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In-situ porosity recognition for laser additive manufacturing of 7075-Al alloy using plasma emission spectroscopy
Poor quality and low repeatability of additively manufactured parts are key technological obstacles for the widespread adoption of additive manufacturing (AM). In-situ monitoring and control of the AM process is vital to overcome this problem. This paper describes the combined artificial intelligenc...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7655859/ https://www.ncbi.nlm.nih.gov/pubmed/33173068 http://dx.doi.org/10.1038/s41598-020-75131-4 |
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author | Ren, Wenjing Mazumder, Jyoti |
author_facet | Ren, Wenjing Mazumder, Jyoti |
author_sort | Ren, Wenjing |
collection | PubMed |
description | Poor quality and low repeatability of additively manufactured parts are key technological obstacles for the widespread adoption of additive manufacturing (AM). In-situ monitoring and control of the AM process is vital to overcome this problem. This paper describes the combined artificial intelligence and plasma emission spectroscopy to identify the porosity of AM parts during the process. The time- and position-synchronized spectra were collected during the directed energy deposition (DED) manufacturing process of a 7075-Al alloy part. Eighteen features extracted from spectra were coupled with the deposition qualities which were characterized by the 3D X-ray Computed Tomography (CT) scan and used to train a Random Forest (RF) classifier. The well-trained RF classifier achieved up to 83% precision for the porosity recognition of depositions. The feature importance recorded by the RF classifier indicates that the intensities of spectra at the wavelength of 414.234 (Fe I) nm and 396.054 (Al I) nm, and the kurtosis of spectra at wavelength ranges of 484–490 nm and 508–518 nm, are the most effective features for porosity recognition. The physical correlations between spectra, porosity formation, and thermal accumulation during the AM process were analyzed. This study demonstrates the great potentials, as well as challenges of plasma emission spectroscopy for in-situ quality monitoring of laser AM which allows the enhancement of AM technique. |
format | Online Article Text |
id | pubmed-7655859 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-76558592020-11-12 In-situ porosity recognition for laser additive manufacturing of 7075-Al alloy using plasma emission spectroscopy Ren, Wenjing Mazumder, Jyoti Sci Rep Article Poor quality and low repeatability of additively manufactured parts are key technological obstacles for the widespread adoption of additive manufacturing (AM). In-situ monitoring and control of the AM process is vital to overcome this problem. This paper describes the combined artificial intelligence and plasma emission spectroscopy to identify the porosity of AM parts during the process. The time- and position-synchronized spectra were collected during the directed energy deposition (DED) manufacturing process of a 7075-Al alloy part. Eighteen features extracted from spectra were coupled with the deposition qualities which were characterized by the 3D X-ray Computed Tomography (CT) scan and used to train a Random Forest (RF) classifier. The well-trained RF classifier achieved up to 83% precision for the porosity recognition of depositions. The feature importance recorded by the RF classifier indicates that the intensities of spectra at the wavelength of 414.234 (Fe I) nm and 396.054 (Al I) nm, and the kurtosis of spectra at wavelength ranges of 484–490 nm and 508–518 nm, are the most effective features for porosity recognition. The physical correlations between spectra, porosity formation, and thermal accumulation during the AM process were analyzed. This study demonstrates the great potentials, as well as challenges of plasma emission spectroscopy for in-situ quality monitoring of laser AM which allows the enhancement of AM technique. Nature Publishing Group UK 2020-11-10 /pmc/articles/PMC7655859/ /pubmed/33173068 http://dx.doi.org/10.1038/s41598-020-75131-4 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Ren, Wenjing Mazumder, Jyoti In-situ porosity recognition for laser additive manufacturing of 7075-Al alloy using plasma emission spectroscopy |
title | In-situ porosity recognition for laser additive manufacturing of 7075-Al alloy using plasma emission spectroscopy |
title_full | In-situ porosity recognition for laser additive manufacturing of 7075-Al alloy using plasma emission spectroscopy |
title_fullStr | In-situ porosity recognition for laser additive manufacturing of 7075-Al alloy using plasma emission spectroscopy |
title_full_unstemmed | In-situ porosity recognition for laser additive manufacturing of 7075-Al alloy using plasma emission spectroscopy |
title_short | In-situ porosity recognition for laser additive manufacturing of 7075-Al alloy using plasma emission spectroscopy |
title_sort | in-situ porosity recognition for laser additive manufacturing of 7075-al alloy using plasma emission spectroscopy |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7655859/ https://www.ncbi.nlm.nih.gov/pubmed/33173068 http://dx.doi.org/10.1038/s41598-020-75131-4 |
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