Parameter Optimization of Wire Arc Additive Manufacturing via Machine Learning and a Multiobjective Optimization Algorithm
In wire arc additive manufacturing(WAAM),the intricate interactions among process parameters complicate the task of finding the best settings for manufacturing metal components with superior molding quality and desired geometries.To expedite parameter optimization,this study investigates the effects of the welding current,wire-feed speed(WFS),and travel speed(TS)on the melt width(W),melt height(H),and dilution ratio(D)via 3-factor and 3-level full-factor tests.Artificial neural network(ANN),support vector regression(SVR),and Gaussian process regression(GPR)models are developed to predict these metrics.Comparative analysis indicates that GPR is most effective for predicting the melt width,ANN excels in predicting the melt height,and SVR is superior for assessing the dilution ratio.Multiobjective optimization,which uses the nondominated sorting genetic algorithm-II(NSGA-II),maximizes the melt width and height while minimizing the dilution ratio.The optimized parameters were validated experimentally,confirming the accuracy and effectiveness of the approach.
万方数据
维普
