Prediction of TC11 single-track geometry in laser metal deposition based on back propagation neural network and random forest

Laser metal deposition process usually involves the nonlinear interaction of multiple factors, such as process parameters and ambient temperature. In this study, random forest (RF) and multilayer back propagation neural network (BPNN) algorithms were employed to investigate the coupling relationship between process parameters and single-track geometry in laser metal deposition for TC11 alloy. With laser power, scanning speed, and powder feeding rate as inputs and track width and height as outputs, 30 different groups of experimental results were adopted as training groups. Their geometries were also predicted. The maximum relative errors of track width and height predictions based on BPNN model were 0.007 % and 0.029 %, respectively, which were lower than those based on RF model. Then, the two models were used to predict the geometry under four new sets of process parameters. Experimental results showed that the maximum error of BPNN model is lower than that of RF model. BPNN model also showed potential to improve cladding quality and efficiency.