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

Fred M. Carter, Conor Porter, Dominik Kozjek, Kento Shimoyoshi, Makoto Fujishima, Naruhiro Irino, Jian Cao*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
JournalCIRP Annals
DOIs
StateAccepted/In press - 2024

Keywords

  • Additive manufacturing
  • Artificial intelligence
  • powder bed fusion

ASJC Scopus subject areas

  • Mechanical Engineering
  • Industrial and Manufacturing Engineering

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