Mayur A. Makhesana, Prashant J. Bagga, Kaushik M. Patel, Haresh D. Patel, Aditya Balu, Navneet Khanna
{"title":"比较分析用于测量加工过程中刀具磨损的不同机器视觉算法","authors":"Mayur A. Makhesana, Prashant J. Bagga, Kaushik M. Patel, Haresh D. Patel, Aditya Balu, Navneet Khanna","doi":"10.1007/s10845-024-02467-3","DOIUrl":null,"url":null,"abstract":"<p>Automatic tool condition monitoring becomes crucial in metal cutting because tool wear impacts the final product’s quality. The optical microscope approach for assessing tool wear is offline, time-consuming, and subject to measurement error by humans. To accomplish this, the machine must be stopped, and the tool must be removed, which causes downtime. As a result, numerous research attempts have been made to develop robust systems for direct tool wear measurement during machining. Therefore, the proposed work focused on developing a direct tool condition monitoring system using machine vision to calculate tool wear parameters, specifically flank wear. The cutting tool insert images are collected using a machine vision setup equipped with an industrial camera, bi-telecentric lens, and a proper illumination system during the machining of AISI 4140 steel. The comparative analysis of image processing algorithms for tool wear measurement is proposed under the selected machining environment. The wear boundary is extracted using digital image processing tools such as image enhancement, image segmentation, image morphology operation, and edge detection. The wear amount on the tool insert is extracted and recorded using the Hough line transformation function and pixel scanning. The comparison of results revealed the measurement accuracy and repeatability of the proposed image processing algorithm with a maximum of 6.25% and minimum of 1.10% error compared to manual measurement. Hence, the proposed approach eliminates manual measurements and improves the machining productivity.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"151 1","pages":""},"PeriodicalIF":5.9000,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comparative analysis of different machine vision algorithms for tool wear measurement during machining\",\"authors\":\"Mayur A. Makhesana, Prashant J. Bagga, Kaushik M. Patel, Haresh D. Patel, Aditya Balu, Navneet Khanna\",\"doi\":\"10.1007/s10845-024-02467-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Automatic tool condition monitoring becomes crucial in metal cutting because tool wear impacts the final product’s quality. The optical microscope approach for assessing tool wear is offline, time-consuming, and subject to measurement error by humans. To accomplish this, the machine must be stopped, and the tool must be removed, which causes downtime. As a result, numerous research attempts have been made to develop robust systems for direct tool wear measurement during machining. Therefore, the proposed work focused on developing a direct tool condition monitoring system using machine vision to calculate tool wear parameters, specifically flank wear. The cutting tool insert images are collected using a machine vision setup equipped with an industrial camera, bi-telecentric lens, and a proper illumination system during the machining of AISI 4140 steel. The comparative analysis of image processing algorithms for tool wear measurement is proposed under the selected machining environment. The wear boundary is extracted using digital image processing tools such as image enhancement, image segmentation, image morphology operation, and edge detection. The wear amount on the tool insert is extracted and recorded using the Hough line transformation function and pixel scanning. The comparison of results revealed the measurement accuracy and repeatability of the proposed image processing algorithm with a maximum of 6.25% and minimum of 1.10% error compared to manual measurement. Hence, the proposed approach eliminates manual measurements and improves the machining productivity.</p>\",\"PeriodicalId\":16193,\"journal\":{\"name\":\"Journal of Intelligent Manufacturing\",\"volume\":\"151 1\",\"pages\":\"\"},\"PeriodicalIF\":5.9000,\"publicationDate\":\"2024-07-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Intelligent Manufacturing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s10845-024-02467-3\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Intelligent Manufacturing","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s10845-024-02467-3","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Comparative analysis of different machine vision algorithms for tool wear measurement during machining
Automatic tool condition monitoring becomes crucial in metal cutting because tool wear impacts the final product’s quality. The optical microscope approach for assessing tool wear is offline, time-consuming, and subject to measurement error by humans. To accomplish this, the machine must be stopped, and the tool must be removed, which causes downtime. As a result, numerous research attempts have been made to develop robust systems for direct tool wear measurement during machining. Therefore, the proposed work focused on developing a direct tool condition monitoring system using machine vision to calculate tool wear parameters, specifically flank wear. The cutting tool insert images are collected using a machine vision setup equipped with an industrial camera, bi-telecentric lens, and a proper illumination system during the machining of AISI 4140 steel. The comparative analysis of image processing algorithms for tool wear measurement is proposed under the selected machining environment. The wear boundary is extracted using digital image processing tools such as image enhancement, image segmentation, image morphology operation, and edge detection. The wear amount on the tool insert is extracted and recorded using the Hough line transformation function and pixel scanning. The comparison of results revealed the measurement accuracy and repeatability of the proposed image processing algorithm with a maximum of 6.25% and minimum of 1.10% error compared to manual measurement. Hence, the proposed approach eliminates manual measurements and improves the machining productivity.
期刊介绍:
The Journal of Nonlinear Engineering aims to be a platform for sharing original research results in theoretical, experimental, practical, and applied nonlinear phenomena within engineering. It serves as a forum to exchange ideas and applications of nonlinear problems across various engineering disciplines. Articles are considered for publication if they explore nonlinearities in engineering systems, offering realistic mathematical modeling, utilizing nonlinearity for new designs, stabilizing systems, understanding system behavior through nonlinearity, optimizing systems based on nonlinear interactions, and developing algorithms to harness and leverage nonlinear elements.