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A New Method for Automatic Detection of Defects in Selective Laser Melting Based on Machine Vision

Selective laser melting (SLM) is a forming technology in the field of metal additive manufacturing. In order to improve the quality of formed parts, it is necessary to monitor the selective laser melting forming process. At present, most of the research on the monitoring of the selective laser melti...

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Autores principales: Lin, Zhenqiang, Lai, Yiwen, Pan, Taotao, Zhang, Wang, Zheng, Jun, Ge, Xiaohong, Liu, Yuangang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8347657/
https://www.ncbi.nlm.nih.gov/pubmed/34361366
http://dx.doi.org/10.3390/ma14154175
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author Lin, Zhenqiang
Lai, Yiwen
Pan, Taotao
Zhang, Wang
Zheng, Jun
Ge, Xiaohong
Liu, Yuangang
author_facet Lin, Zhenqiang
Lai, Yiwen
Pan, Taotao
Zhang, Wang
Zheng, Jun
Ge, Xiaohong
Liu, Yuangang
author_sort Lin, Zhenqiang
collection PubMed
description Selective laser melting (SLM) is a forming technology in the field of metal additive manufacturing. In order to improve the quality of formed parts, it is necessary to monitor the selective laser melting forming process. At present, most of the research on the monitoring of the selective laser melting forming process focuses on the monitoring of the melting pool, but the quality of forming parts cannot be controlled in real-time. As an indispensable link in the SLM forming process, the quality of powder spreading directly affects the quality of the formed parts. Therefore, this paper proposes a detection method for SLM powder spreading defects, mainly using industrial cameras to collect SLM powder spreading surfaces, designing corresponding image processing algorithms to extract three common powder spreading defects, and establishing appropriate classifiers to distinguish different types of powder spreading defects. It is determined that the multilayer perceptron (MLP) is the most accurate classifier. This detection method has high recognition rate and fast detection speed, which cannot only meet the SLM forming efficiency, but also improve the quality of the formed parts through feedback control.
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spelling pubmed-83476572021-08-08 A New Method for Automatic Detection of Defects in Selective Laser Melting Based on Machine Vision Lin, Zhenqiang Lai, Yiwen Pan, Taotao Zhang, Wang Zheng, Jun Ge, Xiaohong Liu, Yuangang Materials (Basel) Article Selective laser melting (SLM) is a forming technology in the field of metal additive manufacturing. In order to improve the quality of formed parts, it is necessary to monitor the selective laser melting forming process. At present, most of the research on the monitoring of the selective laser melting forming process focuses on the monitoring of the melting pool, but the quality of forming parts cannot be controlled in real-time. As an indispensable link in the SLM forming process, the quality of powder spreading directly affects the quality of the formed parts. Therefore, this paper proposes a detection method for SLM powder spreading defects, mainly using industrial cameras to collect SLM powder spreading surfaces, designing corresponding image processing algorithms to extract three common powder spreading defects, and establishing appropriate classifiers to distinguish different types of powder spreading defects. It is determined that the multilayer perceptron (MLP) is the most accurate classifier. This detection method has high recognition rate and fast detection speed, which cannot only meet the SLM forming efficiency, but also improve the quality of the formed parts through feedback control. MDPI 2021-07-27 /pmc/articles/PMC8347657/ /pubmed/34361366 http://dx.doi.org/10.3390/ma14154175 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lin, Zhenqiang
Lai, Yiwen
Pan, Taotao
Zhang, Wang
Zheng, Jun
Ge, Xiaohong
Liu, Yuangang
A New Method for Automatic Detection of Defects in Selective Laser Melting Based on Machine Vision
title A New Method for Automatic Detection of Defects in Selective Laser Melting Based on Machine Vision
title_full A New Method for Automatic Detection of Defects in Selective Laser Melting Based on Machine Vision
title_fullStr A New Method for Automatic Detection of Defects in Selective Laser Melting Based on Machine Vision
title_full_unstemmed A New Method for Automatic Detection of Defects in Selective Laser Melting Based on Machine Vision
title_short A New Method for Automatic Detection of Defects in Selective Laser Melting Based on Machine Vision
title_sort new method for automatic detection of defects in selective laser melting based on machine vision
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8347657/
https://www.ncbi.nlm.nih.gov/pubmed/34361366
http://dx.doi.org/10.3390/ma14154175
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