Data-driven sustainability improvement in laser metal deposition (LMD) - Liu, Zhuo (Nanyang Technological University, 2023)
This study improves the sustainability of laser metal deposition (LMD) process by reducing material and energy waste caused by the constant blowing out of powder during dry-run movement. To achieve this, the study formulates the LMD path planning problem as a modified travelling salesman problem using mixed-integer linear programming, while considering LMD-specific constraints such as line approach direction and material filling direction. To solve this problem effectively and efficiently, two meta-heuristics, genetic algorithm, and simulated annealing, are proposed. A comparison study is conducted, and the results show that simulated annealing outperforms genetic algorithm in terms of fitness and search time. Thus, by implementing the proposed algorithm reduces the distance of dry-run movement and improves the sustainability of LMD process. The proposed approach has the potential to significantly improve the state-of-the-art of LMD path planning, particularly in computer-aided manufacturing (CAM) and sustainable manufacturing.