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Predicting the Printability in Selective Laser Melting with a Supervised Machine Learning Method
Though selective laser melting (SLM) has a rapidly increasing market these years, the quality of the SLM-fabricated part is extremely dependent on the process parameters. However, the current metallographic examination method to find the parameter window is time-consuming and involves subjective ass...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7698234/ https://www.ncbi.nlm.nih.gov/pubmed/33182718 http://dx.doi.org/10.3390/ma13225063 |
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author | Chen, Yingyan Wang, Hongze Wu, Yi Wang, Haowei |
author_facet | Chen, Yingyan Wang, Hongze Wu, Yi Wang, Haowei |
author_sort | Chen, Yingyan |
collection | PubMed |
description | Though selective laser melting (SLM) has a rapidly increasing market these years, the quality of the SLM-fabricated part is extremely dependent on the process parameters. However, the current metallographic examination method to find the parameter window is time-consuming and involves subjective assessments of the experimenters. Here, we proposed a supervised machine learning (ML) method to detect the track defect and predict the printability of material in SLM intelligently. The printed tracks were classified into five types based on the measured surface morphologies and characteristics. The classification results were used as the target output of the ML model. Four indicators had been calculated to evaluate the quality of the tracks quantitatively, serving as input variables of the model. The data-driven model can determine the defect-free process parameter combination, which significantly improves the efficiency in searching the process parameter window and has great potential for the application in the unmanned factory in the future. |
format | Online Article Text |
id | pubmed-7698234 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-76982342020-11-29 Predicting the Printability in Selective Laser Melting with a Supervised Machine Learning Method Chen, Yingyan Wang, Hongze Wu, Yi Wang, Haowei Materials (Basel) Article Though selective laser melting (SLM) has a rapidly increasing market these years, the quality of the SLM-fabricated part is extremely dependent on the process parameters. However, the current metallographic examination method to find the parameter window is time-consuming and involves subjective assessments of the experimenters. Here, we proposed a supervised machine learning (ML) method to detect the track defect and predict the printability of material in SLM intelligently. The printed tracks were classified into five types based on the measured surface morphologies and characteristics. The classification results were used as the target output of the ML model. Four indicators had been calculated to evaluate the quality of the tracks quantitatively, serving as input variables of the model. The data-driven model can determine the defect-free process parameter combination, which significantly improves the efficiency in searching the process parameter window and has great potential for the application in the unmanned factory in the future. MDPI 2020-11-10 /pmc/articles/PMC7698234/ /pubmed/33182718 http://dx.doi.org/10.3390/ma13225063 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Chen, Yingyan Wang, Hongze Wu, Yi Wang, Haowei Predicting the Printability in Selective Laser Melting with a Supervised Machine Learning Method |
title | Predicting the Printability in Selective Laser Melting with a Supervised Machine Learning Method |
title_full | Predicting the Printability in Selective Laser Melting with a Supervised Machine Learning Method |
title_fullStr | Predicting the Printability in Selective Laser Melting with a Supervised Machine Learning Method |
title_full_unstemmed | Predicting the Printability in Selective Laser Melting with a Supervised Machine Learning Method |
title_short | Predicting the Printability in Selective Laser Melting with a Supervised Machine Learning Method |
title_sort | predicting the printability in selective laser melting with a supervised machine learning method |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7698234/ https://www.ncbi.nlm.nih.gov/pubmed/33182718 http://dx.doi.org/10.3390/ma13225063 |
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