基于机械臂氩弧焊工艺参数预测与优化研究OA

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.