Welding parameters prediction for arbitrary layer height in robotic wire and arc additive manufacturing

Abstract

In wire and arc additive manufacturing, the weld bead geometry determined the slicing layer height, which was decided by the welding parameters. Generally, the determination of the welding parameters relied on empirical and experimental data through the trial-and-error methods that incur considerable time and cost. To obtain the proper welding process parameters according to the desired single bead geometry and layer height, a full factorial experimental design matrix was applied to collect the original data of welding parameters and bead geometrical variables. A forward artificial neural network (FANN) was built to predict the bead geometry form the welding parameters. Then, a closed-loop iteration method combined a genetic algorithm (GA) and the FANN model (FANN-GA) was developed to search for the most optimal welding process parameters in accordance with the selected bead geometrical variables. The results confirmed that the FANN-GA model has a good performance on the backward prediction of the welding process parameters compared with the direct backward artificial neural network (BANN). Several groups of single layer multi-bead and multi-layer multi-bead experiment were performed to testify the proposed method, and the relative error between the desired and actual layer height was small. The proposed method makes it possible to fabricate the component with an arbitrary desired layer height, and could be used in the adaptive slicing additive manufacturing or surface coating.