金属加工（热加工）Issue(3)：38-45,8.
基于机械臂氩弧焊工艺参数预测与优化研究
Research on prediction and optimization of process parameters for robotic arc welding
摘要
Abstract
In response to the high-dimensional nonlinear coupling issue between welding parameters and weld performance in robotic argon arc welding,a systematic experimental dataset was constructed through a 4-factor(welding speed,gas flow rate,welding current,oscillation amplitude)5-level orthogonal experiment,with melt width and melt depth as key weld performance indicators.The welding parameters were optimized based on the orthogonal experiment.Subsequently,the performance of various data-driven prediction models was compared and analyzed.The results indicated that the proposed hybrid model combining BP neural network and belief rule base(BP-BRB)demonstrated the best prediction accuracy on the test set.The total mean square error(MSE)of its predictions significantly decreased from 1.534947 for the traditional BP model to 1.170058,representing an accuracy improvement of approximately 23.78％,effectively enhancing the modeling capability for complex welding processes.For comparison,welds from three different processes were labeled as follows:Sample 1(optimized by PSO-GA fusion algorithm),Sample 2(optimized by orthogonal experiment),and Sample 3(baseline process before optimization).The results showed that the welding effect after fusion algorithm optimization was the best.Compared to Sample 2,Sample 1 achieved a 1.5％improvement in welding efficiency,while Sample 3 exhibited a 61.74％increase in weld width-to-depth ratio,indicating significant enhancement in welding performance.关键词
焊接参数/遗传算法/神经网络/粒子群算法Key words
welding process parameters/genetic algorithm/neural network(s)/particle swarm optimization引用本文复制引用
闫渭丘,田军委,苏宇,刘雪松,张杰..基于机械臂氩弧焊工艺参数预测与优化研究[J].金属加工（热加工）,2026,(3):38-45,8.基金项目
陕西省教育厅服务地方专项(24JC039) （24JC039）
西安科技计划项目(24GFW0030) （24GFW0030）
西安市科技计划项目(23GXFW0028) （23GXFW0028）
陕西省科技厅重点研发计划项目(2024GX-YBXM-191) （2024GX-YBXM-191）
陕西省教育厅专项科研计划(21JK0679). （21JK0679）
