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Intelligent Tapping Machine: Tap Geometry Inspection
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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10537247/ https://www.ncbi.nlm.nih.gov/pubmed/37766059 http://dx.doi.org/10.3390/s23188005 |
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author | Lin, En-Yu Chen, Ju-Chin Lien, Jenn-Jier James |
author_facet | Lin, En-Yu Chen, Ju-Chin Lien, Jenn-Jier James |
author_sort | Lin, En-Yu |
collection | PubMed |
description | 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’s accumulated empirical experience to determine the service life of the tap. Therefore, we propose the use of visual inspection of the tap’s 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 μm. 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. |
format | Online Article Text |
id | pubmed-10537247 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-105372472023-09-29 Intelligent Tapping Machine: Tap Geometry Inspection Lin, En-Yu Chen, Ju-Chin Lien, Jenn-Jier James Sensors (Basel) Article 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’s accumulated empirical experience to determine the service life of the tap. Therefore, we propose the use of visual inspection of the tap’s 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 μm. 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. MDPI 2023-09-21 /pmc/articles/PMC10537247/ /pubmed/37766059 http://dx.doi.org/10.3390/s23188005 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 Lin, En-Yu Chen, Ju-Chin Lien, Jenn-Jier James Intelligent Tapping Machine: Tap Geometry Inspection |
title | Intelligent Tapping Machine: Tap Geometry Inspection |
title_full | Intelligent Tapping Machine: Tap Geometry Inspection |
title_fullStr | Intelligent Tapping Machine: Tap Geometry Inspection |
title_full_unstemmed | Intelligent Tapping Machine: Tap Geometry Inspection |
title_short | Intelligent Tapping Machine: Tap Geometry Inspection |
title_sort | intelligent tapping machine: tap geometry inspection |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10537247/ https://www.ncbi.nlm.nih.gov/pubmed/37766059 http://dx.doi.org/10.3390/s23188005 |
work_keys_str_mv | AT linenyu intelligenttappingmachinetapgeometryinspection AT chenjuchin intelligenttappingmachinetapgeometryinspection AT lienjennjierjames intelligenttappingmachinetapgeometryinspection |