{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T05:01:31Z","timestamp":1777266091574,"version":"3.51.4"},"reference-count":29,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T00:00:00Z","timestamp":1695254400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and (MOST), Taiwan, R.O.C.","award":["NSTC 112-2425-H-006-001"],"award-info":[{"award-number":["NSTC 112-2425-H-006-001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Currently, the majority of industrial metal processing involves the use of taps for cutting. However, existing tap machines require relocation to specialized inspection stations and only assess the condition of the cutting edges for defects. They do not evaluate the quality of the cutting angles and the amount of removed material. Machine vision, a key component of smart manufacturing, is commonly used for visual inspection. Taps are employed for processing various materials. Traditional tap replacement relies on the technician\u2019s accumulated empirical experience to determine the service life of the tap. Therefore, we propose the use of visual inspection of the tap\u2019s external features to determine whether replacement or regrinding is needed. We examined the bearing surface of the tap and utilized single images to identify the cutting angle, clearance angle, and cone angles. By inspecting the side of the tap, we calculated the wear of each cusp. This inspection process can facilitate the development of a tap life system, allowing for the estimation of the durability and wear of taps and nuts made of different materials. Statistical analysis can be employed to predict the lifespan of taps in production lines. Experimental error is 16 \u03bcm. Wear from tapping 60 times is equivalent to 8 s of electric grinding. We have introduced a parameter, thread removal quantity, which has not been proposed by anyone else.<\/jats:p>","DOI":"10.3390\/s23188005","type":"journal-article","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T21:16:49Z","timestamp":1695331009000},"page":"8005","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Intelligent Tapping Machine: Tap Geometry Inspection"],"prefix":"10.3390","volume":"23","author":[{"given":"En-Yu","family":"Lin","sequence":"first","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ju-Chin","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 807, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jenn-Jier James","family":"Lien","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.neucom.2022.06.083","article-title":"Recent advances on image edge detection: A comprehensive review","volume":"53","author":"Jing","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"87763","DOI":"10.1109\/ACCESS.2021.3089210","article-title":"Quality Assessment Methods to Evaluate the Performance of Edge Detection Algorithms for Digital Image: A Systematic Literature Review","volume":"9","author":"Tariq","year":"2021","journal-title":"IEEE Access"},{"key":"ref_3","first-page":"012039","article-title":"Prewitt and Canny Methods on Inversion Image Edge Detection: An Evaluation","volume":"1933","author":"Rahmawati","year":"2020","journal-title":"VICEST"},{"key":"ref_4","first-page":"269","article-title":"A Comparison of various Edge Detection Techniques used in Image Processing","volume":"9","author":"Shrivakshan","year":"2012","journal-title":"IJCSI"},{"key":"ref_5","first-page":"83","article-title":"Study and Comparison of Various Image Edge Detection Techniques","volume":"4","author":"Maini","year":"2011","journal-title":"IJABE"},{"key":"ref_6","first-page":"8","article-title":"Edge Detection using Guided Image Filtering and Enhanced Ant Colony Optimization","volume":"173","author":"Kumar","year":"2020","journal-title":"ICITETM"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"11191","DOI":"10.1109\/ACCESS.2022.3145428","article-title":"Enhanced Edge Detection Using SR-Guided Threshold Maneuvering and Window Mapping: Handling Broken Edges and Noisy Structures in Canny Edges","volume":"10","author":"Dhillon","year":"2021","journal-title":"IEEE Access"},{"key":"ref_8","first-page":"022131","article-title":"Lane Line Edge Detection Based on Improved Adaptive Canny Algorithm","volume":"1549","author":"Zhang","year":"2020","journal-title":"ESAET"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Tian, R., Sun, G., Liu, X., and Zheng, B. 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