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Prediction of micro-hardness in thread rolling of St37 by convolutional neural networks and transfer learning

This study introduces a non-destructive method by applying convolutional neural networks (CNN) to predict the micro-hardness of the thread-rolled steel. Material microstructure images were collected for our research, and micro-hardness tests were conducted to label the extracted microstructure image...

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Autores principales: Soleymani, Mehdi, Khoshnevisan, Mohammad, Davoodi, Behnam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer London 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9646279/
https://www.ncbi.nlm.nih.gov/pubmed/36407575
http://dx.doi.org/10.1007/s00170-022-10355-4
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author Soleymani, Mehdi
Khoshnevisan, Mohammad
Davoodi, Behnam
author_facet Soleymani, Mehdi
Khoshnevisan, Mohammad
Davoodi, Behnam
author_sort Soleymani, Mehdi
collection PubMed
description This study introduces a non-destructive method by applying convolutional neural networks (CNN) to predict the micro-hardness of the thread-rolled steel. Material microstructure images were collected for our research, and micro-hardness tests were conducted to label the extracted microstructure images. In recent years, researchers have used machine learning (ML) and deep learning (DL) models to predict material properties for forming, machining, additive manufacturing, and other processes. However, they encountered industrial limitations primarily because of the absence of historical information on new and unknown materials, which are necessary to predict material properties by DL models. These problems can be solved by employing CNN models. In our work, we used a CNN model with two convolutional layers and visual geometry group (VGG19) as transfer learning (TL). We predicted four classes of micro-hardness of the St37 rolled threads. The prediction results of the micro-hardness test images by our proposed CNN model and pre-trained VGG19 model are comparable. Our proposed model has produced the same precision and recall scores as VGG19 for class B and class C hardness. VGG19 performed slightly better than our model for precision in class A and recall in class D. We observed that the training time of our proposed model using the CPU (central processing unit) was approximately nine times faster than the VGG19 model. Our proposed CNN and VGG19 have direct applications in advanced manufacturing (AM). They can automatically predict the micro-hardness in the thread rolling of St37. Our proposed model requires less memory and computational power and can be deployed more efficiently than the VGG19 model. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00170-022-10355-4.
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spelling pubmed-96462792022-11-14 Prediction of micro-hardness in thread rolling of St37 by convolutional neural networks and transfer learning Soleymani, Mehdi Khoshnevisan, Mohammad Davoodi, Behnam Int J Adv Manuf Technol Original Article This study introduces a non-destructive method by applying convolutional neural networks (CNN) to predict the micro-hardness of the thread-rolled steel. Material microstructure images were collected for our research, and micro-hardness tests were conducted to label the extracted microstructure images. In recent years, researchers have used machine learning (ML) and deep learning (DL) models to predict material properties for forming, machining, additive manufacturing, and other processes. However, they encountered industrial limitations primarily because of the absence of historical information on new and unknown materials, which are necessary to predict material properties by DL models. These problems can be solved by employing CNN models. In our work, we used a CNN model with two convolutional layers and visual geometry group (VGG19) as transfer learning (TL). We predicted four classes of micro-hardness of the St37 rolled threads. The prediction results of the micro-hardness test images by our proposed CNN model and pre-trained VGG19 model are comparable. Our proposed model has produced the same precision and recall scores as VGG19 for class B and class C hardness. VGG19 performed slightly better than our model for precision in class A and recall in class D. We observed that the training time of our proposed model using the CPU (central processing unit) was approximately nine times faster than the VGG19 model. Our proposed CNN and VGG19 have direct applications in advanced manufacturing (AM). They can automatically predict the micro-hardness in the thread rolling of St37. Our proposed model requires less memory and computational power and can be deployed more efficiently than the VGG19 model. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00170-022-10355-4. Springer London 2022-11-10 2022 /pmc/articles/PMC9646279/ /pubmed/36407575 http://dx.doi.org/10.1007/s00170-022-10355-4 Text en © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Original Article
Soleymani, Mehdi
Khoshnevisan, Mohammad
Davoodi, Behnam
Prediction of micro-hardness in thread rolling of St37 by convolutional neural networks and transfer learning
title Prediction of micro-hardness in thread rolling of St37 by convolutional neural networks and transfer learning
title_full Prediction of micro-hardness in thread rolling of St37 by convolutional neural networks and transfer learning
title_fullStr Prediction of micro-hardness in thread rolling of St37 by convolutional neural networks and transfer learning
title_full_unstemmed Prediction of micro-hardness in thread rolling of St37 by convolutional neural networks and transfer learning
title_short Prediction of micro-hardness in thread rolling of St37 by convolutional neural networks and transfer learning
title_sort prediction of micro-hardness in thread rolling of st37 by convolutional neural networks and transfer learning
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9646279/
https://www.ncbi.nlm.nih.gov/pubmed/36407575
http://dx.doi.org/10.1007/s00170-022-10355-4
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