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

被引：0

作者：

Carter, Fred M., III ^{[1
,2
]}

Porter, Conor ^{[1
]}

Kozjek, Dominik ^{[1
]}

Shimoyoshi, Kento ^{[2
]}

Fujishima, Makoto ^{[3
]}

Irino, Naruhiro ^{[3
]}

Cao, Jian ^{[1
]}

机构：

[1] Northwestern Univ, Dept Mech Engn, Evanston, IL 60208 USA

[2] DMG MORI Mfg, Davis, CA USA

[3] MG MORI Co LTD, Tokyo, Japan

来源：

CIRP ANNALS-MANUFACTURING TECHNOLOGY | 2024年 / 73卷 / 01期

关键词：

Additive manufacturing; Artificial intelligence; powder bed fusion;

D O I：

10.1016/j.cirp.2024.04.043

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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. (c) 2024 CIRP. Published by Elsevier Ltd. All rights reserved.

引用

页码：149 / 152

页数：4

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