{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T19:49:01Z","timestamp":1779392941232,"version":"3.53.1"},"reference-count":46,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,7]],"date-time":"2023-02-07T00:00:00Z","timestamp":1675728000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004281","name":"National Science Center","doi-asserted-by":"publisher","award":["2020\/39\/ST8\/00188"],"award-info":[{"award-number":["2020\/39\/ST8\/00188"]}],"id":[{"id":"10.13039\/501100004281","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The controlled interaction of work material and cutting tool is responsible for the precise outcome of machining activity. Any deviation in cutting parameters such as speed, feed, and depth of cut causes a disturbance to the machining. This leads to the deterioration of a cutting edge and unfinished work material. Recognition and description of tool failure are essential and must be addressed using intelligent techniques. Deep learning is an efficient method that assists in dealing with a large amount of dynamic data. The manufacturing industry generates momentous information every day and has enormous scope for data analysis. Most intelligent systems have been applied toward the prediction of tool conditions; however, they must be explored for descriptive analytics for on-board pattern recognition. In an attempt to recognize the variation in milling operation leading to tool faults, the development of a Deep Belief Network (DBN) is presented. The network intends to classify in total six tool conditions (one healthy and five faulty) through image-based vibration signals acquired in real time. The model was designed, trained, tested, and validated through datasets collected considering diverse input parameters.<\/jats:p>","DOI":"10.3390\/s23041872","type":"journal-article","created":{"date-parts":[[2023,2,8]],"date-time":"2023-02-08T02:04:16Z","timestamp":1675821856000},"page":"1872","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Development of Deep Belief Network for Tool Faults Recognition"],"prefix":"10.3390","volume":"23","author":[{"given":"Archana P.","family":"Kale","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Modern Education Society\u2019s College of Engineering (MESCOE), Pune 411001, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2747-7557","authenticated-orcid":false,"given":"Revati M.","family":"Wahul","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Modern Education Society\u2019s College of Engineering (MESCOE), Pune 411001, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abhishek D.","family":"Patange","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, COEP Technological University, Pune 411005, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5499-2565","authenticated-orcid":false,"given":"Rohan","family":"Soman","sequence":"additional","affiliation":[{"name":"Institute of Fluid Flow Machinery, Polish Academy of Sciences, Fiszera 14, 80-231 Gdansk, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8061-8614","authenticated-orcid":false,"given":"Wieslaw","family":"Ostachowicz","sequence":"additional","affiliation":[{"name":"Institute of Fluid Flow Machinery, Polish Academy of Sciences, Fiszera 14, 80-231 Gdansk, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2709","DOI":"10.1007\/s00170-021-08365-9","article-title":"Influence of tool path strategies on machining time, tool wear, and surface roughness during milling of AISI X210Cr12 steel","volume":"119","author":"Uzun","year":"2022","journal-title":"Int. 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