{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T03:42:22Z","timestamp":1781322142201,"version":"3.54.1"},"reference-count":31,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51965051"],"award-info":[{"award-number":["51965051"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004763","name":"Natural Science Foundation of Inner Mongolia","doi-asserted-by":"publisher","award":["2019LH05029"],"award-info":[{"award-number":["2019LH05029"]}],"id":[{"id":"10.13039\/501100004763","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100020788","name":"Inner Mongolia Science and Technology Project","doi-asserted-by":"publisher","award":["2021GG0259"],"award-info":[{"award-number":["2021GG0259"]}],"id":[{"id":"10.13039\/501100020788","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003850","name":"Inner Mongolia University Basic Scientific Research Project","doi-asserted-by":"publisher","award":["JY20220191"],"award-info":[{"award-number":["JY20220191"]}],"id":[{"id":"10.13039\/501100003850","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009408","name":"Research Fund Project of Inner Mongolia University of Technology","doi-asserted-by":"publisher","award":["DC2200000930"],"award-info":[{"award-number":["DC2200000930"]}],"id":[{"id":"10.13039\/501100009408","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/access.2022.3221994","type":"journal-article","created":{"date-parts":[[2022,11,14]],"date-time":"2022-11-14T21:42:06Z","timestamp":1668462126000},"page":"119546-119557","source":"Crossref","is-referenced-by-count":11,"title":["Tool Wear Monitoring Based on Transfer Learning and Improved Deep Residual Network"],"prefix":"10.1109","volume":"10","author":[{"given":"Nan","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Inner Mongolia University of Technology, Hohhot, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4413-285X","authenticated-orcid":false,"given":"Jiawei","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Inner Mongolia University of Technology, Hohhot, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Inner Mongolia University of Technology, Hohhot, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoqiang","family":"Kong","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Inner Mongolia University of Technology, Hohhot, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4522-1723","authenticated-orcid":false,"given":"Huaqiang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Inner Mongolia University of Technology, Hohhot, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.3390\/app10196916"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-10-3229-5_47"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.3390\/s21113929"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmsy.2021.09.017"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.3390\/app112210889"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.02.055"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2019.02.055"},{"key":"ref15","first-page":"1","article-title":"An intelligent fault diagnosis method for rotating machinery based on data fusion and deep residual neural network","volume":"52","author":"peng","year":"2021","journal-title":"Appl Intell"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"3605","DOI":"10.1016\/j.istruc.2021.06.081","article-title":"A novel damage identification method based on short time Fourier transform and a new efficient index","volume":"33","author":"reza","year":"2021","journal-title":"Structure"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.3390\/app12031675"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/LCOMM.2020.3039251"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.32604\/iasc.2022.020918"},{"key":"ref4","first-page":"1","article-title":"Tool wear prediction using a hybrid of tool chip image and evolutionary fuzzy neural network","volume":"118","author":"lin","year":"2021","journal-title":"Int J Adv Manuf Technol"},{"key":"ref27","article-title":"How transferable are features in deep neural networks?","author":"yosinski","year":"2014","journal-title":"arXiv 1411 1792"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2018.03.076"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"3217","DOI":"10.1007\/s00170-018-2420-0","article-title":"Tool condition monitoring using spectral subtraction and convolutional neural networks in milling process","volume":"98","author":"fatemeh","year":"2018","journal-title":"Int J Adv Manuf Technol"},{"key":"ref29","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2015","journal-title":"Proc Int Conf on Learn Represent"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.3390\/ma14143773"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.3390\/s19183817"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2018.12.018"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.3390\/app11199026"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-021-07156-6"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/s40430-018-1411-2"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-022-09198-w"},{"key":"ref22","article-title":"Learning sparse networks using targeted dropout","author":"aidan","year":"2019","journal-title":"arXiv 1905 13678"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.106923"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2989510"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2911850"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2021.101516"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2021.104244"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9668973\/09950242.pdf?arnumber=9950242","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T19:53:00Z","timestamp":1670874780000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9950242\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":31,"URL":"https:\/\/doi.org\/10.1109\/access.2022.3221994","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]}}}