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Hardness Prediction of Laser Powder Bed Fusion Product Based on Melt Pool Radiation Intensity

The quality stability and batch consistency of laser powder bed fusion products are key issues that must be solved in additive manufacturing. The melt pool radiation intensity data of laser powder bed fusion contain a significant amount of forming process information, and studies have shown that the...

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Autores principales: Zhang, Ting, Zhou, Xin, Zhang, Peiyu, Duan, Yucong, Cheng, Xing, Wang, Xuede, Ding, Guoquan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9267859/
https://www.ncbi.nlm.nih.gov/pubmed/35806799
http://dx.doi.org/10.3390/ma15134674
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author Zhang, Ting
Zhou, Xin
Zhang, Peiyu
Duan, Yucong
Cheng, Xing
Wang, Xuede
Ding, Guoquan
author_facet Zhang, Ting
Zhou, Xin
Zhang, Peiyu
Duan, Yucong
Cheng, Xing
Wang, Xuede
Ding, Guoquan
author_sort Zhang, Ting
collection PubMed
description The quality stability and batch consistency of laser powder bed fusion products are key issues that must be solved in additive manufacturing. The melt pool radiation intensity data of laser powder bed fusion contain a significant amount of forming process information, and studies have shown that the analysis of melt pool radiation intensity using data-driven methods can achieve online quality judgment; however, there are still speed and accuracy problems. In this study, we propose a data-driven model for hardness predictions of laser powder bed fusion products based on process parameters fused with power spectrum features of melt pool intensity data, which quickly and accurately predicts the microhardness of laser powder bed fusion specimens and can make constructive guidance for closed-loop feedback quality regulation in practical production. The effects of three integrated learning models, Random Forest, XGBoost and LightGBM, are also compared. The results indicate that random forest has the highest prediction accuracy in this dataset; however, it has the limitation of slow training and prediction speeds. The LightGBM algorithm has the fastest training and prediction speeds, about 1.4% and 4.4% of the random forest, respectively; however, the prediction accuracy is lower than that of random forest and XGBoost. XGBoost has the best overall comparative performance with adequate training and prediction speeds, about 23.7% and 37.9% of the random forest, respectively, while ensuring a specified prediction accuracy, which is suitable for application in engineering practices.
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spelling pubmed-92678592022-07-09 Hardness Prediction of Laser Powder Bed Fusion Product Based on Melt Pool Radiation Intensity Zhang, Ting Zhou, Xin Zhang, Peiyu Duan, Yucong Cheng, Xing Wang, Xuede Ding, Guoquan Materials (Basel) Article The quality stability and batch consistency of laser powder bed fusion products are key issues that must be solved in additive manufacturing. The melt pool radiation intensity data of laser powder bed fusion contain a significant amount of forming process information, and studies have shown that the analysis of melt pool radiation intensity using data-driven methods can achieve online quality judgment; however, there are still speed and accuracy problems. In this study, we propose a data-driven model for hardness predictions of laser powder bed fusion products based on process parameters fused with power spectrum features of melt pool intensity data, which quickly and accurately predicts the microhardness of laser powder bed fusion specimens and can make constructive guidance for closed-loop feedback quality regulation in practical production. The effects of three integrated learning models, Random Forest, XGBoost and LightGBM, are also compared. The results indicate that random forest has the highest prediction accuracy in this dataset; however, it has the limitation of slow training and prediction speeds. The LightGBM algorithm has the fastest training and prediction speeds, about 1.4% and 4.4% of the random forest, respectively; however, the prediction accuracy is lower than that of random forest and XGBoost. XGBoost has the best overall comparative performance with adequate training and prediction speeds, about 23.7% and 37.9% of the random forest, respectively, while ensuring a specified prediction accuracy, which is suitable for application in engineering practices. MDPI 2022-07-03 /pmc/articles/PMC9267859/ /pubmed/35806799 http://dx.doi.org/10.3390/ma15134674 Text en © 2022 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
Zhang, Ting
Zhou, Xin
Zhang, Peiyu
Duan, Yucong
Cheng, Xing
Wang, Xuede
Ding, Guoquan
Hardness Prediction of Laser Powder Bed Fusion Product Based on Melt Pool Radiation Intensity
title Hardness Prediction of Laser Powder Bed Fusion Product Based on Melt Pool Radiation Intensity
title_full Hardness Prediction of Laser Powder Bed Fusion Product Based on Melt Pool Radiation Intensity
title_fullStr Hardness Prediction of Laser Powder Bed Fusion Product Based on Melt Pool Radiation Intensity
title_full_unstemmed Hardness Prediction of Laser Powder Bed Fusion Product Based on Melt Pool Radiation Intensity
title_short Hardness Prediction of Laser Powder Bed Fusion Product Based on Melt Pool Radiation Intensity
title_sort hardness prediction of laser powder bed fusion product based on melt pool radiation intensity
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9267859/
https://www.ncbi.nlm.nih.gov/pubmed/35806799
http://dx.doi.org/10.3390/ma15134674
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