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This article proposes a tool wear prediction method based on a particle swarm optimization (PSO)-optimized deep belief network (DBN) to improve prediction accuracy. First, features are extracted from sensor signals through time-domain, frequency-domain, and time\u2013frequency-domain methods. Subsequently, a correlation analysis method is employed to identify wear-sensitive features of the tool. Finally, the proposed PSO-DBN model is rigorously evaluated using cross-validation, demonstrating its effectiveness in accurately predicting tool wear across all test cases. Comparative experiments against established models and state-of-the-art approaches show that the PSO-DBN model achieves superior performance, with lower root mean squared error (RMSE) and mean absolute error (MAE) values and a higher R2 score, confirming its precision and reliability in tool wear prediction.<\/jats:p>","DOI":"10.1115\/1.4069658","type":"journal-article","created":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T15:20:37Z","timestamp":1756480837000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":3,"title":["Prediction of Tool Wear by Improved Deep Belief Networks Based on Multisensor Signals"],"prefix":"10.1115","volume":"25","author":[{"given":"Zisheng","family":"Li","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04d996474","id-type":"ROR","asserted-by":"publisher"}],"name":"Southwest University of Science and Technology School of Manufacturing Science and Engineering, , , \u00a0 ,","place":["Mianyang, Sichuan, China, 621010"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoping","family":"Xiao","sequence":"additional","affiliation":[{"name":"Southwest University of Science and Technology Engineering Technology Center, , , \u00a0 ,","place":["Mianyang, Sichuan, China, 621010"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjun","family":"Zhou","sequence":"additional","affiliation":[{"name":"Southwest University of Science and Technology School of Manufacturing Science and Engineering, , , \u00a0 ,","place":["Mianyang, Sichuan, China, 621010"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Zhang","sequence":"additional","affiliation":[{"name":"Southwest Jiaotong University School of Mechanical Engineering, , , \u00a0 ,","place":["Chengdu, Sichuan, China, 610031"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenhao","family":"Wang","sequence":"additional","affiliation":[{"name":"Southwest University of Science and Technology School of Manufacturing Science and Engineering, , , \u00a0 ,","place":["Mianyang, Sichuan, China, 621010"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"33","published-online":{"date-parts":[[2025,9,15]]},"reference":[{"key":"2025091512390843600_CIT0001","doi-asserted-by":"publisher","first-page":"119886","DOI":"10.1016\/j.eswa.2023.119886","article-title":"Online Monitoring Model of Micro-Milling Force Incorporating Tool Wear Prediction Process","volume":"223","author":"Ding","year":"2023","journal-title":"Expert Syst. 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