Low-carbon Modeling and Process Parameter Optimization in Laser Additive Manufacturing Process

doi:10.3901/JME.2022.05.223

Abstract

Abstract: Laser additive manufacturing is a rapid cladding metal powder forming process based on high energy laser beam which produce a large amount of carbon emissions due to long time process and high energy consumption. The carbon emission characteristics of laser additive manufacturing process are systematically analyzed at first, and the carbon emission model of laser additive manufacturing process is established. Then, a multi-objective optimization model of process parameters in laser additive manufacturing process is established with the optimization objectives of carbon emission, powder utilization rate and cladding quality.Furthermore, a novel artificial fish swarm optimization algorithm combined with improved non-dominated sorting and artificial fish step size is proposed which solves the problem that it is blindness of finding parameter solution set and the poor uniformity of objective function solution set. And then the optimal process parameter combination is obtained by entropy weight-grey correlation analysis combined with Pareto optimal solution set method. Finally, the laser cladding experiment is carried out on the LDM4030 laser additive manufacturing equipment as the experimental platform. The experimental results show that the model and algorithm can effectively reduce the carbon emission in the laser additive manufacturing process, improve the utilization rate of powder and ensure the quality of cladding. It provides an effective way for China's laser additive manufacturing equipment industry to achieve carbon peak and carbon neutralization.

Key words: laser additive manufacturing, carbon emissions, process parameters, multi-objective optimization

CLC Number:

TG156

JIANG Xing-yu, LIU Ao, YANG Guo-zhe, LIU Wei-jun, BIAN Hong-you, SUO Ying-qi. Low-carbon Modeling and Process Parameter Optimization in Laser Additive Manufacturing Process[J]. Journal of Mechanical Engineering, 2022, 58(5): 223-238.

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