{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T06:49:55Z","timestamp":1774594195262,"version":"3.50.1"},"reference-count":59,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T00:00:00Z","timestamp":1674604800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Foundation of Korea (NRF)","award":["NRF-2017R1A6A1A03015562"],"award-info":[{"award-number":["NRF-2017R1A6A1A03015562"]}]},{"name":"National Research Foundation of Korea (NRF)","award":["NRF-2021R1F1A1056483"],"award-info":[{"award-number":["NRF-2021R1F1A1056483"]}]},{"name":"National Research Foundation of Korea (NRF)","award":["GRRC TUKorea2020-B03"],"award-info":[{"award-number":["GRRC TUKorea2020-B03"]}]},{"name":"GRRC program of Gyeonggi province","award":["NRF-2017R1A6A1A03015562"],"award-info":[{"award-number":["NRF-2017R1A6A1A03015562"]}]},{"name":"GRRC program of Gyeonggi province","award":["NRF-2021R1F1A1056483"],"award-info":[{"award-number":["NRF-2021R1F1A1056483"]}]},{"name":"GRRC program of Gyeonggi province","award":["GRRC TUKorea2020-B03"],"award-info":[{"award-number":["GRRC TUKorea2020-B03"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this study, the Convolution Neural Network (CNN) algorithm is applied for non-destructive evaluation of aluminum panels. A method of classifying the locations of defects is proposed by exciting an aluminum panel to generate ultrasonic Lamb waves, measuring data with a sensor array, and then deep learning the characteristics of 2D imaged, reflected waves from defects. For the purpose of a better performance, the optimal excitation location and sensor locations are investigated. To ensure the robustness of the training model and extract the feature effectively, experimental data are collected by slightly changing the excitation frequency and shifting the location of the defect. The high classification accuracy for each defect location can be achieved. It is found that the proposed algorithm is also successfully applied even when a bar is attached to the panel.<\/jats:p>","DOI":"10.3390\/s23031349","type":"journal-article","created":{"date-parts":[[2023,1,26]],"date-time":"2023-01-26T01:30:30Z","timestamp":1674696630000},"page":"1349","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Optimal Transducer Placement for Deep Learning-Based Non-Destructive Evaluation"],"prefix":"10.3390","volume":"23","author":[{"given":"Ji-Yun","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Tech University of Korea, Siheung-si 15073, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9930-0083","authenticated-orcid":false,"given":"Je-Heon","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Tech University of Korea, Siheung-si 15073, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/S0266-3538(99)00094-9","article-title":"Non-Destructive Detection of Fatigue Damage in Thick Composites by Pulse-Echo Ultrasonics","volume":"60","author":"Mouritz","year":"2000","journal-title":"Compos. 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