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Tool Wear Condition Monitoring Method Based on Deep Learning with Force Signals

Tool wear condition monitoring is an important component of mechanical processing automation, and accurately identifying the wear status of tools can improve processing quality and production efficiency. This paper studied a new deep learning model, to identify the wear status of tools. The force si...

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Detalles Bibliográficos
Autores principales: Zhang, Yaping, Qi, Xiaozhi, Wang, Tao, He, Yuanhang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10221429/
https://www.ncbi.nlm.nih.gov/pubmed/37430508
http://dx.doi.org/10.3390/s23104595
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author Zhang, Yaping
Qi, Xiaozhi
Wang, Tao
He, Yuanhang
author_facet Zhang, Yaping
Qi, Xiaozhi
Wang, Tao
He, Yuanhang
author_sort Zhang, Yaping
collection PubMed
description Tool wear condition monitoring is an important component of mechanical processing automation, and accurately identifying the wear status of tools can improve processing quality and production efficiency. This paper studied a new deep learning model, to identify the wear status of tools. The force signal was transformed into a two-dimensional image using continuous wavelet transform (CWT), short-time Fourier transform (STFT), and Gramian angular summation field (GASF) methods. The generated images were then fed into the proposed convolutional neural network (CNN) model for further analysis. The calculation results show that the accuracy of tool wear state recognition proposed in this paper was above 90%, which was higher than the accuracy of AlexNet, ResNet, and other models. The accuracy of the images generated using the CWT method and identified with the CNN model was the highest, which is attributed to the fact that the CWT method can extract local features of an image and is less affected by noise. Comparing the precision and recall values of the model, it was verified that the image obtained by the CWT method had the highest accuracy in identifying tool wear state. These results demonstrate the potential advantages of using a force signal transformed into a two-dimensional image for tool wear state recognition and of applying CNN models in this area. They also indicate the wide application prospects of this method in industrial production.
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spelling pubmed-102214292023-05-28 Tool Wear Condition Monitoring Method Based on Deep Learning with Force Signals Zhang, Yaping Qi, Xiaozhi Wang, Tao He, Yuanhang Sensors (Basel) Article Tool wear condition monitoring is an important component of mechanical processing automation, and accurately identifying the wear status of tools can improve processing quality and production efficiency. This paper studied a new deep learning model, to identify the wear status of tools. The force signal was transformed into a two-dimensional image using continuous wavelet transform (CWT), short-time Fourier transform (STFT), and Gramian angular summation field (GASF) methods. The generated images were then fed into the proposed convolutional neural network (CNN) model for further analysis. The calculation results show that the accuracy of tool wear state recognition proposed in this paper was above 90%, which was higher than the accuracy of AlexNet, ResNet, and other models. The accuracy of the images generated using the CWT method and identified with the CNN model was the highest, which is attributed to the fact that the CWT method can extract local features of an image and is less affected by noise. Comparing the precision and recall values of the model, it was verified that the image obtained by the CWT method had the highest accuracy in identifying tool wear state. These results demonstrate the potential advantages of using a force signal transformed into a two-dimensional image for tool wear state recognition and of applying CNN models in this area. They also indicate the wide application prospects of this method in industrial production. MDPI 2023-05-09 /pmc/articles/PMC10221429/ /pubmed/37430508 http://dx.doi.org/10.3390/s23104595 Text en © 2023 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, Yaping
Qi, Xiaozhi
Wang, Tao
He, Yuanhang
Tool Wear Condition Monitoring Method Based on Deep Learning with Force Signals
title Tool Wear Condition Monitoring Method Based on Deep Learning with Force Signals
title_full Tool Wear Condition Monitoring Method Based on Deep Learning with Force Signals
title_fullStr Tool Wear Condition Monitoring Method Based on Deep Learning with Force Signals
title_full_unstemmed Tool Wear Condition Monitoring Method Based on Deep Learning with Force Signals
title_short Tool Wear Condition Monitoring Method Based on Deep Learning with Force Signals
title_sort tool wear condition monitoring method based on deep learning with force signals
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10221429/
https://www.ncbi.nlm.nih.gov/pubmed/37430508
http://dx.doi.org/10.3390/s23104595
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