文献类型：期刊文献

英文题名：Nonlinear ultrasonic characterization of average grain size based on deep learning

作者：Wu, Peng[1]; Liu, Lishuai[1,2,3]; Xiang, Yanxun[1]; Xuan, Fu-Zhen[1]

机构：[1] East China University Of Science And Technology, Shanghai Key Laboratory Of Intelligent Sensing And Detection Technology, School Of Mechanical And Power Engineering, Shanghai, China; [2] China Southern Power Grid Co., Ltd., Electric Power Research Institute, China; [3] Shanghai Institute Of Aircraft Mechanics And Control, Shanghai, 200092, China

年份：2024

起止页码：248

外文期刊名：Proceedings of 2024 IEEE Far East NDT New Technology and Application Forum, FENDT 2024

收录：EI(收录号：20253118909792)

语种：英文

外文关键词：Grain growth - Grain size and shape - Materials testing - Ultrasonic testing

摘要：The grain size of metal materials has a great impact on their mechanical properties, and ultrasonic testing has been widely used in characterizing the grain size of metal materials. However, linear ultrasonic testing represented by attenuation and sound velocity is difficult to distinguish small-sized grains, especially in the early stages of grain growth. Nonlinear ultrasonic testing technology has high sensitivity to microscale microstructure evolution, but the complex changes in acoustic nonlinear parameters inevitably lead to underdetermined problems when accurately characterizing the average grain size. This article proposes the use of depth studies to address the dilemma of characterizing the average grain size of metal materials using nonlinear ultrasound. Using characteristic parameters containing ultrasonic nonlinear effects as inputs for multi-layer perceptron and LSTM network, an implicit mapping of acoustic nonlinear effects and average grain size of polycrystalline pure copper is constructed to achieve high sensitivity characterization of small grains that cannot be measured by traditional ultrasonic testing. ? 2024 IEEE.