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Data Treatment of In Situ Monitoring Systems in Selective Laser Melting Machines
Author(s) -
Yadav Pinku,
Rigo Olivier,
Arvieu Corinne,
Le Guen Emilie,
Lacoste Eric
Publication year - 2021
Publication title -
advanced engineering materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.938
H-Index - 114
eISSN - 1527-2648
pISSN - 1438-1656
DOI - 10.1002/adem.202001327
Subject(s) - process (computing) , benchmark (surveying) , reliability (semiconductor) , selective laser melting , quality assurance , convolutional neural network , computer science , artificial neural network , artificial intelligence , support vector machine , materials science , sensor fusion , laser , machine learning , data mining , engineering , optics , quantum mechanics , power (physics) , operations management , physics , external quality assessment , geodesy , geography , operating system
Quality assurance of the final build part in laser‐powder bed fusion (L‐PBF) is greatly influenced by the various process steps such as powder handling, powder bed spreading, and laser‐material interaction. Each process step is interlinked to each other and can affect the overall behavior of the succeeding steps. Therefore, it is vital to monitor each step individually, post‐process, and establish a link among the data to develop an approach to quantify the defects via inline monitoring. This study focuses on using pre‐ and post‐exposure powder bed image data and in situ melt pool monitoring (MPM) data to monitor the build's overall quality. Two convolutional neural networks have been trained to treat the pre and post‐exposure images with a trained accuracy of 93.16% and 96.20%, respectively. The supervised machine‐learning algorithm called “support vector machine” is used to classify and post‐process the photodiodes data obtained from the MPM. A case study on “benchmark part” is presented to check the proposed algorithms' overall working and detect abnormalities at three different process steps (pre and post‐exposure, MPM) individually. This study shows the potential of machine learning approaches to improve the overall reliability of the (L‐PBF) process by inter‐linking the different process steps.