Optimisation of process parameters for improving surface quality in laser powder bed fusion

• Published in: International journal of advanced manufacturing technology Vol. 130; no. 5-6; pp. 2833 - 2845
• Main Authors: Qin, Yuchu, Lou, Shan, Shi, Peizhi, Qi, Qunfen, Zeng, Wenhan, Scott, Paul J., Jiang, Xiangqian
• Format: Journal Article
• Language: English
• Published: London Springer London 2024; Springer Nature B.V
• Subjects: Beds (process engineering); CAE) and Design; Computer-Aided Engineering (CAD; Design optimization; Design parameters; Engineering; Industrial and Production Engineering; Lasers; Mechanical Engineering; Media Management; Original Article; Powder beds; Process parameters; Surface properties; Laser powder bed fusion; Surface roughness; Additive manufacturing; Process parameter optimisation; Design of experiments; Multi-attribute decision-making
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-023-12826-8

Surface quality is one of the critical factors that affect the performance of a laser powder bed fusion part. Optimising process parameters in process design is an important way to improve surface quality. So far, a number of optimisation methods have been presented within academia. Each of these methods can work well in its specific context. But they were established on a few special surfaces and may not be capable to produce satisfying results for an arbitrary part. Besides, they do not consider the simultaneous improvement of the quality of multiple critical surfaces of a part. In this paper, an approach for optimising process parameters to improve the surface quality of laser powder bed fusion parts is proposed. Firstly, Taguchi optimisation is performed to generate a small number of alternative combinations of the process parameters to be optimised. Then, actual build and measurement experiments are conducted to obtain the quality indicator values of a certain number of critical surfaces under each alternative combination. After that, a flexible three-way technique for order of preference by similarity to ideal solution is used to determine the optimal combination of process parameters from the generated alternatives. Finally, a case study is presented to demonstrate the proposed approach. The demonstration results show that the proposed approach only needs a small amount of experimental data and takes into account the simultaneous improvement of the quality of multiple critical surfaces of an arbitrary part.