文献类型：期刊文献

英文题名：Autonomous characterization of grain size distribution using nonlinear Lamb waves based on deep learning

作者：Liu, Lishuai[1];Wu, Peng[1];Xiang, Yanxun[1];Xuan, Fu-Zhen[1]

机构：[1]East China Univ Sci & Technol, Sch Mech & Power Engn, Key Lab Pressure Syst & Safety MOE, Shanghai 200237, Peoples R China

年份：2022

卷号：152

期号：3

起止页码：1913

外文期刊名：JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA

收录：;EI(收录号：20224112857546);WOS:【SCI-EXPANDED(收录号:WOS:000860558400005)】；

基金：ACKNOWLEDGMENTS This work was supported by the National Natural Science Foundation of China (Grant Nos. 12104155, 12025403, and U1930202), the Natural Science Foundation of Shanghai (Grant No. 21ZR1417100), and the Shanghai Sailing Program (Grant No. 21YF1408900).

语种：英文

外文关键词：Deep learning - Inverse problems - Microstructural evolution - Ultrasonic testing - Ultrasonic waves - Wavelet decomposition

摘要：Characterization of grain microstructures of metallic materials is crucial to materials science and engineering applications. Unfortunately, the universal electron microscopic methodologies can only capture two-dimensional local observations of the microstructures in a time-consuming destructive way. In this regard, the nonlinear ultrasonic technique shows the potential for efficient and nondestructive microstructure characterization due to its high sensitivity to microstructural features of materials, but is hindered by the ill-posed inverse problem for multiparameter estimation induced by the incomplete understanding of the complicated nonlinear mechanical interaction mechanism. We propose an explainable nonlinearity-aware multilevel wavelet decomposition-multichannel one-dimensional convolutional neural network to hierarchically extracts multilevel time-frequency features of the acoustic nonlinearity and automatically model latent nonlinear dynamics directly from the nonlinear ultrasonic responses. The results demonstrate that the proposed approach establishes the complex mapping between acoustic nonlinearity and microstructural features, thereby determining the lognormal distribution of grain size in metallic materials rather than only average grain size. In the meantime, the integration of the designed nonlinearity-aware network and the quantitative analysis of component importance provides an acceptable physical explainability of the deep learning approach for the nonlinear ultrasonic technique. Our study shows the promise of this technique for real-time in situ evaluation of microstructural evolution in various applications. (C) 2022 Acoustical Society of America.