CN116890179A - Parameter adjustment method and device for industrial production - Google Patents
Parameter adjustment method and device for industrial production Download PDFInfo
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- CN116890179A CN116890179A CN202311032939.1A CN202311032939A CN116890179A CN 116890179 A CN116890179 A CN 116890179A CN 202311032939 A CN202311032939 A CN 202311032939A CN 116890179 A CN116890179 A CN 116890179A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
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Abstract
The application discloses a parameter adjustment method and a device for industrial production, which relate to the technical field of intelligent welding in automobile manufacturing and comprise the following steps: s101, collecting multiple information, including welding physical parameter information and software control information, of the welding robot in the process of intelligent welding of parts of the same specification in the automobile manufacturing process, and processing the welding physical parameter information and the software control information in the process of welding after the information is collected. According to the intelligent welding method, the parameters of the welding robot during intelligent welding of the parts with the same specification in the automobile manufacturing process are monitored, if abnormal hidden dangers exist in the welding parameters of the welding robot, the abnormal conditions are not found and adjusted in time, the intelligent adjustment is performed on the parameters during welding, the conditions that the abnormal conditions exist in the welding parameters of the welding robot, the abnormal conditions are not found in time are effectively prevented, the welding robot is guaranteed to weld efficiently, and efficient production of automobile manufacturing is further facilitated.
Description
Technical Field
The application relates to the technical field of intelligent welding in automobile manufacturing, in particular to a parameter adjusting method and device for industrial production.
Background
Industrial welding refers to the use of various welding methods and techniques in industrial production to join together parts of metallic or non-metallic materials to form a durable weld that meets specific manufacturing, construction, or repair requirements. Welding is a common metal connection technology and is widely applied to various industrial fields including automobile manufacturing, construction, aerospace, energy industry, electronic manufacturing and the like.
Automobile manufacturing involves a large number of welding tasks, including body welding, chassis welding, exhaust system welding, etc., and since automobile manufacturing involves a large number of welding jobs, welding robots can provide efficient, accurate and stable welding solutions, the prior art widely employs welding robots for intelligent welding.
The welding robot generally carries out intelligent adjustment on parameters during welding when carrying out intelligent welding, and an intelligent welding system combines sensor technology, data analysis and automatic control so as to realize a more accurate, efficient and stable welding process.
The prior art has the following defects: in the automobile manufacturing process, when the welding robot performs intelligent welding on parts with the same specification, if welding parameters of the welding robot are abnormal but not found and adjusted in time, mass welding quality unqualified products can be caused in welding, so that efficient production of automobile manufacturing is inconvenient.
The above information disclosed in the background section is only for enhancement of understanding of the background of the disclosure and therefore it may include information that does not form the prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
The application aims to provide a parameter adjustment method and device for industrial production, which are used for monitoring parameters when a welding robot performs intelligent welding on parts with the same specification in the automobile manufacturing process, and performing intelligent adjustment on parameters in welding in time when abnormal welding parameters of the welding robot are abnormal but are not found and adjusted in time if abnormal hidden dangers exist in the welding parameters of the welding robot, so that the welding robot is ensured to perform efficient welding, and efficient production of automobile manufacturing is further facilitated, and the problems in the background technology are solved.
In order to achieve the above object, the present application provides the following technical solutions: a parameter adjusting method and device for industrial production comprises the following steps:
s101, collecting multiple information, including welding physical parameter information and software control information, of a welding robot in the process of manufacturing the automobile for intelligent welding of parts of the same specification, and processing the welding physical parameter information and the software control information in the process of welding after the information is collected;
s102, comprehensively analyzing the processed welding physical parameter information and software control information when the welding robot performs intelligent welding in the automobile manufacturing process, and generating hidden danger assessment indexes;
s103, comparing and analyzing a hidden danger assessment index generated when the welding robot performs intelligent welding in the automobile manufacturing process with a preset hidden danger assessment index reference threshold value to generate a high hidden danger risk signal and a low hidden danger risk signal;
and S104, when the welding robot generates a high hidden danger risk signal during intelligent welding in the automobile manufacturing process, intelligent adjustment is performed on parameters during welding in time, comprehensive analysis is performed on hidden danger assessment indexes output after adjustment, an adjustment result signal is generated, and different early warning prompts or no early warning prompt is sent out on the adjustment result signal.
Preferably, the welding physical parameter information of the welding robot in the automobile manufacturing process when the welding robot intelligently welds parts with the same specification comprises a welding current abnormal hiding coefficient and a welding speed fluctuation coefficient, and after acquisition, the welding current abnormal hiding coefficient and the welding speed fluctuation coefficient are respectively calibrated to alpha I And beta V The software control information when the welding robot performs intelligent welding on parts with the same specification in the automobile manufacturing process comprises a control path deviation coefficient, and after acquisition, the control path deviation coefficient is calibrated to be gamma S 。
Preferably, the logic for obtaining the welding current anomaly concealment coefficient is as follows:
a100, obtaining a welding robot pairThe optimal welding current reference range when intelligent welding is carried out on parts with the same specification, and the optimal welding current reference range is marked as I min ～I max ；
A200, acquiring actual welding current values of the welding robot at different moments in T time when the welding robot performs intelligent welding, and calibrating the actual welding current values as I x X represents the number of the actual welding current values at different moments in the T time when the welding robot performs intelligent welding, and x=1, 2, 3, 4, … … and m are positive integers;
a300, the actual welding current value obtained in the T time is compared with the optimal welding current reference range I min ～I max Comparing and will not be in the optimal welding current reference range I min ～I max The actual welding current value between is defined as non-optimal current value, and the non-optimal current value is calibrated as I y Y represents the number of the non-optimal current value obtained in the T time, y=1, 2, 3, 4, … …, n being a positive integer;
a400, calculating a welding current abnormal hiding coefficient, wherein the calculated expression is as follows: [t1，t2]for the period of time of the non-optimal current value acquired in time T, T1 < T2, < ->
Preferably, the logic for obtaining the welding speed fluctuation coefficient is as follows:
b100, acquiring actual average welding rates of different time periods in the T time when the welding robot intelligently welds parts of the same specification, and calibrating the actual welding rates as V k K represents that the welding robot performs intellectualization on parts with the same specificationThe number of the actual average welding rate of different periods in the T time during welding, wherein k=1, 2, 3, 4, … … and N, and N is a positive integer;
b200, calculating standard deviations of actual average welding rates of different periods of time in T time when the welding robot performs intelligent welding, and calibrating the standard deviations of the actual average welding rates as R, wherein the standard deviations are as follows:
；
wherein V is Flat plate For the average value of the actual average welding rates of different time periods in the T time when the welding robot performs intelligent welding, the obtained calculation formula is as follows:
b300, calculating a welding speed fluctuation coefficient, wherein the calculated expression is as follows: beta V ＝ln(R 2 +1)。
Preferably, the logic for controlling the acquisition of the path deviation coefficient is as follows:
c100, acquiring an actual software control welding path and a pre-planned welding path when the welding robot performs intelligent welding, and marking the actual software control welding path and the pre-planned welding path as L1 and L2 respectively;
200, dividing an actual software control welding path L1 and a pre-planned welding path L2 when the welding robot performs intelligent welding into a plurality of points according to time points, overlapping the points on the actual software control welding path L1 and the pre-planned welding path L2 according to the time points, and comparing and analyzing;
c300, inputting the actual software control welding path L1 and the pre-planned welding path L2 into a two-dimensional coordinate system, calculating the distance between the point on the actual software control welding path L1 and the point on the pre-planned welding path L2 at the same moment by adopting Euclidean distance calculation, and calibrating the distance between the point on the actual software control welding path L1 and the point on the pre-planned welding path L2 at the same moment as L w W represents the number of the actual average welding rate of different time periods in the T time when the welding robot performs intelligent welding on parts with the same specification, w=1, 2, 3, 4, … … and p, and p is a positive integer;
c400, calculating a control path deviation coefficient, wherein the calculated expression is as follows:
preferably, the welding current abnormality hiding coefficient alpha is obtained I Coefficient of welding speed fluctuation beta V Control path deviation coefficient gamma S Then, a data analysis model is established according to the acquired data information to generate a hidden danger assessment index YH δ The formula according to is:
；
wherein f1, f2 and f3 are respectively the welding current anomaly hiding coefficients alpha I Coefficient of welding speed fluctuation beta V The control path deviation coefficient is calibrated to gamma S And f1, f2, f3 are all greater than 0.
Preferably, the hidden danger assessment index generated when the welding robot performs intelligent welding in the automobile manufacturing process is compared with a preset hidden danger assessment index reference threshold value for analysis, if the hidden danger assessment index is greater than or equal to the hidden danger assessment index reference threshold value, a high hidden danger risk signal is generated, and if the hidden danger assessment index is smaller than the hidden danger assessment index reference threshold value, a low hidden danger risk signal is generated.
Preferably, when a high hidden danger risk signal is generated during intelligent welding of a welding robot in the automobile manufacturing process, intelligent adjustment is performed on parameters during welding, a data set is established for a plurality of hidden danger evaluation indexes output after adjustment, the data set is calibrated to be P, and then P= { YH δ δ represents the number of hidden trouble assessment indices within the data set, δ=1, 2, 3, 4, … …, u being a positive integer;
calculating the average value and standard deviation of hidden danger evaluation indexes in a data set, and respectively comparing the average value and standard deviation of the hidden danger evaluation indexes with a preset hidden danger evaluation index reference threshold value and a preset standard deviation reference threshold value, wherein the comparison and the judgment are as follows:
if the average value of the hidden danger assessment indexes is larger than or equal to the reference threshold value of the hidden danger assessment indexes, generating a welding parameter adjustment failure signal, transmitting the signal to a prompt end, and sending an early warning prompt of the welding parameter adjustment failure through the prompt end to prompt related staff that the welding parameter adjustment fails and that the welding robot needs to be shut down and maintained;
if the average value of the hidden danger evaluation indexes is smaller than the reference threshold value of the hidden danger evaluation indexes and the standard deviation of the hidden danger evaluation indexes is larger than or equal to the reference threshold value of the standard deviation, generating a signal of unstable welding parameter adjustment, transmitting the signal to a prompt end, and sending an early warning prompt of unstable welding parameter adjustment through the prompt end to prompt relevant staff that the welding parameter adjustment is unstable, wherein shutdown maintenance is needed for the welding robot;
if the average value of the hidden danger evaluation indexes is smaller than the reference threshold value of the hidden danger evaluation indexes and the standard deviation of the hidden danger evaluation indexes is smaller than the reference threshold value of the standard deviation, a signal for successfully adjusting the welding parameters is generated, the signal is transmitted to the prompt terminal, and the early warning prompt is not sent out through the prompt terminal.
In the technical scheme, the application has the technical effects and advantages that:
according to the intelligent welding method, parameters of the welding robot during intelligent welding of parts with the same specification in the automobile manufacturing process are monitored, if abnormal hidden dangers exist in the welding parameters of the welding robot, which are not found and adjusted in time, the parameters during intelligent welding are adjusted in time, the occurrence of the conditions that the welding parameters of the welding robot are abnormal but not found in time is effectively prevented, the welding robot is guaranteed to weld efficiently, and efficient production of automobile manufacturing is further facilitated;
according to the intelligent welding robot parameter adjustment method, comprehensive analysis is performed on parameter adjustment conditions during intelligent welding of the welding robot, if adjustment failure or unstable adjustment occurs in parameter adjustment, relevant workers are timely informed of stopping maintenance of the welding robot, the welding robot is guaranteed to be in an efficient welding state, and secondly, when an early warning prompt is sent out, whether adjustment failure occurs or adjustment is unstable in parameter adjustment of the welding robot can be known, so that reasonable analysis is facilitated for possible faults of the welding robot by maintenance workers.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to these drawings for those skilled in the art.
FIG. 1 is a flow chart of a method for adjusting parameters for industrial production and a device thereof.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.
The application provides a parameter adjustment method for industrial production, which is shown in figure 1 and comprises the following steps:
s101, collecting multiple information, including welding physical parameter information and software control information, of a welding robot in the process of manufacturing the automobile for intelligent welding of parts of the same specification, and processing the welding physical parameter information and the software control information in the process of welding after the information is collected;
the welding physical parameter information of the welding robot during intelligent welding of parts with the same specification in the automobile manufacturing process comprises a welding current abnormal hiding coefficient and a welding speed fluctuation coefficient, and after acquisition, the welding current abnormal hiding coefficient is obtainedAnd the fluctuation coefficient of the welding speed is respectively calibrated as alpha I And beta V ；
The abnormal current in the welding process may have a serious influence on the welding quality of the parts, the current is one of the key parameters in the welding process, the formation of a welding line, the flow of molten metal and the stability of the welding quality are directly influenced, and the following is a serious influence possibly caused by the abnormal current:
weld joint is not filled: if the current is too low, the flow capacity of the molten metal is reduced, resulting in an insufficient weld, which may result in an insufficient weld strength, affecting the reliability of the welded connection;
air holes and slag inclusion: current anomalies may cause the gases in the molten metal to not be effectively vented, forming air holes or inclusions in the weld, which can significantly reduce the strength and tightness of the weld;
cold cracking: too high a current may cause too rapid cooling of the weld area, resulting in cold cracking, which may form cracks in the weld, severely affecting the weld quality and strength;
welding deformation: current anomalies can cause excessive thermalization of the weld area, causing weld distortion, affecting the size and shape of the part;
the welding strength is reduced: improper current may result in reduced strength of the weld, affecting the overall performance and durability of the part;
uneven weld joint: current anomalies can lead to non-uniformity in weld width and depth, affecting consistency in weld quality;
product quality problem: poor weld quality can lead to product quality problems affecting the performance, safety and reliability of the overall vehicle;
therefore, the welding current during intelligent welding of the welding robot is monitored, and the problem of abnormal welding current can be found in time;
the logic for obtaining the welding current abnormal hiding coefficient is as follows:
a100, acquiring an optimal welding current reference range when the welding robot performs intelligent welding on parts with the same specification, and performing optimal welding currentThe reference range is marked as I min ～I max ；
It should be noted that, before actual production, performing a welding test is an effective way to obtain the optimal parameters, and through the test, different welding parameter combinations can be tried, and an optimal welding current reference range suitable for the parts with the specification can be found therefrom, and the optimal welding current reference range is not specifically limited herein and can be adjusted according to the parts with different specifications;
a200, acquiring actual welding current values of the welding robot at different moments in T time when the welding robot performs intelligent welding, and calibrating the actual welding current values as I x X represents the number of the actual welding current values at different moments in the T time when the welding robot performs intelligent welding, and x=1, 2, 3, 4, … … and m are positive integers;
in the welding process, a current sensor can be installed to monitor the welding current value of the welding robot in real time when intelligent welding is performed, and the actual value of the welding current is transmitted to a monitoring system through the current sensor, so that current information can be obtained in real time;
a300, the actual welding current value obtained in the T time is compared with the optimal welding current reference range I min ～I max Comparing and will not be in the optimal welding current reference range I min ～I max The actual welding current value between is defined as non-optimal current value, and the non-optimal current value is calibrated as I y Y represents the number of the non-optimal current value obtained in the T time, y=1, 2, 3, 4, … …, n being a positive integer;
a400, calculating a welding current abnormal hiding coefficient, wherein the calculated expression is as follows: [t1，t2]for the period of time of the non-optimal current value acquired in time T, T1 < T2, < ->
The calculation expression of the welding current abnormal hidden coefficient shows that the larger the expression value of the welding current abnormal hidden coefficient generated when the welding robot runs in the T time during intelligent welding is, the larger the hidden danger that the welding parameters of the welding robot are abnormal but not found in time for adjustment is indicated, otherwise, the smaller the hidden danger that the welding parameters of the welding robot are abnormal but not found in time for adjustment is indicated;
the welding rate refers to the distance that a welding robot moves in unit time in the welding process, when the welding robot performs intelligent welding on parts of the same specification in the automobile manufacturing process, the welding rate is the speed of a welding seam when the welding robot completes welding operation, the stability of the welding rate is a key parameter in the welding process, the formation of a welding pool, the distribution of welding temperature and the stability of welding quality are affected, and the instability of the welding rate may cause the following serious influence on the welding quality of the parts:
uneven weld joint: instability in the welding rate can lead to variations in the width and depth of the weld, thereby making the weld non-uniform, which can reduce the consistency and reliability of the weld;
welding defects: unstable welding speed can lead to unstable formation of a welding pool, so that defects such as air holes, slag inclusion and the like are caused, and the welding quality is influenced;
welding deformation: instability in the welding rate may cause the cooling rate of the weld puddle to vary in different areas, thereby causing welding distortion, affecting the size and shape of the part;
the welding strength is reduced: unstable welding rates may lead to non-uniformity of the metal structure in the weld puddle, thereby affecting consistency and stability of the weld strength;
the weld quality is reduced: instability in the welding rate may cause non-uniformity of the metal composition in the weld pool, thereby affecting the performance and quality of the weld;
abnormal welding temperature: unstable welding speed can lead to uneven temperature distribution of a welding pool, abnormal welding temperature is caused, and welding quality and strength are affected;
unqualified welding quality: instability of the welding rate may cause unsatisfactory welding quality, so that the welded parts cannot pass quality inspection, and the quality of the overall product is affected;
therefore, the welding rate during intelligent welding of the welding robot is monitored, and the problem of abnormal welding rate can be found in time;
the welding speed fluctuation coefficient is obtained by the following logic:
b100, obtaining actual average welding speed of the welding robot in different time periods (the time in the selected time periods can be all equal or all unequal or in a crossed mode) within T time when the welding robot performs intelligent welding on parts of the same specification, and calibrating the actual welding speed as V k K represents the number of the actual average welding rate of different time periods in T time when the welding robot performs intelligent welding on parts with the same specification, and k=1, 2, 3, 4, … … and N are positive integers;
the displacement sensor or the encoder is arranged in the welding process, so that the moving distance and the moving speed of the welding robot can be monitored in real time, and the actual average welding rate of different time periods can be calculated;
b200, calculating standard deviations of actual average welding rates of different periods of time in T time when the welding robot performs intelligent welding, and calibrating the standard deviations of the actual average welding rates as R, wherein the standard deviations are as follows:
；
wherein V is Flat plate For the average value of the actual average welding speed of different time periods in T time when the intelligent welding is carried out by the welding robot, the obtained meterThe calculation formula is as follows:
the larger the expression value of the actual average welding rate standard deviation R is, the worse the stability of the actual average welding rate of the welding robot in different time periods in the T time is shown, otherwise, the better the stability of the actual average welding rate of the welding robot in different time periods in the T time is shown;
b300, calculating a welding speed fluctuation coefficient, wherein the calculated expression is as follows: beta V ＝ln(R 2 +1)；
The calculation expression of the welding speed fluctuation coefficient shows that the larger the expression value of the welding speed fluctuation coefficient generated when the welding robot runs in the T time during intelligent welding is, the larger the hidden danger that the welding parameters of the welding robot are abnormal but not found in time for adjustment is, otherwise, the smaller the hidden danger that the welding parameters of the welding robot are abnormal but not found in time for adjustment is;
the software control information when the welding robot performs intelligent welding on parts of the same specification in the automobile manufacturing process comprises a control path deviation coefficient, and after acquisition, the control path deviation coefficient is calibrated to be gamma S ；
When the welding robot performs intelligent welding on parts with the same specification in the automobile manufacturing process, a serious welding quality problem may be caused by deviation between a software-controlled welding path and a pre-planned welding path, and the following may be the influence caused by the deviation:
weld mismatch: if the software-controlled welding path deviates from the pre-planned welding path, the welding seam may be mismatched, so that the width and depth of the welding seam are inconsistent, and the strength and consistency of the welding seam are affected;
uneven weld gap: the deviation can cause inconsistent weld gaps in different areas, so that insufficient welding filling is caused, and the quality and appearance of the weld are affected;
welding deformation: the deviation between the software control path and the pre-planned path may cause uneven heating distribution of the welding area, cause welding deformation, and influence the size and shape of the part;
the weld quality is reduced: the deviation may cause inaccurate positions of the welding gun or welding wire and the workpiece during the welding process, thereby affecting the formation and quality of the weld;
the welding strength is reduced: the deviation may cause insufficient melting and bonding of the partial areas in the welding path, resulting in a decrease in welding strength;
air holes and slag inclusion: the deviation of the software control path can cause that the gas in the welding seam can not be smoothly discharged, so that air holes and slag inclusion are formed, and the quality of the welding seam is affected;
therefore, the abnormal problem of deviation between the software-controlled welding path and the pre-planned welding path can be timely found by monitoring the software-controlled welding path when the intelligent welding is carried out on the welding robot;
the logic for controlling the acquisition of the path deviation coefficient is as follows:
c100, acquiring an actual software control welding path and a pre-planned welding path when the welding robot performs intelligent welding, and marking the actual software control welding path and the pre-planned welding path as L1 and L2 respectively;
it should be noted that, the welding robot is usually equipped with a sensor and a scanning technology, and can scan the surface of the part in real time, and automatically generate a welding path according to the scanning result;
200, dividing an actual software control welding path L1 and a pre-planned welding path L2 when the welding robot performs intelligent welding into a plurality of points according to time points, overlapping the points on the actual software control welding path L1 and the pre-planned welding path L2 according to the time points, and comparing and analyzing;
it should be noted that, openCV (open source computer vision library) is a widely used open source image processing library, which can be used for analyzing and processing image data in a real-time welding process, and through OpenCV, image information of a welding path and a pre-planned path can be obtained in real time, and compared and analyzed;
c300, inputting the actual software control welding path L1 and the pre-planned welding path L2 into a two-dimensional coordinate system, calculating the distance between the point on the actual software control welding path L1 and the point on the pre-planned welding path L2 at the same moment by adopting Euclidean distance calculation, and calibrating the distance between the point on the actual software control welding path L1 and the point on the pre-planned welding path L2 at the same moment as L w W represents the number of the actual average welding rate of different time periods in the T time when the welding robot performs intelligent welding on parts with the same specification, w=1, 2, 3, 4, … … and p, and p is a positive integer;
it should be noted that, euclidean distance is the most common distance calculation method, and is applicable to two-dimensional or three-dimensional coordinate points;
c400, calculating a control path deviation coefficient, wherein the calculated expression is as follows:
the calculation expression of the control path deviation coefficient shows that the larger the expression value of the control path deviation coefficient generated when the welding robot runs in the T time during intelligent welding is, the larger the hidden danger that the welding parameters of the welding robot are abnormal but not found in time for adjustment is, otherwise, the smaller the hidden danger that the welding parameters of the welding robot are abnormal but not found in time for adjustment is;
s102, comprehensively analyzing the processed welding physical parameter information and software control information when the welding robot performs intelligent welding in the automobile manufacturing process, and generating hidden danger assessment indexes;
obtaining the welding current abnormal hiding coefficient alpha I Coefficient of welding speed fluctuation beta V Control path deviation coefficient gamma S Then, a data analysis model is established according to the acquired data information to generate a hidden danger assessment index YH δ The formula according to is:
；
wherein f1, f2 and f3 are respectively the welding current anomaly hiding coefficients alpha I Coefficient of welding speed fluctuation beta V The control path deviation coefficient is calibrated to gamma S F1, f2, f3 are all greater than 0;
the calculation formula shows that the greater the abnormal hidden coefficient of the welding current generated when the welding robot runs in the T time, the greater the fluctuation coefficient of the welding speed and the greater the deviation coefficient of the control path are, namely the hidden danger assessment index YH generated when the welding robot runs in the T time when the welding robot carries out intelligent welding δ The larger the expression value of (2) is, the larger the hidden danger that the welding parameters of the welding robot are abnormal but not found in time to be adjusted is, the smaller the welding current abnormal hidden coefficient generated when the welding robot runs in the T time during intelligent welding is, the smaller the welding speed fluctuation coefficient is, the smaller the control path deviation coefficient is, namely the hidden danger assessment index YH generated when the welding robot runs in the T time during intelligent welding is δ The smaller the expression value of the welding robot is, the smaller the hidden trouble that the welding parameters of the welding robot are abnormal but not found in time to be adjusted is;
s103, comparing and analyzing a hidden danger assessment index generated when the welding robot performs intelligent welding in the automobile manufacturing process with a preset hidden danger assessment index reference threshold value to generate a high hidden danger risk signal and a low hidden danger risk signal;
comparing and analyzing a hidden danger assessment index generated when the welding robot performs intelligent welding in the automobile manufacturing process with a preset hidden danger assessment index reference threshold, generating a high hidden danger risk signal if the hidden danger assessment index is greater than or equal to the hidden danger assessment index reference threshold, and generating a low hidden danger risk signal if the hidden danger assessment index is less than the hidden danger assessment index reference threshold;
s104, when a high hidden danger risk signal is generated during intelligent welding of the welding robot in the automobile manufacturing process, intelligent adjustment is timely performed on parameters during welding, comprehensive analysis is performed on hidden danger assessment indexes output after adjustment, an adjustment result signal is generated, and different early warning prompts or no early warning prompt is sent out on the adjustment result signal;
when a high hidden danger risk signal is generated when a welding robot performs intelligent welding in the automobile manufacturing process, intelligent adjustment is performed on parameters during welding, a data set is established for a plurality of hidden danger evaluation indexes output after adjustment, and the data set is calibrated to be P, so that P= { YH is obtained δ δ represents the number of hidden trouble assessment indices within the data set, δ=1, 2, 3, 4, … …, u being a positive integer;
calculating the average value and standard deviation of hidden danger evaluation indexes in a data set, and respectively comparing the average value and standard deviation of the hidden danger evaluation indexes with a preset hidden danger evaluation index reference threshold value and a preset standard deviation reference threshold value, wherein the comparison and the judgment are as follows:
if the average value of the hidden danger assessment indexes is larger than or equal to the reference threshold value of the hidden danger assessment indexes, generating a welding parameter adjustment failure signal, transmitting the signal to a prompt end, and sending an early warning prompt of the welding parameter adjustment failure through the prompt end to prompt related staff that the welding parameter adjustment fails and that the welding robot needs to be shut down and maintained;
if the average value of the hidden danger evaluation indexes is smaller than the reference threshold value of the hidden danger evaluation indexes and the standard deviation of the hidden danger evaluation indexes is larger than or equal to the reference threshold value of the standard deviation, generating a signal of unstable welding parameter adjustment, transmitting the signal to a prompt end, and sending an early warning prompt of unstable welding parameter adjustment through the prompt end to prompt relevant staff that the welding parameter adjustment is unstable, wherein shutdown maintenance is needed for the welding robot;
if the average value of the hidden danger evaluation indexes is smaller than the reference threshold value of the hidden danger evaluation indexes and the standard deviation of the hidden danger evaluation indexes is smaller than the reference threshold value of the standard deviation, a signal for successfully adjusting the welding parameters is generated, the signal is transmitted to the prompt terminal, and the early warning prompt is not sent out through the prompt terminal.
According to the intelligent welding method, parameters of the welding robot during intelligent welding of parts with the same specification in the automobile manufacturing process are monitored, if abnormal hidden dangers exist in the welding parameters of the welding robot, which are not found and adjusted in time, the parameters during intelligent welding are adjusted in time, the occurrence of the conditions that the welding parameters of the welding robot are abnormal but not found in time is effectively prevented, the welding robot is guaranteed to weld efficiently, and efficient production of automobile manufacturing is further facilitated;
according to the intelligent welding robot parameter adjustment method, comprehensive analysis is performed on parameter adjustment conditions during intelligent welding of the welding robot, if adjustment failure or unstable adjustment occurs in parameter adjustment, relevant workers are timely informed of stopping maintenance of the welding robot, the welding robot is guaranteed to be in an efficient welding state, and secondly, when an early warning prompt is sent out, whether adjustment failure occurs or adjustment is unstable in parameter adjustment of the welding robot can be known, so that reasonable analysis is facilitated for possible faults of the welding robot by maintenance workers.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired or wireless means (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the several embodiments provided by the present application, it should be understood that the disclosed systems and methods may be implemented in other ways. For example, the embodiments described above are merely illustrative, e.g., the division of the elements is merely a logical functional division, and there may be additional divisions in actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
While certain exemplary embodiments of the present application have been described above by way of illustration only, it will be apparent to those of ordinary skill in the art that modifications may be made to the described embodiments in various different ways without departing from the spirit and scope of the application. Accordingly, the drawings and description are to be regarded as illustrative in nature and not as restrictive of the scope of the application, which is defined by the appended claims.
Claims (9)
1. The parameter adjustment method for industrial production is characterized by comprising the following steps of:
s101, collecting multiple information, including welding physical parameter information and software control information, of a welding robot in the process of manufacturing the automobile for intelligent welding of parts of the same specification, and processing the welding physical parameter information and the software control information in the process of welding after the information is collected;
s102, comprehensively analyzing the processed welding physical parameter information and software control information when the welding robot performs intelligent welding in the automobile manufacturing process, and generating hidden danger assessment indexes;
s103, comparing and analyzing a hidden danger assessment index generated when the welding robot performs intelligent welding in the automobile manufacturing process with a preset hidden danger assessment index reference threshold value to generate a high hidden danger risk signal and a low hidden danger risk signal;
and S104, when the welding robot generates a high hidden danger risk signal during intelligent welding in the automobile manufacturing process, intelligent adjustment is performed on parameters during welding in time, comprehensive analysis is performed on hidden danger assessment indexes output after adjustment, an adjustment result signal is generated, and different early warning prompts or no early warning prompt is sent out on the adjustment result signal.
2. The method for adjusting parameters for industrial production according to claim 1, wherein the welding physical parameter information when the welding robot performs intelligent welding on the parts with the same specification in the automobile manufacturing process comprises a welding current abnormality hiding coefficient and a welding speed fluctuation coefficient, and after the acquisition, the welding current abnormality hiding coefficient and the welding speed fluctuation coefficient are respectively calibrated to be alpha I And beta V The software control information when the welding robot performs intelligent welding on parts with the same specification in the automobile manufacturing process comprises a control path deviation coefficient, and after acquisition, the control path deviation coefficient is used for controllingCalibrated as gamma S 。
3. The method for adjusting parameters for industrial production according to claim 2, wherein the logic for obtaining the welding current anomaly concealment coefficient is as follows:
a100, acquiring an optimal welding current reference range when the welding robot performs intelligent welding on parts with the same specification, and calibrating the optimal welding current reference range as I min ～I max ；
A200, acquiring actual welding current values of the welding robot at different moments in T time when the welding robot performs intelligent welding, and calibrating the actual welding current values as I x X represents the number of the actual welding current values at different moments in the T time when the welding robot performs intelligent welding, and x=1, 2, 3, 4, … … and m are positive integers;
a300, the actual welding current value obtained in the T time is compared with the optimal welding current reference range I min ～I max Comparing and will not be in the optimal welding current reference range I min ～I max The actual welding current value between is defined as non-optimal current value, and the non-optimal current value is calibrated as I y Y represents the number of the non-optimal current value obtained in the T time, y=1, 2, 3, 4, … …, n being a positive integer;
a400, calculating a welding current abnormal hiding coefficient, wherein the calculated expression is as follows: [t1，t2]for the period of time of the non-optimal current value acquired in time T, T1 < T2, < ->
4. A method for adjusting parameters for industrial production according to claim 3, wherein the logic for obtaining the welding speed fluctuation coefficient is as follows:
b100, acquiring actual average welding rates of different time periods in the T time when the welding robot intelligently welds parts of the same specification, and calibrating the actual welding rates as V k K represents the number of the actual average welding rate of different time periods in T time when the welding robot performs intelligent welding on parts with the same specification, and k=1, 2, 3, 4, … … and N are positive integers;
b200, calculating standard deviations of actual average welding rates of different periods of time in T time when the welding robot performs intelligent welding, and calibrating the standard deviations of the actual average welding rates as R, wherein the standard deviations are as follows:
；
wherein V is Flat plate For the average value of the actual average welding rates of different time periods in the T time when the welding robot performs intelligent welding, the obtained calculation formula is as follows:
b300, calculating a welding speed fluctuation coefficient, wherein the calculated expression is as follows: beta V ＝ln(R 2 +1)。
5. The method for adjusting parameters for industrial production according to claim 4, wherein the logic for controlling the acquisition of the path deviation coefficient is as follows:
c100, acquiring an actual software control welding path and a pre-planned welding path when the welding robot performs intelligent welding, and marking the actual software control welding path and the pre-planned welding path as L1 and L2 respectively;
200, dividing an actual software control welding path L1 and a pre-planned welding path L2 when the welding robot performs intelligent welding into a plurality of points according to time points, overlapping the points on the actual software control welding path L1 and the pre-planned welding path L2 according to the time points, and comparing and analyzing;
c300, inputting the actual software control welding path L1 and the pre-planned welding path L2 into a two-dimensional coordinate system, calculating the distance between the point on the actual software control welding path L1 and the point on the pre-planned welding path L2 at the same moment by adopting Euclidean distance calculation, and calibrating the distance between the point on the actual software control welding path L1 and the point on the pre-planned welding path L2 at the same moment as L w W represents the number of the actual average welding rate of different time periods in the T time when the welding robot performs intelligent welding on parts with the same specification, w=1, 2, 3, 4, … … and p, and p is a positive integer;
c400, calculating a control path deviation coefficient, wherein the calculated expression is as follows:
6. the method for adjusting parameters for industrial production according to claim 5, wherein a welding current abnormality hiding coefficient αα is obtained I Coefficient of welding speed fluctuation beta V Control path deviation coefficient gamma S Then, a data analysis model is established according to the acquired data information to generate a hidden danger assessment index YH δ The formula according to is:
；
wherein f1, f2 and f3 are respectively the welding current anomaly hiding coefficients alpha I Coefficient of welding speed fluctuation beta V The control path deviation coefficient is calibrated to gamma S And f1, f2, f3 are all greater than 0.
7. The method for adjusting parameters for industrial production according to claim 6, wherein a hidden danger assessment index generated when the welding robot performs intelligent welding in the automobile manufacturing process is compared with a hidden danger assessment index reference threshold value set in advance for analysis, if the hidden danger assessment index is greater than or equal to the hidden danger assessment index reference threshold value, a high hidden danger risk signal is generated, and if the hidden danger assessment index is smaller than the hidden danger assessment index reference threshold value, a low hidden danger risk signal is generated.
8. The method for adjusting parameters for industrial production according to claim 7, wherein when a high risk signal is generated during intelligent welding by a welding robot during the manufacture of an automobile, the parameters during welding are intelligently adjusted, a data set is established for a plurality of risk assessment indexes output after adjustment, the data set is calibrated to be P, and then p= { YH δ δ represents the number of hidden trouble assessment indices within the data set, δ=1, 2, 3, 4, … …, u being a positive integer;
calculating the average value and standard deviation of hidden danger evaluation indexes in a data set, and respectively comparing the average value and standard deviation of the hidden danger evaluation indexes with a preset hidden danger evaluation index reference threshold value and a preset standard deviation reference threshold value, wherein the comparison and the judgment are as follows:
if the average value of the hidden danger assessment indexes is larger than or equal to the reference threshold value of the hidden danger assessment indexes, generating a welding parameter adjustment failure signal, transmitting the signal to a prompt end, and sending an early warning prompt of the welding parameter adjustment failure through the prompt end to prompt related staff that the welding parameter adjustment fails and that the welding robot needs to be shut down and maintained;
if the average value of the hidden danger evaluation indexes is smaller than the reference threshold value of the hidden danger evaluation indexes and the standard deviation of the hidden danger evaluation indexes is larger than or equal to the reference threshold value of the standard deviation, generating a signal of unstable welding parameter adjustment, transmitting the signal to a prompt end, and sending an early warning prompt of unstable welding parameter adjustment through the prompt end to prompt relevant staff that the welding parameter adjustment is unstable, wherein shutdown maintenance is needed for the welding robot;
if the average value of the hidden danger evaluation indexes is smaller than the reference threshold value of the hidden danger evaluation indexes and the standard deviation of the hidden danger evaluation indexes is smaller than the reference threshold value of the standard deviation, a signal for successfully adjusting the welding parameters is generated, the signal is transmitted to the prompt terminal, and the early warning prompt is not sent out through the prompt terminal.
9. An industrial process parameter adjustment device for performing an industrial process parameter adjustment method according to any one of the preceding claims 1-8.
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