State Monitoring Method for Tool Wear in Aerospace Manufacturing Processes Based on a Convolutional Neural Network (CNN)

In the aerospace manufacturing field, tool conditions are essential to ensure the production quality for aerospace parts and reduce processing failures. Therefore, it is extremely necessary to develop a suitable tool condition monitoring method. Thus, we propose a tool wear process state monitoring...

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Autores principales:	Wei Dai, Kui Liang, Bin Wang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	condition monitoring convolutional neural network tool wear fault diagnosis statistical process control Motor vehicles. Aeronautics. Astronautics TL1-4050
Acceso en línea:	https://doaj.org/article/e8c01bb88d0a41798900c50602b0da3f
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id	oai:doaj.org-article:e8c01bb88d0a41798900c50602b0da3f
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spelling	oai:doaj.org-article:e8c01bb88d0a41798900c50602b0da3f2021-11-25T15:57:33ZState Monitoring Method for Tool Wear in Aerospace Manufacturing Processes Based on a Convolutional Neural Network (CNN)10.3390/aerospace81103352226-4310https://doaj.org/article/e8c01bb88d0a41798900c50602b0da3f2021-11-01T00:00:00Zhttps://www.mdpi.com/2226-4310/8/11/335https://doaj.org/toc/2226-4310In the aerospace manufacturing field, tool conditions are essential to ensure the production quality for aerospace parts and reduce processing failures. Therefore, it is extremely necessary to develop a suitable tool condition monitoring method. Thus, we propose a tool wear process state monitoring method for aerospace manufacturing processes based on convolutional neural networks to recognize intermediate abnormal states in multi-stage processes. There are two innovations and advantages of the proposed approach: one is that the criteria for judging abnormal conditions are extended, which is more useful for practical application. The other is that the proposed approach solved the influence of feature-to-recognition stability. Firstly, the tool wear level was divided into different state modes according to the probability density interval based on the kernel density estimation (KDE), and the corresponding state modes were connected to obtain the point-to-point control limit. Then, the state recognition model based on a convolutional neural network (CNN) was developed, and the sensitivity of the monitoring window was considered in the model. Finally, open-source datasets were used to verify the feasibility of the proposed method, and the results demonstrated the applicability of the proposed method in practice for tool condition monitoring.Wei DaiKui LiangBin WangMDPI AGarticlecondition monitoringconvolutional neural networktool wearfault diagnosisstatistical process controlMotor vehicles. Aeronautics. AstronauticsTL1-4050ENAerospace, Vol 8, Iss 335, p 335 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	condition monitoring convolutional neural network tool wear fault diagnosis statistical process control Motor vehicles. Aeronautics. Astronautics TL1-4050
spellingShingle	condition monitoring convolutional neural network tool wear fault diagnosis statistical process control Motor vehicles. Aeronautics. Astronautics TL1-4050 Wei Dai Kui Liang Bin Wang State Monitoring Method for Tool Wear in Aerospace Manufacturing Processes Based on a Convolutional Neural Network (CNN)
description	In the aerospace manufacturing field, tool conditions are essential to ensure the production quality for aerospace parts and reduce processing failures. Therefore, it is extremely necessary to develop a suitable tool condition monitoring method. Thus, we propose a tool wear process state monitoring method for aerospace manufacturing processes based on convolutional neural networks to recognize intermediate abnormal states in multi-stage processes. There are two innovations and advantages of the proposed approach: one is that the criteria for judging abnormal conditions are extended, which is more useful for practical application. The other is that the proposed approach solved the influence of feature-to-recognition stability. Firstly, the tool wear level was divided into different state modes according to the probability density interval based on the kernel density estimation (KDE), and the corresponding state modes were connected to obtain the point-to-point control limit. Then, the state recognition model based on a convolutional neural network (CNN) was developed, and the sensitivity of the monitoring window was considered in the model. Finally, open-source datasets were used to verify the feasibility of the proposed method, and the results demonstrated the applicability of the proposed method in practice for tool condition monitoring.
format	article
author	Wei Dai Kui Liang Bin Wang
author_facet	Wei Dai Kui Liang Bin Wang
author_sort	Wei Dai
title	State Monitoring Method for Tool Wear in Aerospace Manufacturing Processes Based on a Convolutional Neural Network (CNN)
title_short	State Monitoring Method for Tool Wear in Aerospace Manufacturing Processes Based on a Convolutional Neural Network (CNN)
title_full	State Monitoring Method for Tool Wear in Aerospace Manufacturing Processes Based on a Convolutional Neural Network (CNN)
title_fullStr	State Monitoring Method for Tool Wear in Aerospace Manufacturing Processes Based on a Convolutional Neural Network (CNN)
title_full_unstemmed	State Monitoring Method for Tool Wear in Aerospace Manufacturing Processes Based on a Convolutional Neural Network (CNN)
title_sort	state monitoring method for tool wear in aerospace manufacturing processes based on a convolutional neural network (cnn)
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/e8c01bb88d0a41798900c50602b0da3f
work_keys_str_mv	AT weidai statemonitoringmethodfortoolwearinaerospacemanufacturingprocessesbasedonaconvolutionalneuralnetworkcnn AT kuiliang statemonitoringmethodfortoolwearinaerospacemanufacturingprocessesbasedonaconvolutionalneuralnetworkcnn AT binwang statemonitoringmethodfortoolwearinaerospacemanufacturingprocessesbasedonaconvolutionalneuralnetworkcnn
_version_	1718413413078532096

State Monitoring Method for Tool Wear in Aerospace Manufacturing Processes Based on a Convolutional Neural Network (CNN)

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