CN-121988936-A - Digital twin monitoring method and device for flexible welding production line of semitrailer frame

CN121988936ACN 121988936 ACN121988936 ACN 121988936ACN-121988936-A

Abstract

The application relates to the technical field of intelligent manufacturing and welding automation, and discloses a digital twin monitoring method and device for a flexible welding production line of a semi-trailer frame. The method comprises the steps of collecting welding dynamic interference data and process parameters through a sensor, mapping and updating station states of a digital twin model, carrying out cluster analysis on the interference data, dividing a continuous monitoring stage, identifying a high risk stage, extracting interference characteristics of the high risk stage, determining key influence factors through a support vector machine model, generating a process parameter adjustment scheme, carrying out simulation iterative optimization and safety verification on a welding path to form a preliminary optimization scheme, combining resource allocation analysis, generating an equilibrium configuration scheme, synchronously updating the twin model and issuing the twin model to a production line, and carrying out iterative optimization on a quality prediction model according to a difference value by comparing the actual welding seam quality data with a model prediction value.

Inventors

YAO ZHIGANG
CHEN LONG
YAN LUGUANG
GAO SEN

Assignees

驻马店中天金骏车辆有限公司

Dates

Publication Date: 20260508
Application Date: 20260324

Claims (10)

1. A digital twin monitoring method for a flexible welding production line of a semitrailer frame, the method comprising: Step S1, acquiring dynamic interference data and technological parameters of a semitrailer frame in a welding process through a sensor, mapping the dynamic interference data and the technological parameters to a digital twin model, and updating state parameters of corresponding welding stations in the model; step S2, carrying out cluster analysis on the dynamic interference data, determining spatial distribution characteristics and time aggregation characteristics of interference, automatically dividing a welding process into a plurality of continuous monitoring stages according to the spatial distribution characteristics and the time aggregation characteristics, and identifying high-risk stages; step S3, determining a corresponding relation between the interference feature and the technological parameter by utilizing a pre-trained support vector machine model aiming at the high risk stage, determining a key influence feature based on the corresponding relation, and generating a corresponding technological parameter adjustment scheme aiming at the key influence feature; s4, performing simulation and iterative optimization on the welding path according to the process parameter adjustment scheme to obtain an optimized welding path, and performing security verification on the optimized welding path to obtain a preliminary path optimization scheme; S5, analyzing the resource allocation status of each welding stage according to the preliminary path optimization scheme, judging whether the resources are required to be reallocated, generating a balanced resource allocation scheme, updating the process parameters in the digital twin model, and transmitting the updated process parameters to an actual welding production line; And S6, acquiring weld quality data after actual welding operation at intervals of preset time, comparing the actual weld quality data with a predicted value output by a quality prediction model deployed in a digital twin model, and iteratively updating the quality prediction model according to a difference value of comparison results.
2. The digital twin monitoring method for a flexible welding line of semitrailer frames according to claim 1, wherein step S1 comprises: Acquiring dynamic interference data and process parameter values in the welding process in real time through a sensor, wherein the dynamic interference data comprise arc voltage fluctuation, wire feeding speed change, weld tracking deviation, interlayer temperature gradient and groove gap size, and the process parameter values comprise welding current parameters and welding speed parameters; Synchronously transmitting the acquired dynamic interference data and technological parameter values to a preset digital twin model, and updating state parameters of corresponding welding stations in the digital twin model according to the transmitted data; And reflecting the real-time change condition in the welding process through the state parameters to form a virtual mapping result consistent with the actual welding process.
3. The digital twin monitoring method for a flexible welding line of semitrailer frames according to claim 1, wherein determining the spatial distribution and temporal aggregation characteristics of the disturbances in step S2 comprises: performing cluster analysis on arc voltage fluctuation, weld tracking deviation and interlayer temperature gradient mapped to the digital twin model to generate corresponding grouping labels; calculating a clustering center of each welding station according to the grouping labels, mapping the clustering center to a production line layout coordinate system, and generating a spatial distribution feature vector containing interference intensity values of each station; And calculating the time stamp continuity of each cluster group according to the grouping label, determining the aggregation time period of the interference in the time dimension, and generating a time aggregation feature sequence containing the aggregation time period of each cluster group.
4. The digital twin monitoring method for a flexible welding line of semitrailer frames according to claim 1, characterized in that the identification of the high risk phase in step S2 comprises: Dividing the welding process into a plurality of monitoring stages according to the spatial distribution characteristics and the time aggregation characteristics of the interference; Calculating an arc voltage fluctuation amplitude and a weld tracking deviation average value for each monitoring stage; And if the arc voltage fluctuation amplitude and the weld joint tracking deviation average value exceed the preset threshold, marking the corresponding monitoring stage as a high risk stage.
5. The digital twin monitoring method for a flexible welding line of semitrailer frames according to claim 1, wherein step S3 comprises: Extracting a wire feeding speed change rule, an interlayer temperature gradient curve and a groove gap size at the high risk stage, and inputting the rule, the interlayer temperature gradient curve and the groove gap size into a support vector machine model as input features; Training the corresponding relation between the wire feeding speed change rule, the interlayer temperature gradient curve and the groove gap size and the welding current parameter and the welding speed parameter respectively through a support vector machine model, and establishing a mapping relation between the interference characteristic and the technological parameter; And according to the mapping relation, analyzing the contribution degree of the input features to the output parameters, identifying key influence features, determining a welding current and welding speed adjustment rule aiming at the key influence features, and generating a process parameter adjustment scheme through the adjustment rule.
6. The digital twin monitoring method for a flexible welding line of semitrailer frames according to claim 1, wherein step S4 comprises: simulating welding path change in a digital twin model according to the process parameter adjustment scheme, and optimizing a welding path by adopting an iterative calculation method; Identifying an interference area in the welding path through virtual collision detection, adjusting the welding path according to boundary coordinates of the interference area, avoiding potential collision risks, and generating an optimized welding path; And carrying out full-path simulation again based on the optimized welding path, detecting whether an undegraded interference point or a new collision risk exists, and if so, continuing iterative optimization until the welding path meets the safety requirement, thereby obtaining a preliminary path optimization scheme.
7. The digital twin monitoring method for a flexible welding line of semitrailer frames according to claim 6, wherein identifying interference areas in the welding path by virtual collision detection comprises: And constructing a three-dimensional welding environment in the digital twin model, introducing dynamic interference data as an interference source, detecting the intersection condition of the path points and the obstacles by using a ray casting algorithm, and generating boundary coordinates of the interference region.
8. The digital twin monitoring method for a flexible welding line of semitrailer frames according to claim 1, wherein step S5 comprises: Analyzing the resource allocation conditions of welding current and welding speed from the preliminary path optimization scheme, and judging whether the current allocation current is insufficient or not according to the current demand threshold value in the high risk stage; if the current resources allocated in the high-risk stage are insufficient, reallocating the current resources in the low-risk stage to the high-risk stage, and correspondingly adjusting the welding speed based on a wire feeding speed change rule to generate a balanced current and speed resource allocation scheme; And updating welding current parameters and welding speed parameters in the digital twin model through the resource allocation scheme, and transmitting the updated process parameters to an actual welding production line to drive welding operation.
9. The digital twin monitoring method for a flexible welding line of semitrailer frames according to claim 1, wherein step S6 comprises: And driving actual welding operation according to the updated process parameter set, collecting actual welding seam quality data, comparing the actual welding seam quality data with a welding seam quality predicted value output by a quality prediction model deployed in a digital twin model, and calculating the difference value of each quality index, wherein the quality prediction model takes welding current parameters, welding speed parameters and groove gap size as input and the probability of various quality indexes as output data, and predicting the welding seam quality of the next round of welding process in real time by using the updated quality prediction model and outputting the predicted result to a monitoring interface.
10. A digital twin monitoring device for a flexible welding line of a semitrailer frame for implementing a digital twin monitoring method for a flexible welding line of a semitrailer frame as claimed in any one of claims 1-9, characterized in that the device comprises: The building module is used for acquiring dynamic interference data and technological parameters of the semitrailer frame in the welding process through the sensor, mapping the dynamic interference data and the technological parameters to the digital twin model and updating state parameters of corresponding welding stations in the model; The identification module is used for carrying out cluster analysis on the dynamic interference data, determining spatial distribution characteristics and time aggregation characteristics of interference, automatically dividing the welding process into a plurality of continuous monitoring stages according to the spatial distribution characteristics and the time aggregation characteristics, and identifying high-risk stages; The adjusting module is used for determining the corresponding relation between the interference characteristic and the technological parameter by utilizing a pre-trained support vector machine model aiming at the high risk stage, determining the key influence characteristic based on the corresponding relation, and generating a corresponding technological parameter adjusting scheme aiming at the key influence characteristic; the optimizing module is used for carrying out simulation and iterative optimization on the welding path according to the process parameter adjusting scheme to obtain an optimized welding path, and carrying out safety verification on the optimized welding path to obtain a preliminary path optimizing scheme; The updating module is used for analyzing the resource allocation status of each welding stage according to the preliminary path optimization scheme, judging whether the resources are required to be reallocated, generating a balanced resource allocation scheme, updating the process parameters in the digital twin model, and transmitting the updated process parameters to an actual welding production line; The monitoring module is used for acquiring weld quality data after actual welding operation at intervals of preset time, comparing the actual weld quality data with a predicted value output by a quality prediction model deployed in the digital twin model, and iteratively updating the quality prediction model according to a difference value of comparison results.

Description

Digital twin monitoring method and device for flexible welding production line of semitrailer frame Technical Field The application relates to the technical field of intelligent manufacturing and welding automation, in particular to a digital twin monitoring method and device for a flexible welding production line of a semi-trailer frame. Background The semitrailer is used as core equipment for road transportation, and the welding quality of the frame of the semitrailer is directly related to the structural strength and the service life of the vehicle. Along with the promotion of intelligent upgrading in manufacturing industry, the welding production line of the semi-trailer frame is gradually developed to the flexible and automatic directions so as to meet the production requirements of multiple varieties and small batches. However, in the flexible welding production process, due to the influence of factors such as workpiece feeding deviation, clamping precision fluctuation, thermal deformation accumulation, equipment state change and the like, the welding process is often accompanied by various dynamic disturbances such as arc voltage fluctuation, wire feeding speed change, weld tracking deviation, interlayer temperature gradient abnormality and the like. The dynamic interference has complex coupling relation with technological parameters such as welding current, welding speed and the like, and the non-uniformity of space-time distribution is presented along with the progress of welding, so that the stability control of welding quality is provided with a serious challenge. Currently, monitoring methods for welding production lines are mainly focused on threshold value alarming of a single process parameter or welding line appearance detection based on machine vision. The existing method has the following technical defects that firstly, the traditional monitoring system is difficult to carry out deep fusion analysis on multi-source dynamic interference data, the distribution rule of interference on the layout space of a production line and the aggregation characteristic on a welding time axis cannot be accurately described, so that the state of the welding process is understood to stay on the surface, secondly, an effective identification means for a high-risk welding stage is lacking, when a plurality of tiny interferences are accidentally overlapped, the system cannot timely early warn about impending quality risks, and the system can always respond passively after the defects are generated, thirdly, a closed-loop linkage mechanism is lacking between a monitoring result and process adjustment, the parameter optimization depends on manual experience, the adjustment period is long, and the dynamic change of the production line is difficult to adapt. In view of the above problems, there is a need for a monitoring method capable of sensing dynamic interference in a welding process in real time, intelligently identifying a high risk stage and optimizing process parameters in a closed loop to improve quality stability and process adaptability of a flexible welding production line of a semi-trailer frame. Disclosure of Invention In order to solve the technical problems, the application provides a digital twin monitoring method and device for a flexible welding production line of a semi-trailer frame, which are used for improving the quality stability and the process self-adaption capability of the flexible welding process of the semi-trailer frame. In a first aspect, the application provides a digital twin monitoring method for a flexible welding production line of a semitrailer frame, the method comprising: Step S1, acquiring dynamic interference data and technological parameters of a semitrailer frame in a welding process through a sensor, mapping the dynamic interference data and the technological parameters to a digital twin model, and updating state parameters of corresponding welding stations in the model; step S2, carrying out cluster analysis on the dynamic interference data, determining spatial distribution characteristics and time aggregation characteristics of interference, automatically dividing a welding process into a plurality of continuous monitoring stages according to the spatial distribution characteristics and the time aggregation characteristics, and identifying high-risk stages; step S3, determining a corresponding relation between the interference feature and the technological parameter by utilizing a pre-trained support vector machine model aiming at the high risk stage, determining a key influence feature based on the corresponding relation, and generating a corresponding technological parameter adjustment scheme aiming at the key influence feature; s4, performing simulation and iterative optimization on the welding path according to the process parameter adjustment scheme to obtain an optimized welding path, and performing security verification on the optimized welding path to obtain a