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The International Journal of Advanced Manufacturing Technology

Published in:

21-10-2023 | ORIGINAL ARTICLE

Process parameter optimisation for selective laser melting of AlSi10Mg-316L multi-materials using machine learning method

Authors: Huan Miao, Farazila Yusof, Mohd Sayuti Ab Karim, Irfan Anjum Badruddin, Mohamed Hussien, Sarfaraz Kamangar, Hao Zhang

Published in: The International Journal of Advanced Manufacturing Technology | Issue 7-8/2023

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Abstract

The present work focuses on process parameter optimisation for selective laser melting (SLM) of AlSi10Mg-316L multi-materials using machine learning method. The properties of the multi-material samples were measured at different process parameters. These process parameter and property data were used to train and validate the machine learning model. A multi-output Gaussian process regression (MO-GPR) model was developed to directly predict the multidimensional output to overcome the limitations of the standard Gaussian process regression (GPR) model. Based on the prediction data, process parameter maps were constructed, and the optimal process parameters for different compositions were selected from the process parameter maps. The results showed that the laser power, scan velocity and hatching space have an important influence on the density and surface roughness of the samples. Results also indicated that there is no linear functional relationship between the optimal volumetric energy density (VED) values and the AlSi10Mg-316L compositions.

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Title: Process parameter optimisation for selective laser melting of AlSi10Mg-316L multi-materials using machine learning method
Authors: Huan Miao
Farazila Yusof
Mohd Sayuti Ab Karim
Irfan Anjum Badruddin
Mohamed Hussien
Sarfaraz Kamangar
Hao Zhang
Publication date: 21-10-2023
Publisher: Springer London
Published in: The International Journal of Advanced Manufacturing Technology / Issue 7-8/2023
Print ISSN: 0268-3768
Electronic ISSN: 1433-3015
DOI: https://doi.org/10.1007/s00170-023-12489-5

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