Multi-objective optimisation of wire arc additive manufacturing deposition using genetic algorithm

摘要

Wire arc additive manufacturing (WAAM) is a promising technique for depositing metal 3D components with a high deposition rate and low cost. The smallest unit of WAAM-deposited components is a bead, and the properties of single beads determine the quality of overall deposition. The quality of single beads mostly depends upon the input process parameters such as current, tool speed, and wire feed rate. This research proposed optimisation techniques for the selection of process parameters for near-net shape deposition with minimum defects using genetic algorithm (GA). Experiments were conducted according to central composite design (CCD), and mathematical models were developed using response surface methodology (RSM). Al5356 aluminium alloy was chosen as a filler wire for the deposition of beads and an ABB robot equipped with synchronous welding power sources was used for defect-free deposition. Bead geometry (bead height and bead width) and surface roughness are considered the output responses. Optimal process parameters are determined through a genetic algorithm to maximise the bead height and width and minimise the surface roughness. The optimum value is validated through the experiment result, and it is observed that the obtained shape and surface roughness are acceptable for the deposition of WAAM components.