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In Situ Monitoring Systems of The SLM Process: On the Need to Develop Machine Learning Models for Data Processing

SIRRIS, Rue du Bois Saint-Jean 12, 4102 Seraing, Belgium
CNRS, Arts et Metiers Institute of Technology, University of Bordeaux, Bordeaux INP, INRAE, I2M Bordeaux, F-33400 Talence, France
Author to whom correspondence should be addressed.
Crystals 2020, 10(6), 524;
Received: 27 May 2020 / Revised: 10 June 2020 / Accepted: 13 June 2020 / Published: 18 June 2020
(This article belongs to the Special Issue Additive Manufacturing (AM) of Metallic Alloys)
In recent years, technological advancements have led to the industrialization of the laser powder bed fusion process. Despite all of the advancements, quality assurance, reliability, and lack of repeatability of the laser powder bed fusion process still hinder risk-averse industries from adopting it wholeheartedly. The process-induced defects or drifts can have a detrimental effect on the quality of the final part, which could lead to catastrophic failure of the finished part. It led to the development of in situ monitoring systems to effectively monitor the process signatures during printing. Nevertheless, post-processing of the in situ data and defect detection in an automated fashion are major challenges. Nowadays, many studies have been focused on incorporating machine learning approaches to solve this problem and develop a feedback control loop system to monitor the process in real-time. In our study, we review the types of process defects that can be monitored via process signatures captured by in situ sensing devices and recent advancements in the field of data analytics for easy and automated defect detection. We also discuss the working principles of the most common in situ sensing sensors to have a better understanding of the process. Commercially available in situ monitoring devices on laser powder bed fusion systems are also reviewed. This review is inspired by the work of Grasso and Colosimo, which presented an overall review of powder bed fusion technology. View Full-Text
Keywords: L-PBF; in situ sensing; quality assurance; machine learning L-PBF; in situ sensing; quality assurance; machine learning
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Yadav, P.; Rigo, O.; Arvieu, C.; Le Guen, E.; Lacoste, E. In Situ Monitoring Systems of The SLM Process: On the Need to Develop Machine Learning Models for Data Processing. Crystals 2020, 10, 524.

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