Prediction of Residual Compressive Strength after Impact Based on Acoustic Emission Characteristic Parameters

被引:2
作者
Zhao, Jingyu [1 ]
Guo, Zaoyang [2 ]
Lyu, Qihui [3 ]
Wang, Ben [3 ]
机构
[1] Syst Design Inst Mech Elect Engn, Beijing 100854, Peoples R China
[2] Harbin Inst Technol, Sch Sci, Shenzhen 518055, Peoples R China
[3] Dongguan Univ Technol, Res Inst Interdisciplinary Sci, Sch Mat Sci & Engn, Dongguan 523808, Peoples R China
关键词
low-velocity impact; residual compressive strength; acoustic emission; machine learning; LOW-VELOCITY IMPACT; COMPOSITES;
D O I
10.3390/polym16131780
中图分类号
O63 [高分子化学(高聚物)];
学科分类号
070305 ; 080501 ; 081704 ;
摘要
This study proposes a prediction method for residual compressive strength after impact based on the extreme gradient boosting model, focusing on composite laminates as the studied material system. Acoustic emission tests were conducted under controlled temperature and humidity conditions to collect characteristic parameters, establishing a mapping relationship between these parameters and residual compressive strength under small sample conditions. The model accurately predicted the residual compressive strength of the laminates after impact, with the coefficient of determination and root mean square error for the test set being 0.9910 and 2.9174, respectively. A comparison of the performance of the artificial neural network model and the extreme gradient boosting model shows that, in the case of small data volumes, the extreme gradient boosting model exhibits superior accuracy and robustness compared to the artificial neural network. Furthermore, the sensitivity of acoustic emission characteristic parameters is analyzed using the SHAP method, revealing that indicators such as peak amplitude, ring count, energy, and peak frequency significantly impact the prediction results of residual compressive strength. The machine-learning-based method for assessing the damage tolerance of composite laminates proposed in this paper utilizes the global monitoring advantages of acoustic emission technology to rapidly predict the residual compressive strength after the impact of composite laminates, providing a theoretical approach for online structural health monitoring of composite laminates. This method is applicable to various composite laminate structures under different impact conditions, demonstrating its broad applicability and reliability.
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页数:15
相关论文
共 30 条
[1]   Recent advances in drilling of carbon fiber-reinforced polymers for aerospace applications: a review [J].
Aamir, Muhammad ;
Tolouei-Rad, Majid ;
Giasin, Khaled ;
Nosrati, Ataollah .
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2019, 105 (5-6) :2289-2308
[2]  
[Anonymous], 2005, STANDARD TEST METHOD, V15, P1, DOI DOI 10.1520/D7136_D7136M-15
[3]  
[Anonymous], 2012, D7137D7137M12 ASTM
[4]   Ultimate Strength Prediction of Carbon/Epoxy Tensile Specimens from Acoustic Emission Data [J].
Arumugam, V. ;
Shankar, R. Naren ;
Sridhar, B. T. N. ;
Stanley, A. Joseph .
JOURNAL OF MATERIALS SCIENCE & TECHNOLOGY, 2010, 26 (08) :725-729
[5]   Explainable extreme gradient boosting tree-based prediction of load-carrying capacity of FRP-RC columns [J].
Bakouregui, Abdoulaye Sanni ;
Mohamed, Hamdy M. ;
Yahia, Ammar ;
Benmokrane, Brahim .
ENGINEERING STRUCTURES, 2021, 245
[6]  
Bensiger SS B., 2014, Mech. Eng, V71, P24514
[7]   Effects of Fiber Architectures on the Impact Resistance of Composite Laminates Under Low-Velocity Impact [J].
Bian, Tianya ;
Lyu, Qihui ;
Fan, Xiaobin ;
Zhang, Xiaomei ;
Li, Xiang ;
Guo, Zaoyang .
APPLIED COMPOSITE MATERIALS, 2022, 29 (03) :1125-1145
[8]  
Borisov V, 2022, Arxiv, DOI arXiv:2110.01889
[9]   XGBoost: A Scalable Tree Boosting System [J].
Chen, Tianqi ;
Guestrin, Carlos .
KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2016, :785-794
[10]   The response of laminated composite plates and profiles under low-velocity impact load [J].
Gliszczynski, A. ;
Kubiak, T. ;
Rozylo, P. ;
Jakubczak, P. ;
Bienias, J. .
COMPOSITE STRUCTURES, 2019, 207 :1-12