Machine learning guided adaptive laser power control in selective laser melting for pore reduction

An adaptive laser power control strategy for Selective Laser Melting (SLM) has been developed using data from a co-axial photodiode monitoring system with 200 KHz temporal resolution. A supervised machine learning based algorithm outputs variable laser power along the scanning path based on mechanis...

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Bibliographic Details
Published in:	CIRP annals Vol. 73; no. 1; pp. 149 - 152
Main Authors:	Carter, Fred M., Porter, Conor, Kozjek, Dominik, Shimoyoshi, Kento, Fujishima, Makoto, Irino, Naruhiro, Cao, Jian
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 2024
Subjects:	Additive manufacturing Artificial intelligence powder bed fusion powder bed fusion Artificial intelligence Additive manufacturing
Online Access:	Get full text
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Summary:	An adaptive laser power control strategy for Selective Laser Melting (SLM) has been developed using data from a co-axial photodiode monitoring system with 200 KHz temporal resolution. A supervised machine learning based algorithm outputs variable laser power along the scanning path based on mechanistic features. The approach was implemented on a commercial machine and demonstrated an average 12 % reduction in porosity size and 65 % reduction in the standard deviation of porosity size measured by X-Ray Computed Tomography (CT) compared to parts built with constant laser power. This approach is scalable and its precalculated nature is compatible with regulatory concerns.
ISSN:	0007-8506
DOI:	10.1016/j.cirp.2024.04.043