Optimal process parameter combinations search for desired deposited layer geometry in laser-arc hybrid additive manufacturing based on multi-pass overlapping deposited layer contour prediction model and improved NSGA-II algorithm

Abstract

Laser-arc hybrid additive manufacturing (LHAM) leverages the synergistic effect of a combined laser-arc heat source to overcome the limitations of both laser additive manufacturing (LAM) and wire arc additive manufacturing (WAAM). As an advanced process, LHAM holds significant potential for development within the realm of metal additive manufacturing (MAM). How to achieve an optimal combination of process parameters based on the ideal deposited layer geometry of LHAM is a key issue that constrains the maturation of the LHAM process. In this study, an automated solution for obtaining the ideal deposited layer and searching for optimal process parameter combination for LHAM multi-pass overlapping was investigated. Orthogonal experiment was designed to obtain the original data of LHAM multi-pass overlapping deposited layer. Parabolic curve was used to fit the LHAM single-pass layer contour and a multi-pass overlapping layer contour prediction model was constructed based on the iterative approach. The accuracy of the prediction model was evaluated using three metrics. Aiming at the shortcomings of the second-generation non-dominated genetic algorithm (NSGA-II), an improved NSGA-II algorithm was proposed, and an optimisation model for the combination of process parameters was constructed by combining the entropy-weight TOPSIS method. The optimisation model was able to automatically optimise the process parameter combinations through the expected deposition layer morphology parameters. The relative error of the actual height of the multi-pass overlapping layer from the preset height selected by the optimal combination of process parameters was only 2.067%, and a smoother surface of the layer was obtained, which verified the feasibility of the optimisation model.