Integrating object detection and image segmentation for detecting the tool wear area on stitched image

Abstract Flank wear is the most common wear that happens in the end milling process. However, the process of detecting the flank wear is cumbersome. To achieve comprehensively automatic detecting the flank wear area of the spiral end milling cutter, this study proposed a novel flank wear detection m...

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Autores principales:	Wan-Ju Lin, Jian-Wen Chen, Jian-Ping Jhuang, Meng-Shiun Tsai, Che-Lun Hung, Kuan-Ming Li
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b6a6b7c5d7b340269b74ca42860ab8fb
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spelling	oai:doaj.org-article:b6a6b7c5d7b340269b74ca42860ab8fb2021-12-02T17:13:18ZIntegrating object detection and image segmentation for detecting the tool wear area on stitched image10.1038/s41598-021-97610-y2045-2322https://doaj.org/article/b6a6b7c5d7b340269b74ca42860ab8fb2021-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97610-yhttps://doaj.org/toc/2045-2322Abstract Flank wear is the most common wear that happens in the end milling process. However, the process of detecting the flank wear is cumbersome. To achieve comprehensively automatic detecting the flank wear area of the spiral end milling cutter, this study proposed a novel flank wear detection method of combining the template matching and deep learning techniques to expand the curved surface images into panorama images, which is more available to detect the flank wear areas without choosing a specific position of cutting tool image. You Only Look Once v4 model was employed to automatically detect the range of cutting tips. Then, popular segmentation models, namely, U-Net, Segnet and Autoencoder were used to extract the areas of the tool flank wear. To evaluate the segmenting performance among these models, U-Net model obtained the best maximum dice coefficient score with 0.93. Moreover, the predicting wear areas of the U-Net model is presented in the trend figure, which can determine the times of the tool change depend on the curve of the tool wear. Overall, the experiments have shown that the proposed methods can effectively extract the tool wear regions of the spiral cutting tool. With the developed system, users can obtain detailed information about the cutting tool before being worn severely to change the cutting tools in advance.Wan-Ju LinJian-Wen ChenJian-Ping JhuangMeng-Shiun TsaiChe-Lun HungKuan-Ming LiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Wan-Ju Lin Jian-Wen Chen Jian-Ping Jhuang Meng-Shiun Tsai Che-Lun Hung Kuan-Ming Li Integrating object detection and image segmentation for detecting the tool wear area on stitched image
description	Abstract Flank wear is the most common wear that happens in the end milling process. However, the process of detecting the flank wear is cumbersome. To achieve comprehensively automatic detecting the flank wear area of the spiral end milling cutter, this study proposed a novel flank wear detection method of combining the template matching and deep learning techniques to expand the curved surface images into panorama images, which is more available to detect the flank wear areas without choosing a specific position of cutting tool image. You Only Look Once v4 model was employed to automatically detect the range of cutting tips. Then, popular segmentation models, namely, U-Net, Segnet and Autoencoder were used to extract the areas of the tool flank wear. To evaluate the segmenting performance among these models, U-Net model obtained the best maximum dice coefficient score with 0.93. Moreover, the predicting wear areas of the U-Net model is presented in the trend figure, which can determine the times of the tool change depend on the curve of the tool wear. Overall, the experiments have shown that the proposed methods can effectively extract the tool wear regions of the spiral cutting tool. With the developed system, users can obtain detailed information about the cutting tool before being worn severely to change the cutting tools in advance.
format	article
author	Wan-Ju Lin Jian-Wen Chen Jian-Ping Jhuang Meng-Shiun Tsai Che-Lun Hung Kuan-Ming Li
author_facet	Wan-Ju Lin Jian-Wen Chen Jian-Ping Jhuang Meng-Shiun Tsai Che-Lun Hung Kuan-Ming Li
author_sort	Wan-Ju Lin
title	Integrating object detection and image segmentation for detecting the tool wear area on stitched image
title_short	Integrating object detection and image segmentation for detecting the tool wear area on stitched image
title_full	Integrating object detection and image segmentation for detecting the tool wear area on stitched image
title_fullStr	Integrating object detection and image segmentation for detecting the tool wear area on stitched image
title_full_unstemmed	Integrating object detection and image segmentation for detecting the tool wear area on stitched image
title_sort	integrating object detection and image segmentation for detecting the tool wear area on stitched image
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b6a6b7c5d7b340269b74ca42860ab8fb
work_keys_str_mv	AT wanjulin integratingobjectdetectionandimagesegmentationfordetectingthetoolwearareaonstitchedimage AT jianwenchen integratingobjectdetectionandimagesegmentationfordetectingthetoolwearareaonstitchedimage AT jianpingjhuang integratingobjectdetectionandimagesegmentationfordetectingthetoolwearareaonstitchedimage AT mengshiuntsai integratingobjectdetectionandimagesegmentationfordetectingthetoolwearareaonstitchedimage AT chelunhung integratingobjectdetectionandimagesegmentationfordetectingthetoolwearareaonstitchedimage AT kuanmingli integratingobjectdetectionandimagesegmentationfordetectingthetoolwearareaonstitchedimage
_version_	1718381340960751616

Integrating object detection and image segmentation for detecting the tool wear area on stitched image

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