{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T05:52:05Z","timestamp":1778392325873,"version":"3.51.4"},"reference-count":32,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2020,5,19]],"date-time":"2020-05-19T00:00:00Z","timestamp":1589846400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1808214"],"award-info":[{"award-number":["U1808214"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Milling is a main processing mode of the modern manufacturing industry, which seriously affects the quality and precision of the machined workpiece. However, it is difficult to monitor the tool wear condition in the continuous cutting process, especially under a variable speed condition. The existing tool wear condition monitoring methods only carry out analysis with a constant engine speed. Different from the general monitoring methods, this paper put forward a milling cutter wear condition monitoring method based on order analysis (OA) and stacked sparse autoencoder (SSAE). The methodology in the research include signals feature extraction and tool wear state monitoring and were designed to analyze the three-phase spindle current signals instead of the traditional force signals and vibration signals. The variable speed signals were transformed into angle domain stationary signals by order analysis, and the SSAE neural network was used to monitor the tool wear state. The proposed method was verified on the laboratory signals and the results showed a better performance than the other methods and a better applicability in actual industrial manufacturing.<\/jats:p>","DOI":"10.3390\/s20102878","type":"journal-article","created":{"date-parts":[[2020,5,20]],"date-time":"2020-05-20T02:48:24Z","timestamp":1589942904000},"page":"2878","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["A Novel Order Analysis and Stacked Sparse Auto-Encoder Feature Learning Method for Milling Tool Wear Condition Monitoring"],"prefix":"10.3390","volume":"20","author":[{"given":"Jiayu","family":"Ou","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongkun","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gangjin","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1016\/j.isatra.2017.03.024","article-title":"Image edge detection based tool condition monitoring with morphological component analysis","volume":"69","author":"Yu","year":"2017","journal-title":"ISA Trans."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"107","DOI":"10.26552\/com.C.2020.2.107-114","article-title":"Application of simulation methods for study on availability of one-aisle machine order picking process","volume":"22","author":"Kostrzewski","year":"2020","journal-title":"Communications"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"8605","DOI":"10.1016\/j.matpr.2017.07.208","article-title":"Real time tool wear condition monitoring in hard turning of inconel 718 using sensor fusion system","volume":"4","author":"Mali","year":"2017","journal-title":"Mater. 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