{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T10:40:27Z","timestamp":1781952027075,"version":"3.54.5"},"reference-count":34,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,20]],"date-time":"2023-01-20T00:00:00Z","timestamp":1674172800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2020R1F1A1072926"],"award-info":[{"award-number":["NRF-2020R1F1A1072926"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["20012834"],"award-info":[{"award-number":["20012834"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Technology Innovation Program","award":["NRF-2020R1F1A1072926"],"award-info":[{"award-number":["NRF-2020R1F1A1072926"]}]},{"name":"Technology Innovation Program","award":["20012834"],"award-info":[{"award-number":["20012834"]}]},{"name":"Ministry of Trade, Industry and Energy","award":["NRF-2020R1F1A1072926"],"award-info":[{"award-number":["NRF-2020R1F1A1072926"]}]},{"name":"Ministry of Trade, Industry and Energy","award":["20012834"],"award-info":[{"award-number":["20012834"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>A novel method for tool wear estimation in milling using infrared (IR) laser vision and a deep-learning algorithm is proposed and demonstrated. The measurement device employs an IR line laser to irradiate the tool focal point at angles of \u22127.5\u00b0, 0.0\u00b0, and +7.5\u00b0 to the vertical plane, and three cameras are placed at 45\u00b0 intervals around the tool to collect the reflected IR light at different locations. For the processing materials and methods, a dry processing method was applied to a 100 mm \u00d7 100 mm \u00d7 40 mm SDK-11 workpiece through end milling and downward cutting using a TH308 insert. This device uses the diffused light reflected off the surface of a rotating tool roughened by flank wear, and a polarization filter is considered. As the measured tool wear images exhibit a low dynamic range of exposure, high dynamic range (HDR) images are obtained using an exposure fusion method. Finally, tool wear is estimated from the images using a multi-view convolutional neural network. As shown in the results of the estimated tool wear, a mean absolute error (MAE) of prediction error calculated was to be 9.5~35.21 \u03bcm. The proposed method can improve machining efficiency by reducing the downtime for tool wear measurement and by increasing tool life utilization.<\/jats:p>","DOI":"10.3390\/s23031208","type":"journal-article","created":{"date-parts":[[2023,1,20]],"date-time":"2023-01-20T06:52:41Z","timestamp":1674197561000},"page":"1208","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Tool-Wear-Estimation System in Milling Using Multi-View CNN Based on Reflected Infrared Images"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9252-9187","authenticated-orcid":false,"given":"Woong-Ki","family":"Jang","sequence":"first","affiliation":[{"name":"Department of Smart Health Science and Technology, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon 24341, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong-Wook","family":"Kim","sequence":"additional","affiliation":[{"name":"Electric Power Train R&D Department, Korea Automotive Technology Institute, 303 Pungse-ro, Pungse-myeon, Dongnam-gu, Cheonan 31214, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Young-Ho","family":"Seo","sequence":"additional","affiliation":[{"name":"Department of Smart Health Science and Technology, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon 24341, Republic of Korea"},{"name":"Department of Mechatronics Engineering, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon 24341, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6806-4201","authenticated-orcid":false,"given":"Byeong-Hee","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Smart Health Science and Technology, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon 24341, Republic of Korea"},{"name":"Department of Mechatronics Engineering, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon 24341, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.1109\/TASE.2015.2513208","article-title":"Residual life prediction of multistage manufacturing processes with interaction between tool wear and product quality degradation","volume":"14","author":"Hao","year":"2016","journal-title":"IEEE Trans. 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