相控阵超声检测缺陷识别与分类研究进展
Research progress on defect recognition and classification by using phased array ultrasonic testing
摘 要
相控阵超声技术是近年来无损检测领域的重点研究方向之一，已经取得了飞速发展，其中基于相控阵超声成像的缺陷识别与分类是研究的热点之一。概述了相控阵超声无损检测的基本原理，介绍了具有代表性的缺陷识别与分类算法，包括支持向量机、人工神经网络、遗传算法、神经进化算法和基于深度学习的算法。最后指出了现有缺陷识别与分类算法面临的挑战，并结合实际提出了相控阵超声缺陷识别与分类的发展方向。
超声无损检测
超声成像
缺陷识别与分类
phased array ultrasonic
ultrasonic nondestructive testing
ultrasonic imaging
defect recognition and classification
Abstract
Phased array ultrasonic technology is one of the key research directions in the field of nondestructive testing in recent years and has made rapid development, among which defect recognition and classification based on ultrasonic phased array imaging is one of the research hotspots. This paper summarized the basic principles of ultrasonic phased array nondestructive testing and introduced representative defect recognition and classification algorithms, including support vector machines, artificial neural networks, genetic algorithms, neural evolutionary algorithms, and algorithms based on deep learning. Finally, it pointed out the challenges of existing defect recognition and classification algorithms and put forward the development direction of ultrasonic phased array defect recognition and classification.
中图分类号 TG115.28 DOI 10.11973/wsjc202312007
所属栏目 “2023 Evident杯相控阵超声检测技术优秀论文评选”活动获奖论文
基金项目
收稿日期 2023/4/26
修改稿日期
网络出版日期
作者单位点击查看
联系人作者陈钦(chenqin@cardc.cn)
备注刘春华(1986-),男,硕士,工程师,主要从事特种设备检验与检测工作
引用该论文: LIU Chunhua,ZHOU Changlin,CHEN Xiaohui,CHEN Qin. Research progress on defect recognition and classification by using phased array ultrasonic testing[J]. Nondestructive Testing, 2023, 45(12): 31～37
刘春华,周长霖,陈晓辉,陈钦. 相控阵超声检测缺陷识别与分类研究进展[J]. 无损检测, 2023, 45(12): 31～37
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参考文献
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【2】张碧星, 张萍, 阎守国, 等.超声成像检测研究进展[J].陕西师范大学学报(自然科学版), 2022, 50(6):1-16.
【3】SAMBATH S, NAGARAJ P, SELVAKUMAR N.Automatic defect classification in ultrasonic NDT using artificial intelligence[J].Journal of Nondestructive Evaluation, 2011, 30(1):20-28.
【4】CHAKI S, KRAWCZAK P.Non-destructive health monitoring of structural polymer composites:trends and perspectives in the digital era[J].Materials, 2022, 15(21):7838.
【5】孙芳. 超声相控阵技术若干关键问题的研究[D].天津:天津大学, 2012.
【6】靳世久, 杨晓霞, 陈世利, 等.超声相控阵检测技术的发展及应用[J].电子测量与仪器学报, 2014, 28(9):925-934.
【7】WEN JJ, BREAZEALE M A.A diffraction beam field expressed as the superposition of Gaussian beams[J].The Journal of the Acoustical Society of America, 1988, 83(5):1752-1756.
【8】丁辉. 计算超声学:声场分析及应用[M].北京:科学出版社, 2010.
【9】CASSEREAU D, FINK M.Time-reversal of ultrasonic fields.III.Theory of the closed time-reversal cavity[J].IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 1992, 39(5):579-592.
【10】HOLMES C, DRINKWATER B W, WILCOX P D.Post-processing of the full matrix of ultrasonic transmit-receive array data for non-destructive evaluation[J].NDT & E International, 2005, 38(8):701-711.
【11】LONG R, RUSSELL J, CAWLEY P.Ultrasonic phased array inspection using full matrix capture[J].Insight-Non-Destructive Testing and Condition Monitoring, 2012, 54(7):380-385.
【12】YAN X F, BRADY D J, ZHANG W P, et al.Compressive sampling for array cameras[J].SIAM Journal on Imaging Sciences, 2021, 14(1):156-177.
【13】SOLODOV I Y.Ultrasonics of non-linear contacts:propagation, reflection and NDE-applications[J].Ultrasonics, 1998, 36(1):383-390.
【14】MEZIANE A, NORRIS A N, SHUVALOV A L.Nonlinear shear wave interaction at a frictional interface:energy dissipation and generation of harmonics[J].The Journal of the Acoustical Society of America, 2011, 130(4):1820-1828.
【15】HAUPERT S, RENAUD G, SCHUMM A.Ultrasonic imaging of nonlinear scatterers buried in a medium[J].NDT & E International, 2017, 87:1-6.
【16】凌田昊, 郑慧峰, 何虹儒, 等.基于振动声调制的微裂纹定位成像研究[J].计量学报, 2020, 41(2):214-220.
【17】HONARVAR F, VARVANI-FARAHANI A.A review of ultrasonic testing applications in additive manufacturing:defect evaluation, material characterization, and process control[J].Ultrasonics, 2020, 108:106227.
【18】XU Q A, WANG H T.Sound field modeling method and key imaging technology of an ultrasonic phased array:a review[J].Applied Sciences, 2022, 12(16):7962.
【19】刘春华, 陈鹏, 陈晓辉, 等.一种基于超声图像的螺栓螺纹缺陷检测方法:CN115345876B[P].2023-04-07.
【20】WANG H B, FAN Z C, CHEN X D, et al.Automated classification of pipeline defects from ultrasonic phased array total focusing method imaging[J].Energies, 2022, 15(21):8272.
【21】YANG XX, CHEN S L, JIN S J, et al.Crack orientation and depth estimation in a low-pressure turbine disc using a phased array ultrasonic transducer and an artificial neural network[J].Sensors, 2013, 13(9):12375-12391.
【22】SONG J Y, LIU Y Y, MA S W.Ultrasonic phased array sparse-TFM imaging based on deep learning and genetic algorithm[C]//2021 International Conference on Image, Video Processing, and Artificial Intelligence.Shanghai, China:SPIE, 2021.
【23】汪子君, 戴景民, 李志强.进化神经网络及多目标进化算法在红外无损检测中的应用[J].无损检测, 2012, 34(7):24-27.
【24】MUNIR N, KIM H J, PARK J, et al.Convolutional neural network for ultrasonic weldment flaw classification in noisy conditions[J].Ultrasonics, 2019, 94:74-81.
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