Machine learning-based prediction of CoCrFeNiMo0.2 high-entropy alloy weld bead dimensions in wire arc additive manufacturing

Abstract

Wire arc additive manufacturing (WAAM) is a promising method to fabricate large-sized components. By adjusting WAAM process parameters, dimensions of WAAM-produced weld beads can be optimized. To this end, the current study adopted the cold metal transition (CMT) process to produce different-sized beads of CoCrFeNiMo0.2 high-entropy alloy (HEA), varying WAAM parameters and linking them with the formed bead dimensions and bead-substrate contact angles. Three machine learning algorithms, namely back propagation neural network (BPNN), support vector regression (SVR), and random forest regression (RFR), were used to predict bead dimensions and contact angles under various WAAM parameters. The BPNN model has great prediction performance in the height of beads. The SVR model has the highest accuracy in predicting the width and cross-sectional area of beads. The RFR outperforms the other two models in contact angles prediction. This work not only provides a reference for the WAAM of high-entropy alloys, but also provides new ideas for predicting bead size in WAAM.