基于深度学习的材料超声回波衰减预测方法  

Material ultrasonic echo attenuation prediction method based on deep learning

作　　者：刘骁 沙正骁 梁菁 LIU Xiao;SHA Zhengxiao;LIANG Jing(AECC Beijing Institute of Aeronautical Materials,Beijing 100095,China;Beijing Key Laboratory of Aeronautical Materials Testing and Evaluation,Beijing 100095,China;Key Laboratory of Science and Technology on Aeronautical Materials Testing and Evaluation,Aeroengine Corporation of China,Beijing 100095,China)

机构地区：[1]中国航发北京航空材料研究院,北京100095 [2]航空材料检测与评价北京市重点实验室,北京100095 [3]中国航空发动机集团材料检测与评价重点实验室,北京100095

出　　处：《应用声学》2023年第3期529-539,共11页Journal of Applied Acoustics

摘　　要：材料超声回波衰减是评价材料均匀一致性的常用方法。针对具有复杂结构的航空发动机盘件难以进行材料底面超声回波衰减评价的问题,该文提出了利用超声背散射波信号直接预测底面回波衰减的方法。采用10 MHz聚焦探头进行超声背散射波数据的采集,利用深度学习技术构建和训练模型,建立了基于深度学习的材料底面回波衰减预测方法,同时讨论了采用不同信号形式的超声波信号分类识别模型的准确率差异。研究发现:基于深度学习技术可实现通过超声背散射波预测材料的底面回波衰减,预测结果和实际底面回波衰减实验结果具有良好的一致性。Aiming at the difficulty of evaluating the material bottom ultrasonic echo attenuation of aero-engine disks with complex structures,this paper proposes a method to directly predict the bottom echo attenuation using ultrasonic backscattered wave signals.A 10 MHz focusing probe is used to collect the detection data set.Through the construction and training of models by deep learning,an prediction method for bottom echo attenuation of materials based on deep learning is established.At the same time,the accuracy difference of ultrasonic signal classification and recognition models with different signal forms is discussed.The study found that the use of deep learning can realize prediction of the bottom echo attenuation of materials through ultrasonic backscattered waves,and the predicted results are in good agreement with the actual bottom echo attenuation test results.

关 键 词：超声检测 底面回波衰减 深度学习 超声背散射波 

分 类 号：TG115.285[金属学及工艺—物理冶金]