Process Drift Detection in SLM process using in situ monitoring and machine learning approach
| Main Authors: | Pinku Yadav, Olivier Rigo, Corinne Arvieu, Eric Lacoste |
|---|---|
| Format: | info Proceeding Journal |
| Terbitan: |
, 2021
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/4883541 |
Daftar Isi:
- Laser-powder bed fusion (L-PBF) see a growing demand in industry applications for its complexity free manufacturing capabilities. Besides, all the advantages over traditional manufacturing techniques, reliability and repeatability is a major concern in this domain. In-situ monitoring of the process using high precision sensors such as photodiodes, High-speed IR cameras both in co-axial and off-axis positions is being developed and is implemented in commercially available machines. But understanding the correlations between the very large amount acquired data from sensors and build quality is a challenge. In our work, we present in a first time a sensitivity analysis of the the in-situ monitoring systems installed on commercial SLM280HL machine . We have studied the link between variation in signal captured by various coaxial photodiodes (visible and infrared region) with drift in the process. Later, we make use machine learning (ML) approaches to treat the data in an automated fashion. Use of ML for treating the enormous data and detection of the drift will reduce the time and data storage problem linked to the current system. Our results obtained with machine monitoring systems are linked with thus obtained by instrumented lab-bench PHILAE (Photonic Interaction Laboratory and Augmented Expertience).