CN-121998183-A - Nine-axis linkage intelligent welding process optimization method based on simulation and digital twinning

CN121998183ACN 121998183 ACN121998183 ACN 121998183ACN-121998183-A

Abstract

A nine-axis linkage intelligent welding process optimization method based on simulation and digital twinning. The existing welding process is mostly dependent on manual experience and single equipment control, and lacks an integrated treatment system of simulation prediction, equipment coordination, virtual-real linkage and iterative optimization. The nine-axis linkage intelligent welding process optimization method is characterized in that a platform is built by means of simulation software, after a three-dimensional model of a member to be welded is built, welding data is obtained through multi-physical field coupling simulation processing of the three-dimensional model of the member to be welded, the welding data is input into an AI multi-objective optimization algorithm to form a simulation data training model, and a processing process of optimal welding sequence and process parameters is obtained through screening according to the simulation data training model.

Inventors

YAO JIA
WU WENSHAN
YIN ZIHAN
XIAO WEI
HE ANPING
LU WEI

Assignees

南宁桂电电子科技研究院有限公司
桂林电子科技大学

Dates

Publication Date: 20260508
Application Date: 20260120

Claims (8)

1. A nine-axis linkage intelligent welding process optimization method based on simulation and digital twinning is characterized in that the nine-axis linkage intelligent welding process optimization method is a processing process of constructing a platform by means of simulation software, after a three-dimensional model of a member to be welded is constructed, acquiring welding data by means of multi-physical field coupling simulation processing of the three-dimensional model of the member to be welded, inputting the welding data into an AI multi-objective optimization algorithm to form a simulation data training model, and screening out optimal welding sequences and process parameters according to the simulation data training model.
2. The nine-axis linkage intelligent welding process optimization method based on simulation and digital twinning is characterized in that a nine-axis linkage welding system construction process is carried out before a three-dimensional model of a member to be welded is constructed, a six-axis welding robot, a three-axis positioner and a high-performance pulse welding power supply are integrated to form a nine-axis linkage welding unit, and then the nine-axis linkage welding unit is ensured to be in a motion synchronization state in welding operation under the cooperation of a developed AI cooperative control module and a kinematic algorithm.
3. The nine-axis linkage intelligent welding process optimization method based on simulation and digital twinning is characterized in that an ROS welding track intelligent planning process is carried out after optimal welding sequence and process parameters are obtained through screening, the ROS welding track intelligent planning process is an ROS-based inverse kinematics solver, an AI obstacle recognition model and a path optimization algorithm are combined to generate a collision-free welding track, and the collision-free welding track is subjected to dynamic correction of workpiece clamping error processing through an AI track smoothing optimization algorithm until the quality of a welding seam is ensured to reach the standard, and then the processing is stopped.
4. The nine-axis linkage intelligent welding process optimization method based on simulation and digital twinning is characterized in that a digital twinning platform is built based on a visual development platform, a virtual-real bidirectional data interaction channel is built through combination of an industrial Ethernet protocol, a welding robot and a positioner, optimal welding sequences and process parameters obtained through screening are imported into the digital twinning platform with ROS planning tracks to conduct virtual previewing, virtual-real data are synchronized in real time in the actual welding process, and AI algorithm compares data deviation and iterates to optimize the process parameters, so that a closed loop control mechanism is formed.
5. The nine-axis linkage intelligent welding process optimization method based on simulation and digital twinning is characterized in that a double-station processing process is configured for a nine-axis linkage welding unit, the double-station processing process is a processing process of parallel development of welding operation and workpiece loading and unloading, and the double-station processing process cooperates with an AI cooperative control module to map the running state of equipment in real time through a digital twinning platform to monitor the posture of a positioner.
6. The nine-axis linkage intelligent welding process optimization method based on simulation and digital twinning of claim 1 is characterized in that simulation software is Simufact Welding, an AI multi-objective optimization algorithm is an optimization model based on a genetic algorithm or a particle swarm algorithm, and welding data are input into the AI multi-objective optimization algorithm to form a simulation data training model to determine optimal heat input quantity in an automatic iteration process parameter combination mode.
7. The nine-axis linkage intelligent welding process optimization method based on simulation and digital twinning is characterized in that the optimal welding sequence is determined by comparing simulation deformation differences of a plurality of groups of welding sequences, the overall maximum deformation of components in the nine-axis linkage welding unit is ensured to be reduced from more than or equal to 8mm to less than or equal to 1.5mm, and the maximum equivalent stress of a welding line area is reduced by more than or equal to 11%.
8. The nine-axis linkage intelligent welding process optimization method based on simulation and digital twinning as claimed in claim 3, wherein the inverse kinematics solver is a TRAC-IK solver, and the path optimization algorithm is The AI multi-objective optimization algorithm has correction of + -0.05 mm clamping error, and the weld quality standard rate of the AI multi-objective optimization algorithm is 95%.

Description

Nine-axis linkage intelligent welding process optimization method based on simulation and digital twinning Technical Field The invention particularly relates to a nine-axis linkage intelligent welding process optimization method based on simulation and digital twinning. Background The traditional robot welding uses teaching reproduction as a core, and although the traditional robot welding is applied in a standardized mass production scene in a large scale, the traditional robot welding is limited by technical characteristics and application scenes, has multiple bottlenecks, is difficult to adapt to high-end manufacturing and nonstandard requirements, and mainly shows the following points: Firstly, programming teaching is highly dependent on manual operation, line changing and debugging take a long time, so that the equipment utilization rate is low, teaching precision is directly affected by experience of operators, and welding deviation is easy to occur in the face of complex welding lines; Secondly, the flexible adaptation capability is weak when the intelligent sensing and self-adaptive adjustment functions are lacking and the emergency such as workpiece assembly errors, material fluctuation and the like is faced; Thirdly, teaching personnel need to operate in a robot working area, and potential safety hazards such as collision exist; fourth, the traditional machine type is not provided with a core module such as visual tracking and sensor sensing, only the preset track can be mechanically repeated, the welding path and parameters cannot be dynamically corrected, and each equipment system independently operates and is difficult to integrate with a production management platform in a seamless way, so that process data cannot be traced, and the technology is difficult to optimize in an iterative way. The robot welding process also has a plurality of technical bottlenecks, mainly comprising the following steps: Firstly, the cooperative precision of welding equipment is insufficient. In the existing welding system, the six-axis welding robot and the positioner are controlled independently, a closed-loop cooperative mechanism is lacked, attitude deviation is easy to occur during welding, frequent manual intervention is needed, welding interruption is caused, and consistency of welding seam quality is poor. Secondly, welding deformation is difficult to accurately control. When the structure of the welded component is complex and the welding seam is concentrated, the welding heat input is uneven, the serious thermal deformation is easy to be caused, the traditional process depends on manual experience to select the welding sequence and parameters, the deformation is difficult to accurately control, and the error range allowed by the industry is often exceeded. Thirdly, the process debugging cost is high and the period is long. When the product is changed, the technological parameters are required to be repeatedly subjected to trial welding and debugging, so that the trial welding rejection rate is high, the mass production qualification rate is low, and the production efficiency is seriously restricted. In addition, the prior art does not combine the AI intelligent decision-making capability and the digital twin visual management and control function, cannot monitor the welding process in real time, feed back data and dynamically optimize the welding process, and is difficult to meet the high-precision welding requirement of a complex steel structure. The existing welding process depends on manual experience and single equipment control, an integrated technology system of simulation prediction, equipment coordination, virtual-real linkage and iterative optimization is not formed, the problems cannot be effectively solved, and the quality and efficiency improvement of welding product manufacturing are severely restricted. Disclosure of Invention The invention provides a nine-axis linkage intelligent welding process optimization method based on simulation and digital twinning, which aims to solve the problems. The nine-axis linkage intelligent welding process optimization method based on simulation and digital twin is that a platform is built by means of simulation software, after a three-dimensional model of a member to be welded is built, welding data is obtained through multi-physical field coupling simulation processing of the three-dimensional model of the member to be welded, the welding data is input into an AI multi-objective optimization algorithm to form a simulation data training model, and a processing process of optimal welding sequence and process parameters is obtained through screening according to the simulation data training model. The method comprises the following steps of firstly carrying out a nine-axis linkage welding system construction process before constructing a three-dimensional model of a member to be welded, integrating a six-axis welding robot, a three-axis positioner and a high-p