Modeling and optimization of robot welding process parameters based on improved SVM-PSO

Machine learning has yielded proficient controllers for welding tasks. However, these controllers have limitations in evaluating the interaction between welding process parameters and welding quality. To address these shortcomings, this article investigates the modeling of welding quality and the optimization of welding process parameters through the support vector machine and particle swarm optimization (SVM-PSO) algorithm. The SVM model is used to establish the relationship model between the welding process parameters and the welding quality, and the PSO algorithm is used to search and finally output the optimized welding process parameters. During the welding process, according to the online inspection results of welding quality and the weld geometry information, the SVM-PSO algorithm can be used to optimize the welding process parameters to reduce the occurrence of welding defects.