Leveraging physics-informed neural networks for wavefield analysis in laser ultrasonic testing
文献类型：期刊论文
| 作者 | Li, Yang1; Xu, Bin1; Zou, Yun1; Sha, Gaofeng2; Cai, Guixi3 |
| 刊名 | NONDESTRUCTIVE TESTING AND EVALUATION
 |
| 出版日期 | 2024-12-23 |
| 页码 | 23 |
| 关键词 | Physics-informed neural networks wavefield reconstruction wavefield prediction laser ultrasonic non-destructive testing |
| ISSN号 | 1058-9759 |
| DOI | 10.1080/10589759.2024.2443768 |
| 通讯作者 | Li, Yang(yangli@zzu.edu.cn) |
| 英文摘要 | Laser Ultrasonic (LU) technology has emerged as a pivotal non-destructive testing method, offering a unique capability to visualise ultrasonic wavefields and identify defects without causing structural damage. However, challenges arise in certain testing scenarios where direct laser irradiation of the sample surface is hindered, resulting in incomplete LU wavefield datasets. This limitation poses a significant obstacle in accurately assessing material integrity and defect detection. This paper explores the application of Physics-Informed Neural Networks (PINNs) for LU wavefield reconstruction and prediction. PINNs are employed to reconstruct wavefields from incomplete data and predict wavefield behaviour at different time instances. Results demonstrate PINNs' effectiveness in accurately reconstructing wavefields, with correlation coefficients exceeding 0.94 between reconstructed and actual wavefields. Additionally, PINNs show promise in predicting LU wavefield data, albeit with slightly reduced accuracy beyond the training range. Moreover, PINNs effectively reduce noise in wavefield data, enhancing clarity and reliability. This study lays groundwork for further exploration of PINNs in LU defect detection. |
| 资助项目 | National Natural Science Foundation of China[51705470] ; Henan Provincial Key Scientific Research Project of Higher Education Institutions[222102220025] |
| WOS研究方向 | Materials Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001381216500001 |
| 出版者 | TAYLOR & FRANCIS LTD |
| 资助机构 | National Natural Science Foundation of China ; Henan Provincial Key Scientific Research Project of Higher Education Institutions |
| 源URL |  |
| 专题 | 金属研究所_中国科学院金属研究所 |
| 通讯作者 | Li, Yang |
| 作者单位 | 1.Zhengzhou Univ, Sch Mech & Power Engn, Zhengzhou, Peoples R China 2.Clover Pk Tech Coll, Sch Adv Mfg, Lakewood, WA USA 3.Chinese Acad Sci, Inst Met Res, Shenyang, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Yang,Xu, Bin,Zou, Yun,et al. Leveraging physics-informed neural networks for wavefield analysis in laser ultrasonic testing[J]. NONDESTRUCTIVE TESTING AND EVALUATION,2024:23. |
| APA | Li, Yang,Xu, Bin,Zou, Yun,Sha, Gaofeng,&Cai, Guixi.(2024).Leveraging physics-informed neural networks for wavefield analysis in laser ultrasonic testing.NONDESTRUCTIVE TESTING AND EVALUATION,23. |
| MLA | Li, Yang,et al."Leveraging physics-informed neural networks for wavefield analysis in laser ultrasonic testing".NONDESTRUCTIVE TESTING AND EVALUATION (2024):23. |
入库方式： OAI收割
来源：金属研究所
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