基于声发射信号稀疏表示的铝合金板材拉伸过程识别方法

doi:10.3901/JME.2024.02.001

机械工程学报 ›› 2024, Vol. 60 ›› Issue (2): 1-9.doi: 10.3901/JME.2024.02.001

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基于声发射信号稀疏表示的铝合金板材拉伸过程识别方法

焦敬品¹, 孙延东¹, 李光海², 赵鹏经¹, 吴斌¹, 何存富¹

1. 北京工业大学材料与制造学部北京 100124;
2. 中国特种设备检测研究院北京 100029

收稿日期:2023-01-25 修回日期:2023-06-15 出版日期:2024-01-20 发布日期:2024-04-09
通讯作者: 焦敬品(通信作者)，女，1973年出生，博士，教授，博士研究生导师。主要研究方向为现代测控技术与方法、无损检测新技术、现代信号分析与处理技术、新型传感器技术。E-mail：jiaojp@bjut.edu.cn
作者简介:孙延东，男，1994年出生。主要研究方向为声信号识别与分类方法。E-mail：sd_yandong@163.com
基金资助:
国家自然科学基金资助项目(11972053, 12274012)。

Sparse Representation of Acoustic Emission Signals for Identifying Tensile Process of Aluminum Alloy Sheets

JIAO Jingpin¹, SUN Yandong¹, LI Guanghai², ZHAO Pengjing¹, WU Bin¹, HE Cunfu¹

1. Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124;
2. China Special Equipment Inspection and Research Institute, Beijing 100029

Received:2023-01-25 Revised:2023-06-15 Online:2024-01-20 Published:2024-04-09

摘要/Abstract

摘要： 针对钣金成形过程质量监测的需要，提出一种基于声发射信号稀疏表示的铝合金板材拉伸过程自动识别方法。该方法对钣金拉伸过程监测的声发射信号进行非负矩阵分解，提取其在低维子空间映射的特征系数，用于构造训练字典和测试样本，并利用l₁范数进行稀疏分解和信号重构，进而实现对拉伸过程中弹性、塑性、屈服、强化和颈缩5个不同应力-应变状态的自动识别。同时，研究了声发射信号的数据类型对不同应力-应变状态识别准确率的影响。结果表明，提出的基于声发射信号稀疏表示方法可以很好实现铝合金拉伸过程中5个不同应力-应变状态的识别，相较于快速傅里叶变换类型数据，利用短时傅里叶变换数据的识别准确率更高。研究工作为钣金成型质量监控提供了可行的解决方案。

关键词: 拉伸过程, 稀疏表示, 声发射, 应力-应变状态, 非负矩阵分解

Abstract: Aiming at the need of quality monitoring in sheet metal forming process, an automatic identification method of aluminum alloy sheet drawing process based on sparse representation of acoustic emission signals was proposed. The method to monitor the process of sheet metal drawing of the acoustic emission signal is non-negative matrix factorization, and extract the mapping on the low-dimensional subspace characteristic coefficient of dictionary is used to construct the training and testing samples, and the use of l₁ norm for sparse solution and the signal reconstruction, thus realize the tensile elasticity, plasticity, yield, hardening, and in the process of necking five different stress-strain state of automatic identification. At the same time, the influence of data types of acoustic emission signals on the recognition accuracy of different stress-strain states is studied. The results show that the proposed sparse representation method based on acoustic emission signals can well realize the identification of five different stress-strain states in the tensile process of aluminum alloy. Compared with Fast Fourier Transform data, the identification accuracy of Short-Time Fourier Transform data is higher. The research provides a feasible solution for the quality control of sheet metal forming.

Key words: tensile process, sparse representation, acoustic emission, stress-strain state, non-negative matrix factorization

中图分类号:

TM154

焦敬品, 孙延东, 李光海, 赵鹏经, 吴斌, 何存富. 基于声发射信号稀疏表示的铝合金板材拉伸过程识别方法[J]. 机械工程学报, 2024, 60(2): 1-9.

JIAO Jingpin, SUN Yandong, LI Guanghai, ZHAO Pengjing, WU Bin, HE Cunfu. Sparse Representation of Acoustic Emission Signals for Identifying Tensile Process of Aluminum Alloy Sheets[J]. Journal of Mechanical Engineering, 2024, 60(2): 1-9.

参考文献

[1] BEHRENS B A, BOUGUECHA A, BUSE C. Potentials of in situ monitoring of aluminum alloy forging by acoustic emission[J]. Archives of Civil and Mechanical Engineering, 2016, 16(4):724-733.
[2] BEHRENS B A, SANTANGELO A, BUSE C. Acoustic emission technique for online monitoring during cold forging of steel components:a promising approach for online crack detection in metal forming processes[J]. Production Engineering, 2013, 7(4):423-432.
[3] LONG X J, LI Q F, HE C H. Acoustic emission monitoring and evaluation for rolled steel damage under different tensile rates[J]. Journal of Vibration and Shock, 2017, 36(7):219-225.
[4] 周友行, 张俏, 田茂, 等.批量钻削监测信号双谱特征融合研究及应用[J].机械工程学报, 2014, 50(24):24-30.
ZHOU Youhang, ZHANG Qiao, TIAN Mao, et al. Research and application of bi-spectrum features fusion based on batch drilling monitoring signals[J]. Journal of Mechanical Engineering, 2014, 50(24):24-30.
[5] BRAVO A, TOUBAL L, KOFFI D. Development of novel green and biocomposite materials:Tensile and flexural properties and damage analysis using acoustic emission[J]. Materials and Design, 2014, 66(66):16-28.
[6] 张颖, 吴昊, 高晗, 等. 20#钢不同晶粒度试件拉伸损伤试验的声发射特性研究[J].应用声学, 2017, 36(3):228-233.
ZHANG Ying, WU Hao, GAO Han, et al. The acoustic emission characteristics of 20#steel with different grain size specimens tensile damage tests[J]. Applied Acoustics, 2017, 36(3):228-233.
[7] 丁利伟, 沈玉娣.复合材料拉伸过程的声发射特性研究[J].无损检测, 2009, 31(10):781-785.
DING Liwei, SHEN Yudi. Research on Acoustic emission characteristics of the composite material during tensile test[J]. Nondestructive Testing, 2009, 31(10):781-785.
[8] 李雪换, 底月兰, 王海斗, 等.基于声发射技术的热障涂层拉伸失效模式研究[J].机械工程学报, 2020, 56(14):57-64.
LI Xuehuan, DI Yuelan, WANG Haidou, et al. Research on crack failure modes of thermal barrier coatings based on acoustic emission technique[J]. Journal of Mechanical Engineering, 2020, 56(14):57-64.
[9] CEVAT T S, MEHDI A N, MANOUCHEHR S. Investigation of surface damages during sheet metal forming using acoustic emission[J]. Proceedings of the Institution of Mechanical Engineers, Part J:Journal of Engineering Tribology, 2013, 227(3):286-296.
[10] SHANBHAG V V, ROLFE B F, ARUNACHALAM N. Investigating galling wear behaviour in sheet metal stamping using acoustic emissions[J]. Wear, 2018, 414(11):31-42.
[11] TAHA Z, WIDIYATI K. Artificial neural network for bearing defect detection based on acoustic emission[J]. International Journal of Advanced Manufacturing Technology, 2010, 50(1-4):289-296.
[12] GODIN N, HUGUET S, GAERTNER R. Clustering of acoustic emission signals collected during tensile tests on unidirectional glass/polyester composite using supervised and unsupervised classifiers[J]. NDT & E International, 2004, 37(4):253-260.
[13] ZHANG Z, XU Y, YANG J. A survey of sparse representation:Algorithms and applications[J]. IEEE Access, 2017, 3:490-530.
[14] 王华庆, 任帮月, 宋浏阳, 等.基于终止准则改进K-SVD字典学习的稀疏表示特征增强方法[J].机械工程学报, 2019, 55(7):35-43.
WANG Huaqing, REN Bangyue, SONG Liuyang, et al. Sparse representation method based on termination criteria improved K-SVD dictionary learning for feature enhancement[J]. Journal of Mechanical Engineering, 2019, 55(7):35-43.
[15] QIAO Lishan, CHEN Songcan, TAN Xiaoyang. Sparsity preserving projections with applications to face recognition[J]. Pattern Recognition, 2010, 4(2):331-341.
[16] YE Mengjun, HU Changhui, WAN Liguang, et al. Fast single sample face recognition based on sparse representation classification[J]. Multimedia Tools and Applications, 2020, 80(3):1-23.
[17] 王银玲, 李华聪.遗传算法优化的碳纤维复合材料声发射数据聚类分析[J].无损检测, 2019, 41(6):1-5, 37.
WANG Yinling, LI Huacong. Clustering Analysis of acoustic emission data of carbon fiber composites optimized by genetic algorithm[J]. Nondestructive Testing, 2019, 41(6):1-5, 37.
[18] TONG X, ZHANG J, YAO L. Cluster analysis of acoustic emission signals of 2D-C/SiC under tensile loading[J]. Chinese Journal of Solid Mechanics, 2014, 35(2):109-114.
[19] AHARON M, ELAD M, BRUCKSTEIN A. K-SVD:An algorithm for designing overcomplete dictionaries for sparse representation[J]. IEEE Transactions on Signal Processing, 2006, 54(11):4311-4322.
[20] ZHUANG H, YANG M, CUI Z. A method for static hand gesture recognition based on non-negative matrix factorization and compressive sensing[J]. IAENG International Journal of Computer Science, 2017, 44(1):52-59.

基于声发射信号稀疏表示的铝合金板材拉伸过程识别方法

Sparse Representation of Acoustic Emission Signals for Identifying Tensile Process of Aluminum Alloy Sheets

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