{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T11:15:33Z","timestamp":1784632533936,"version":"3.55.0"},"reference-count":37,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2023,12,26]],"date-time":"2023-12-26T00:00:00Z","timestamp":1703548800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52090043"],"award-info":[{"award-number":["52090043"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52105337"],"award-info":[{"award-number":["52105337"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["advanced.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Advanced Intelligent Systems"],"published-print":{"date-parts":[[2024,3]]},"abstract":"<jats:p>Metallic plastic deformation involves complex microstructural changes and defect evolution, posing challenges in predicting and controlling the quality and performance of formed parts. Therefore, a pressing demand exists for a proficient online defect\u2010sensing system to monitor\u00a0 defects evolution continuously within components during plastic deformation in real time. This article proposes an intelligent online sensing approach for detecting defects in metallic plastic forming based on acoustic emission (AE) and machine learning. A comparative analysis is conducted on AE amplitude signals, stress\u2013strain curves, and defect evolution during the tensile process of TA15 titanium alloy specimens under different stress states. It is found that the defect formation process can be divided into four stages based on the AE amplitude signals. A convolutional neural network model for intelligent defect sensing is established. It leverages transfer learning and is grounded in the relationship between AE signals and the evolution of internal defects. The prediction accuracy using different pretrained models is investigated and compared. It is discerned that utilizing GoogleNet as the pretrained model offers the swiftest training pace with a prediction accuracy of 97.57%. This approach enables intelligent online sensing of internal defect evolution in metal plastic deformation processes.<\/jats:p>","DOI":"10.1002\/aisy.202300616","type":"journal-article","created":{"date-parts":[[2023,12,26]],"date-time":"2023-12-26T23:13:57Z","timestamp":1703632437000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Intelligent Online Sensing of Defects Evolution in Metallic Materials during Plastic Deformation"],"prefix":"10.1002","volume":"6","author":[{"given":"Xuefeng","family":"Tang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Materials Processing and Die &amp; Mould Technology School of Materials Science and Engineering Huazhong University of Science and Technology  Wuhan 430074 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuanyue","family":"He","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Materials Processing and Die &amp; Mould Technology School of Materials Science and Engineering Huazhong University of Science and Technology  Wuhan 430074 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wentian","family":"Guo","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Materials Processing and Die &amp; Mould Technology School of Materials Science and Engineering Huazhong University of Science and Technology  Wuhan 430074 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peixian","family":"Lin","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Materials Processing and Die &amp; Mould Technology School of Materials Science and Engineering Huazhong University of Science and Technology  Wuhan 430074 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Deng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Materials Processing and Die &amp; Mould Technology School of Materials Science and Engineering Huazhong University of Science and Technology  Wuhan 430074 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5377-2026","authenticated-orcid":false,"given":"Xinyun","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Materials Processing and Die &amp; Mould Technology School of Materials Science and Engineering Huazhong University of Science and Technology  Wuhan 430074 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianxin","family":"Xie","sequence":"additional","affiliation":[{"name":"Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing  Beijing 100083 China"},{"name":"Key Laboratory for Advanced Materials Processing (MOE) University of Science and Technology Beijing  Beijing 100083 China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,12,26]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/aisy.202100158"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.3390\/ma15197019"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/aisy.202100014"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-33532-1"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/aisy.202000268"},{"key":"e_1_2_8_7_1","first-page":"3129","volume":"31","author":"Hagino N.","year":"2019","journal-title":"Sens. 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