TY - JOUR
T1 - Optimisation of process parameters for improving surface quality in laser powder bed fusion
AU - Qin, Yuchu
AU - Lou, Shan
AU - Shi, Peizhi
AU - Qi, Qunfen
AU - Zeng, Wenhan
AU - Scott, Paul
AU - Jiang, Jane
N1 - Funding Information:
This study was supported by the National Natural Science Foundation of China (no. 52105511), EPSRC New Investigator Award (Ref. EP/S000453/1), and 3M Buckley Innovation Centre via a 3M BIC Fellowship.
Publisher Copyright:
© 2023, The Author(s).
PY - 2024/1/1
Y1 - 2024/1/1
N2 - 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.
AB - 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.
KW - Process parameter optimisation
KW - Surface roughness
KW - Laser powder bed fusion
KW - Additive manufacturing
KW - Design of experiments
KW - Multi-attribute decision-making
UR - http://www.scopus.com/inward/record.url?scp=85180227988&partnerID=8YFLogxK
U2 - 10.1007/s00170-023-12826-8
DO - 10.1007/s00170-023-12826-8
M3 - Article
VL - 130
SP - 2833
EP - 2845
JO - International Journal of Advanced Manufacturing Technology
JF - International Journal of Advanced Manufacturing Technology
SN - 0268-3768
IS - 5-6
ER -