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

IF 3.2 3区工程技术 Q2 ENGINEERING, INDUSTRIAL

Cirp Annals-Manufacturing Technology Pub Date : 2024-01-01 DOI:10.1016/j.cirp.2024.04.043

Fred M. Carter III (3) , Conor Porter , Dominik Kozjek , Kento Shimoyoshi , Makoto Fujishima (3) , Naruhiro Irino (2) , Jian Cao (1)

引用次数: 0

Abstract

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.

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机器学习引导的自适应激光功率控制在选择性激光熔化减少孔隙中的应用

利用时间分辨率为 200 KHz 的同轴光电二极管监测系统的数据，开发了一种用于选择性激光熔化（SLM）的自适应激光功率控制策略。基于监督机器学习的算法可根据机械特征沿扫描路径输出可变激光功率。该方法已在一台商用机器上实施，与使用恒定激光功率制造的部件相比，X 射线计算机断层扫描（CT）测量的气孔尺寸平均减少了 12%，气孔尺寸标准偏差减少了 65%。这种方法具有可扩展性，其预先计算的性质符合监管要求。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Cirp Annals-Manufacturing Technology 工程技术-工程：工业

CiteScore

7.50

自引率

9.80%

发文量

137

审稿时长

13.5 months

期刊介绍： CIRP, The International Academy for Production Engineering, was founded in 1951 to promote, by scientific research, the development of all aspects of manufacturing technology covering the optimization, control and management of processes, machines and systems. This biannual ISI cited journal contains approximately 140 refereed technical and keynote papers. Subject areas covered include: Assembly, Cutting, Design, Electro-Physical and Chemical Processes, Forming, Abrasive processes, Surfaces, Machines, Production Systems and Organizations, Precision Engineering and Metrology, Life-Cycle Engineering, Microsystems Technology (MST), Nanotechnology.