AM-1DCNN Based Automatic Recognition of Mechanical Damage in Composite Material

doi:10.3901/JME.260191

Abstract

Abstract: In order to solve the bottleneck of automatic identification of mechanical damage of carbon fiber reinforced polymer (CFRP), an attention module (AM) and one dimensional convolutional neural network (1DCNN) damage identification method was proposed, based on the nonlinear ultrasonic detection technique. Firstly, one-dimensional ultrasonic vibration signals of different mechanical damage types of composite materials were obtained by ultrasonic testing platform and finite element simulation method. After the model training was completed, the model was used to judge the damage type of the test data set of carbon fiber composites. Finally, try to identify the signals of different damage types of composites, such as undamaged, perforated, split and low-speed impact damage. The results show that the proposed model has strong scalability and fast convergence, and the recognition accuracy of common mechanical damage types of CFRP is 96.79%, which is 2.07% higher than that of the traditional CNN model, laying a foundation for the automatic diagnosis of damage in CFRP composites in service environment in universities.

Key words: composite material, one-dimensional convolution neural network, attention module, damage identification

CLC Number:

TB559
TB332

JIA Kangkang, DU Junmin, CHEN Feiyu, SHI Jianwei, HOU Guoyi, LI Cheng. AM-1DCNN Based Automatic Recognition of Mechanical Damage in Composite Material[J]. Journal of Mechanical Engineering, 2025, 62(6): 247-256.

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URL: http://www.cjmenet.com.cn/EN/10.3901/JME.260191

http://www.cjmenet.com.cn/EN/Y2025/V62/I6/247

References

[1] 杜善义. 先进复合材料与航空航天[J]. 复合材料学报，2007，24(1)：1-12. DU Shanyi. Advanced composite materials and aerospace engineering[J]. Acta Materiae Compositae Scinica，2007，24(1)：1-12.
[2] 范金娟，侯学勤，刘杰. 平面编织复合材料层压板的冲击失效行为[J]. 高分子材料科学与工程，2013，39(1)：92-95. FAN Jinjuan，HOU Xueqin，LIU Jie. Failure behavior of plain woven composite laminates under impact loading[J]. Polymeric Materials Science and Engineering，2013，39(1)：92-95.
[3] 李健，郭薇，杨晓霞，等. 超声相控阵检测CFRP缺陷识别方法[J]. 天津大学学报，2015，48(8)：750-756. LI Jian，GUO Wei，YANG Xiaoxia，et al. A flaw classification method for ultrasonic phased array inspection of CFRP[J]. Journal of Tianjin University，2015，48(8)：750-756.
[4] 李萍，宋波，毛捷，等. 深度学习在超声检测缺陷识别中的应用与发展[J]. 应用声学，2019，38(3)：458-464. LI Ping，SONG Bo，MAO Jie，et al. Application and development of defect recognition using deep learning in ultrasonic testing[J]. Journal of Applied Acoustics，2019，38(3)：458-464.
[5] 刘雨生，李洋，滕利臣，等. 基于超声复解析信号的碳纤维层压复合材料检测方法研究[J]. 机械工程学报，2020，56(24)：31-38. LIU Yusheng，LI Yang，TENG Lichen，et al. Research on ultrasonic testing method for carbon fiber laminated composite using ultrasonic analytical signal[J]. Journal of Mechanical Engineering，2020，56(24)：31-38.
[6] LIU Z，YANG C，HUANG J，et al. Deep learning framework based on integration of S-Mask R-CNN and Inception-v3 for ultrasound image-aided diagnosis of prostate cancer[J]. Future Generation Computer Systems，2021，114：358-367.
[7] ZARGAR S A，YUAN F G. Impact diagnosis in stiffened structural panels using a deep learning approach[J]. Structural Health Monitoring，2021，20(2)：681-691.
[8] LECUN Y，BENGIO Y，HINTON G. Deep learning[J]. Nature，2015，521(7553)：436-444.
[9] KHAN A，SOHAIL A，ZAHOORA U，et al. A survey of the recent architectures of deep convolutional neural networks[J]. Artificial Intelligence Review，2020，53(8)：5455-5516.
[10] JIA D，WEI D，SOCHER R，et al. ImageNet: A large-scale hierarchical image database[C]// 2009 IEEE Conference on Computer Vision and Pattern Recognition，20-25 June 2009，Miami，FL，USA，2009：248-255.
[11] 孟宗，关阳，潘作舟，等. 基于二次数据增强和深度卷积的滚动轴承故障诊断研究[J]. 机械工程学报，2021，57(23)：106-115. MENG Zong，GUAN Yang，PAN Zuozhou，et al. Fault diagnosis of rolling bearing based on secondary data enhancement and deep convolutional network[J]. Journal of Mechanical Engineering，2021，57(23)：106-115.
[12] 薛妍，沈宁，窦东阳. 基于一维卷积神经网络的滚动轴承故障程度诊断[J]. 轴承，2021(4)：48-54. XUE Yan，SHEN Ning，DOU Dongyang. Fault degree diagnosis of rolling bearings based on one-dimensional convolutional neural network[J]. Bearing，2021(4)：48-54.
[13] 王辉，徐佳文，严如强. 基于多尺度注意力深度强化学习网络的行星齿轮箱智能诊断方法[J]. 机械工程学报，2022，58(11)：133-142. WANG Hui，XU Jiawen，YAN Ruqiang. Multi-scale attention based deep reinforcement learning for intelligent fault diagnosis of planetary gearbox[J]. Journal of Mechanical Engineering，2022，58(11)：133-142.
[14] 叶壮，余建波. 基于多通道一维卷积神经网络特征学习的齿轮箱故障诊断方法[J]. 振动与冲击，2020，39(20)：55-66. YE Zhuang，YU Jianbo. Gearbox fault diagnosis based on feature learning of multi channel one dimensional convolutional neural network[J]. Journal of Vibration and Shock，2020，39(20)：55-66.
[15] 高佳豪，郭瑜，伍星. 基于SANC和一维卷积神经网络的齿轮箱轴承故障诊断[J]. 振动与冲击，2020，39(19)：204-209，257. GAO Jiahao，GUO Yu，WU Xing. Gearbox bearing fault diagnosis based on SANC and 1-D CNN[J]. Journal of Vibration and Shock，2020，39(19)：204-209，257.
[16] 陈祝云，钟琪，黄如意，等. 基于增强迁移卷积神经网络的机械智能故障诊断[J]. 机械工程学报，2021，57(21)：96-105. CHEN Zhuyun，ZHONG Qi，HUANG Ruyi，et al. Intelligent fault diagnosis for machinery based on enhanced transfer convolutional neural network[J]. Journal of Mechanical Engineering，2021，57(21)：96-105.
[17] 刘洋，程强，史曜炜，等. 基于注意力模块及1D-CNN的滚动轴承故障诊断[J]. 太阳能学报，2022，43(3)：463-468. LIU Yang，CHENG Qiang，SHI Yaowei，et al. Fault diagnosis of rolling beraings based on attention module and 1D-CNN[J]. Acta Energiae Solaris Sinica，2022，43(3)：463-468.
[18] KHAN A，KO D K，LIM S C，et al. Structural vibration-based classification and prediction of delamination in smart composite laminates using deep learning neural network[J]. Composites Part B： Engineering，2019，161：586-594.
[19] MENG M，CHUA Y J，WOUTERSON E，et al. Ultrasonic signal classification and imaging system for composite materials via deep convolutional neural networks[J]. Neurocomputing，2017，257：128-135.
[20] WOO S，PARK J，LEE J-Y，et al. CBAM : Convolutional block attention[C]// Proceedings of the 15th European Conference on Computer Vision (ECCV)，2018：3-19.
[21] 郭建. CFRP板冲击损伤的脉冲反相非线性超声检测研究[D]. 郑州：郑州大学，2020. GUO Jian. Nonlinear ultrasonic detection of impact damage in CFRP panels based on pulse inversion technique[D]. Zhengzhou：Zhengzhou University，2021.
[22] BATHE K J. Finite element procedures in engineering analysis[J]. Pressure Vessel Technology，1982，106：421-422.
[23] 张鸿庆，王鸣. 有限元的数学理论[M]. 北京：科学出版社，1991. ZHANG Hongqing，WANG Ming. Mathematical theory of finite element[M]. Beijing：Science Press，1991.