{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:20:08Z","timestamp":1773793208123,"version":"3.50.1"},"reference-count":22,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2022,1,13]],"date-time":"2022-01-13T00:00:00Z","timestamp":1642032000000},"content-version":"am","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2022,1,13]],"date-time":"2022-01-13T00:00:00Z","timestamp":1642032000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1835660"],"award-info":[{"award-number":["1835660"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1741677"],"award-info":[{"award-number":["1741677"]}],"id":[{"id":"10.13039\/100000001","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":[[2022,5]]},"abstract":"<jats:sec><jats:label\/><jats:p>Monitoring tool degradation during manufacturing can ensure product accuracy and reliability. However, due to variations in degradation conditions and complexity in signal analysis, effective and broadly applicable monitoring is still challenging to achieve. Herein, a novel monitoring method using ultrasound signals augmented with a numerically trained machine learning technique is reported to monitor the wear condition of friction stir welding and processing tools. Ultrasonic signals travel axially inside the tools, and even minor tool wear will change the time and amplitude of the reflected signal. An artificial intelligence (AI) algorithm is selected as a suitable referee to identify the small variations in the tool conditions based on the reflected ultrasound signals. To properly train the AI referee, a human\u2010error\u2010free data bank using numerical simulation is generated. The simulation models the experimental conditions with high fidelity and can provide comparable ultrasound signals. As a result, the trained AI model can recognize the tool wear from real experiments with subwavelength accuracy prediction of the worn amount on the tool pins.<\/jats:p><\/jats:sec>","DOI":"10.1002\/aisy.202100215","type":"journal-article","created":{"date-parts":[[2022,1,13]],"date-time":"2022-01-13T05:25:57Z","timestamp":1642051557000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Numerically Trained Ultrasound AI for Monitoring Tool Degradation"],"prefix":"10.1002","volume":"4","author":[{"given":"Yuqi","family":"Jin","sequence":"first","affiliation":[{"name":"Center for Agile and Adaptive Additive Manufacturing University of North Texas  Denton TX 76207 USA"},{"name":"Department of Physics University of North Texas  P.O. Box 311427 Denton TX 76203 USA"}]},{"given":"Xinyue","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science Virginia Tech  Blacksburg VA 24061 USA"}]},{"given":"Edward A.","family":"Fox","sequence":"additional","affiliation":[{"name":"Department of Computer Science Virginia Tech  Blacksburg VA 24061 USA"}]},{"given":"Zhiwu","family":"Xie","sequence":"additional","affiliation":[{"name":"University Libraries Virginia Tech  Blacksburg VA 24061 USA"}]},{"given":"Arup","family":"Neogi","sequence":"additional","affiliation":[{"name":"Department of Physics University of North Texas  P.O. Box 311427 Denton TX 76203 USA"}]},{"given":"Rajiv S.","family":"Mishra","sequence":"additional","affiliation":[{"name":"Advanced Materials and Manufacturing Processes Institute Center for Friction Stir Processing University of North Texas  Denton TX 76207 USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7151-2658","authenticated-orcid":false,"given":"Tianhao","family":"Wang","sequence":"additional","affiliation":[{"name":"Energy and Environment Directorate Pacific Northwest National Laboratory  902 Battelle Blvd. Richland WA 99352 USA"}]}],"member":"311","published-online":{"date-parts":[[2022,1,13]]},"reference":[{"key":"e_1_2_10_2_1","unstructured":"W. M.Thomas E. D.Nicholas J. C.Needham M. G.Murch P.Temple-Smith C. 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Symp. on Friction Stir Welding Gothenburg Sweden 2000."},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmatsci.2020.100706"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-021-07531-3"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmatprotec.2017.09.029"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1179\/1362171811Y.0000000023"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1179\/1879139512Y.0000000015"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1006\/mssp.2001.1460"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1115\/1.4029955"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.scriptamat.2011.09.021"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1115\/1.4037242"},{"key":"e_1_2_10_16_1","first-page":"621","volume":"52","author":"Dehelean D.","year":"2008","journal-title":"Weld. 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