficient; Preset a correlation coefficient threshold, and compare and analyze the correlation coefficient with the correlation coefficient threshold; If the correlation coefficient is less than or equal to the correlation coefficient threshold, it means that the fluctuation of welding parameters is less correlated with the abnormality of the weld edge, and an uncorrelated signal is generated; If the correlation coefficient is greater than the correlation coefficient threshold, it means that the fluctuation of the welding parameters is highly correlated with the abnormality of the weld edge, and a correlation signal is generated; Step 6: Based on the relevant signal and the fluctuation of welding parameters during welding, the duration of the detection cycle is adjusted; In some embodiments, based on the associated signal, the parameter deviation area PC of each parameter abnormal period and the total deviation sub-area value PZ of each edge abnormal period are extracted; It should be explained that the total value of the deviation sub-area of ​​the edge anomaly period is the sum of the deviation sub-areas of all deviation anomaly sub-segments in the edge anomaly period; The parameter deviation area PC of each parameter abnormal period and the total deviation sub-area PZ of the corresponding edge abnormal period are packaged into a set of point data (PC, PZ); Based on the two-dimensional coordinate system with the parameter deviation area PC as the X-axis and the total deviation sub-area PZ as the Y-axis, all point data are marked, and then the least squares method is used to obtain a fitting straight line and the fitting straight line equation: , where k represents the slope of the fitted line, b represents the intercept of the fitted line, and k and b are constants; Substitute Y=PZY into the fitted line equation: In the equation, X = PCJ is obtained, where PZY is the deviation sub-area threshold and PCJ is the parameter deviation area warning value; In the subsequent process of the welding process, in a new round of detection cycle, when the total value of the parameter deviation area in the detection cycle is equal to the parameter deviation area warning value, the detection cycle is terminated, so as to detect the weld quality and generate a periodic quality assessment signal in time; Among them, the total value of parameter deviation area is the total value of all parameter deviation areas in a new round of detection cycle; The technical solution of the embodiment of the present invention is mainly as follows: obtaining welding parameters in real time, drawing a real-time parameter curve, setting the limit of normal welding parameters at the same time, identifying the parameter deviation period by comparing the real-time parameters with the limit, and calculating the parameter deviation area. At the same time, the edge state of the weld is monitored, the edge abnormality segment is identified, and the abnormal situation value is calculated; correlation analysis is performed on the fluctuation of welding parameters and the abnormality of the weld edge, and the correlation coefficient is calculated by extracting the overlapping part of the parameter abnormality period and the edge abnormality period to evaluate the correlation between the welding parameter fluctuation and the weld edge abnormality. If the correlation coefficient is high, it indicates that there is a significant correlation between the two; based on the correlation analysis results, the duration of the detection cycle is dynamically adjusted. Specifically, the parameter abnormality period is extracted. The point data of the abnormal period of the segment and edge are used to fit the straight line equation using the least squares method, and the parameter deviation area warning value is calculated. In the subsequent stage of the welding process, when the total value of the parameter deviation area in a new round of detection cycle reaches the warning value, the detection cycle is immediately terminated, the weld quality detection is carried out, and a quality assessment signal is generated; the present invention realizes precise management of welding quality by real-time monitoring of welding parameters and weld status, combining correlation analysis with dynamic regulation. It not only improves production efficiency and product quality, but also reduces the cost and error rate of manual intervention. In addition, the function of dynamically regulating the detection cycle enables the system to respond to abnormalities in welding quality more promptly, thereby further improving the stability and reliability of the overall welding quality.

[0021] Example 3: Based on Example 1 and Example 2, please refer to Figure 2 As shown, the intelligent argon arc welding system for automobile parts according to the embodiment of the present invention comprises: Welding data acquisition module: During the welding process, the welding process is monitored in real time to obtain the welding data of the weld within the detection cycle; Among them, welding data include: edge difference ratio and abnormal situation value; Welding data processing module: calculate welding abnormality coefficient based on welding data; Welding quality assessment module: Based on the welding abnormality coefficient, it assesses whether the quality of the weld within the inspection cycle is qualified and generates a periodic quality assessment signal; The periodic quality assessment signal includes: a periodic quality unqualified signal and a periodic quality qualified signal; Abnormal alarm module: based on the cycle quality failure signal, it generates a welding abnormal alarm and stops the welding operation; Correlation evaluation module: Analyzes the completed time period during the welding process, evaluates whether the fluctuation of welding parameters during welding is related to the welding quality, and generates a correlation evaluation signal; The associated evaluation signal includes: an associated signal and an unassociated signal; Cycle control module: Based on the relevant signals and the fluctuation of welding parameters during welding, the duration of the detection cycle is controlled.

[0022] The above thresholds are set for the convenience of comparison. The thresholds depend on the amount of sample data and the number of bases set by technicians in this field for each set of sample data. The above formulas are obtained by collecting a large amount of data and performing software simulation to select a formula close to the actual value. The factors in the formula are set by technicians in this field according to actual conditions; for example: Formula ; A technician in this field collects multiple sets of welding data and sets a corresponding welding anomaly coefficient for each set of welding data; Substitute the set welding anomaly coefficient and the collected welding data into the formula, any two formulas constitute a binary linear equation system, and the calculated factors are screened and averaged to obtain the values ​​of a1 and a2, which are 2.47 and 2.63 respectively; The size of the factor is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the factor depends on the amount of welding data and the preliminary setting of the corresponding welding anomaly coefficient for each set of welding data by technical personnel in this field; as long as it does not affect the proportional relationship between the parameter and the quantified value, such as the welding anomaly coefficient is proportional to the value of the abnormal situation.

[0023] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. Intelligent argon arc welding method for automobile parts, characterized in that: The following steps are involved: Step 1: During the welding process, the welding process is monitored in real time to obtain the welding data of the weld within the detection cycle; Among them, welding data include: edge difference ratio and abnormal situation value; Step 2: Calculate welding abnormality coefficient based on welding data; Step 3: Based on the welding abnormality coefficient, evaluate whether the quality of the weld in the inspection cycle is qualified, and generate a cycle quality evaluation signal; The periodic quality assessment signal includes: a periodic quality unqualified signal and a periodic quality qualified signal; Step 4: Based on the cycle quality failure signal, a welding abnormality alarm is generated and the welding operation is stopped; Step 5: Analyze the completed time period during the welding process, evaluate whether the fluctuation of welding parameters during welding is related to the welding quality, and generate a related evaluation signal; The associated evaluation signal includes: an associated signal and an unassociated signal; Step 6: Based on the relevant signal and the fluctuation of welding parameters during welding, the duration of the detection cycle is adjusted.

2. The intelligent argon arc welding method for automobile parts according to claim 1, characterized in that: The process of obtaining welding data is as follows: Identify the edge of the weld within the detection cycle and obtain the cycle edge line; Then, the periodic edge line is compared and analyzed with the preset edge line corresponding to the detection period, and the edge difference ratio and the abnormal situation value are calculated respectively.

3. The intelligent argon arc welding method for automobile parts according to claim 2, characterized in that: The process of obtaining the edge difference ratio is: The difference between the periodic edge line length and the preset edge line length is calculated, and the absolute value is taken to obtain the edge difference, and then the ratio of the edge difference to the preset edge line length is calculated to obtain the edge difference ratio.

4. The intelligent argon arc welding method for automobile parts according to claim 2, characterized in that: The process of obtaining the abnormal situation value is as follows: Evenly divide the periodic edge line within the detection period to obtain multiple edge sub-segments; Respectively obtain the area of ​​the region enclosed by each edge sub-segment and the corresponding edge preset sub-segment to obtain the deviation sub-area; Preset a deviation sub-area threshold, and compare and analyze all deviation sub-areas with the deviation sub-area threshold respectively; If the deviation sub-area is less than or equal to the deviation sub-area threshold, a deviation-no-effect signal is generated; If the deviation sub-area is greater than the deviation sub-area threshold, a deviation-influential signal is generated, and the edge sub-segment corresponding to the deviation sub-area is marked as a deviation-abnormal sub-segment; Merge all the consecutive deviation abnormal sub-segments to obtain one or more edge abnormal segments; The total number of edge abnormal segments is calculated by ratio with the total number of edge sub-segments to obtain an abnormal number representation value; At the same time, the ratio of the total length of all edge abnormal segments to the length of the periodic edge line is calculated to obtain the abnormality degree characterization value; The abnormality value is obtained by multiplying the abnormality quantity representation value and the abnormality degree representation value.

5. The intelligent argon arc welding method for automobile parts according to claim 1, characterized in that: The process of obtaining welding abnormality coefficient is as follows: By formula: , calculate the welding abnormality coefficient YC, where a1 and a2 are preset proportional factors, and both are greater than 0, CB is the edge difference ratio, and QK is the abnormal situation value.

6. The intelligent argon arc welding method for automobile parts according to claim 1, characterized in that: The process of generating a periodic quality assessment signal is: Preset a welding abnormality coefficient threshold, and compare and analyze the welding abnormality coefficient with the welding abnormality coefficient threshold; If the welding abnormality coefficient is less than or equal to the welding abnormality coefficient threshold, a cycle quality qualified signal is generated; If the welding abnormality coefficient is greater than the welding abnormality coefficient threshold, a cycle quality failure signal is generated.

7. The intelligent argon arc welding method for automobile parts according to claim 1, characterized in that: The process of generating the associated evaluation signal is: Based on the completed time period during the welding process, obtain the real-time parameters of the welding parameters and obtain the abnormal time period of the parameters; Extract the welding time of all edge abnormal segments respectively to obtain edge abnormal time period; Perform overlap analysis on the parameter abnormal period and the edge abnormal period to obtain the overlap period; By formula: , calculate and obtain the overlap period degree ratio CD, where CH is the total duration of the overlap period, CS is the total duration of the parameter abnormal period, BY is the total duration of the edge abnormal period, b is a constant, and b>0; Extract the number of overlapping time periods to obtain the total number of overlapping time periods, and calculate the ratio with the mean number of total abnormal time periods to obtain the overlapping number ratio; Among them, the mean of total abnormal periods is the average of the total number of parameter abnormal periods and the total number of edge abnormal periods; Then, the overlap period degree ratio and the overlap period quantity ratio are multiplied to obtain a first correlation value; At the same time, the parameter abnormality period and edge abnormality period corresponding to each overlap period are marked as parameter abnormality overlap section and edge abnormality overlap section respectively; By formula: , calculate and obtain the overlap period degree sub-ratio CZ, where TCi represents the duration of the i-th overlap period, TSi represents the duration of the i-th parameter abnormal overlap section, TBi represents the duration of the i-th edge abnormal overlap section, and the value of i is 1, 2, 3, ..., n, where n represents the total number of overlap periods; The total number of parameter abnormality overlap segments and the total number of parameter abnormality time periods are calculated to obtain the parameter abnormality overlap ratio; The total number of edge anomaly overlap segments is calculated to be proportional to the total number of edge anomaly time periods to obtain the edge anomaly overlap ratio; Then, the parameter anomaly coincidence ratio and the edge anomaly coincidence ratio are multiplied to obtain the quantum ratio of the number of coincidence periods; Then, the product of the overlap period degree sub-ratio and the overlap period quantity sub-ratio is calculated to obtain a second correlation value; Then the first correlation value and the second correlation value are summed to obtain a correlation coefficient; Preset a correlation coefficient threshold, and compare and analyze the correlation coefficient with the correlation coefficient threshold; If the correlation coefficient is less than or equal to the correlation coefficient threshold, a no-correlation signal is generated; If the correlation coefficient is greater than the correlation coefficient threshold, a correlation signal is generated.

8. The intelligent argon arc welding method for automobile parts according to claim 7, characterized in that: The process of obtaining the parameter abnormal period is as follows: Based on a two-dimensional coordinate system with time as the X-axis and the real-time parameter as the Y-axis, a real-time parameter curve is drawn, and at the same time, a parameter range interval of the welding parameter is drawn in the two-dimensional coordinate system; Extract all curve segments of the real-time parameter curve that are outside the parameter range to obtain the parameter deviation period, and extract the area enclosed by the real-time parameter curve and the parameter range in each parameter deviation period to obtain the parameter deviation area of ​​each parameter deviation period; Preset parameter deviation area threshold, and compare and analyze all parameter deviation areas with the parameter deviation area threshold; If the parameter deviation area is less than or equal to the parameter deviation area threshold, a parameter fluctuation normal signal is generated; If the parameter deviation area is greater than the parameter deviation area threshold, a parameter fluctuation abnormality signal is generated, and the parameter deviation period corresponding to the parameter deviation area is marked as a parameter abnormality period.

9. The intelligent argon arc welding method for automobile parts according to claim 1, characterized in that: The process of adjusting the duration of the detection cycle is as follows: Based on the relevant signal, the parameter deviation area PC of each parameter abnormal period and the total value PZ of the deviation sub-area of ​​each edge abnormal period are extracted; The parameter deviation area PC of each parameter abnormal period and the total deviation sub-area PZ of the corresponding edge abnormal period are packaged into a set of point data (PC, PZ); Based on the two-dimensional coordinate system with the parameter deviation area PC as the X-axis and the total deviation sub-area PZ as the Y-axis, all point data are marked, and then the least squares method is used to obtain a fitting straight line and the fitting straight line equation: , where k represents the slope of the fitted line, b represents the intercept of the fitted line, and k and b are constants; Substitute Y=PZY into the fitted line equation: In the equation, X = PCJ is obtained, where PZY is the deviation sub-area threshold and PCJ is the parameter deviation area warning value; In the subsequent process of the welding process, in a new round of detection cycle, when the total value of the parameter deviation area in the detection cycle is equal to the parameter deviation area warning value, the detection cycle is terminated, so as to detect the weld quality and generate a periodic quality assessment signal in time; Among them, the total value of the parameter deviation area is the total value of all parameter deviation areas in a new round of detection cycle.

10. Intelligent argon arc welding system for automobile parts, characterized in that: The system is used to execute the method described in any one of claims 1 to 9, and the system comprises: Welding data acquisition module: During the welding process, the welding process is monitored in real time to obtain the welding data of the weld within the detection cycle; Among them, welding data include: edge difference ratio and abnormal situation value; Welding data processing module: calculate welding abnormality coefficient based on welding data; Welding quality assessment module: Based on the welding abnormality coefficient, it assesses whether the quality of the weld within the inspection cycle is qualified and generates a periodic quality assessment signal; The periodic quality assessment signal includes: a periodic quality unqualified signal and a periodic quality qualified signal; Abnormal alarm module: based on the cycle quality failure signal, it generates a welding abnormal alarm and stops the welding operation; Correlation evaluation module: Analyzes the completed time period during the welding process, evaluates whether the fluctuation of welding parameters during welding is related to the welding quality, and generates a correlation evaluation signal; The associated evaluation signal includes: an associated signal and an unassociated signal; Cycle control module: Based on the relevant signals and the fluctuation of welding parameters during welding, the duration of the detection cycle is controlled.

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