SHI Y, PINNA C, SOUTIS C. Low-Velocity Impact of Composite Laminates: Damage Evolution [M] //Dynamic Deformation, Damage and Fracture in Composite Materials and Structures. Amsterdam: Elsevier, 2023: 89-119.
ZHANG X F, WU X, HE Y Z, et al. CFRP Barely Visible Impact Damage Inspection Based on an Ultrasound Wave Distortion Indicator [J]. Composites Part B: Engineering, 2019, 168: 152-158.
LIANG T, REN W W, TIAN G Y, et al. Low Energy Impact Damage Detection in CFRP Using Eddy Current Pulsed Thermography [J]. Composite Structures, 2016, 143: 352-361.
BALAGEAS D L, ROCHE J M, LEROY F H, et al. The Thermographic Signal Reconstruction Method: A Powerful Tool for the Enhancement of Transient Thermographic Images [J]. Biocybernetics and Biomedical Engineering, 2015, 35(1): 1-9.
张峻铭, 杨伟东, 李岩. 人工智能在复合材料研究中的应用[J]. 力学进展, 2021, 51(4): 865-900.
KARSANDIK Y, SABUNCUOGLU B, YILDIRIM B, et al. Impact Behavior of Sandwich Composites for Aviation Applications: A Review [J]. Composite Structures, 2023, 314: 116941.
LI Z Y, MA Z, WANG J F, et al. Low-Velocity Impact Behavior and Damage Mechanisms of Honeycomb Sandwich Structures with Elastomeric Interlayers in CFRP Skins [J]. Thin-Walled Structures, 2024, 205: 112482.
LIAO J, GUO L P, JIANG L, et al. A Machine Learning-Based Feature Extraction Method for Image Classification Using ResNet Architecture [J]. Digital Signal Processing, 2025, 160: 105036.
CHEN Y, SHARIFUZZAMAN S A S M, WANG H X, et al. Deep Learning Based Underground Sewer Defect Classification Using a Modified RegNet [J]. Computers, Materials & Continua, 2023, 75(3): 5455-5473.
SUMIT S S, ANAVATTI S, TAHTALI M, et al. ResNet-Lite: On Improving Image Classification with a Lightweight Network [J]. Procedia Computer Science, 2024, 246: 1488-1497.
OLIVEIRA B C F, SEIBERT A A, BORGES V K, et al. Employing a U-Net Convolutional Neural Network for Segmenting Impact Damages in Optical Lock-in Thermography Images of CFRP Plates [J]. Nondestructive Testing and Evaluation, 2021, 36(4): 440-458.
LIU H C, LI W H, YANG L C, et al. Automatic Reconstruction of Irregular Shape Defects in Pulsed Thermography Using Deep Learning Neural Network [J]. Neural Computing and Applications, 2022, 34(24): 21701-21714.
WEI Z A, FERNANDES H, HERRMANN H G, et al. A Deep Learning Method for the Impact Damage Segmentation of Curve-Shaped CFRP Specimens Inspected by Infrared Thermography [J]. Sensors, 2021, 21(2): 395.
OLIVER G A, ANCELOTTI A C, GOMES G F. Neural Network-Based Damage Identification in Composite Laminated Plates Using Frequency Shifts [J]. Neural Computing and Applications, 2021, 33(8): 3183-3194.
DENG K L, LIU H C, YANG L C, et al. Classification of Barely Visible Impact Damage in Composite Laminates Using Deep Learning and Pulsed Thermographic Inspection [J]. Neural Computing and Applications, 2023, 35(15): 11207-11221.
CAO Y P, DONG Y F, CAO Y L, et al. Two-Stream Convolutional Neural Network for Non-Destructive Subsurface Defect Detection via Similarity Comparison of Lock-in Thermography Signals [J]. NDT & E International, 2020, 112: 102246.
SAEED N, KING N, SAID Z, et al. Automatic Defects Detection in CFRP Thermograms, Using Convolutional Neural Networks and Transfer Learning [J]. Infrared Physics & Technology, 2019, 102: 103048.
DONG Y F, XIA C J, YANG J X, et al. Spatio-Temporal 3-D Residual Networks for Simultaneous Detection and Depth Estimation of CFRP Subsurface Defects in Lock-in Thermography [J]. IEEE Transactions on Industrial Informatics, 2022, 18(4): 2571-2581.
HU B Z, GAO B, WOO W L, et al. A Lightweight Spatial and Temporal Multi-Feature Fusion Network for Defect Detection [J]. IEEE Transactions on Image Processing, 2020, 30: 472-486.
DENG K L, LIU H C, CAO J, et al. Attention Mechanism Enhanced Spatiotemporal-Based Deep Learning Approach for Classifying Barely Visible Impact Damages in CFRP Materials [J]. Composite Structures, 2024, 337: 118030.
徐杰. 基于深度学习和损伤图像预测车用CFRP冲击能量的研究[D]. 哈尔滨: 哈尔滨工业大学, 2024.
HASEBE S, HIGUCHI R, YOKOZEKI T, et al. Internal Low-Velocity Impact Damage Prediction in CFRP Laminates Using Surface Profiles and Machine Learning [J]. Composites Part B: Engineering, 2022, 237: 109844.
端木正, 张晨, 肖航, 等. 一种基于多头自注意力深度学习的巨阻效应纳米线材料设计方法: 202411284915. X [P]. 2025-04-01.
杜禹樵, 马成坤, 王柏涛, 等. 基于碳纳米纸传感器和深度学习的碳纤维复合材料损伤监测[J]. 沈阳航空航天大学学报, 2024, 41(3): 43-52.
WANG Z F, ZHAO C C, YANG Z Y, et al. Multi-Scale Collaborative Prediction of Optimal Configuration for Carbon Fiber Woven Composites Based on Deep Learning Neural Networks [J]. Composite Structures, 2024, 339: 118165.
王敏, 文鹤鸣. 碳纳米管/碳纤维增强复合材料层合板低速冲击响应和破坏的数值模拟[J]. 爆炸与冲击, 2022, 42(3): 25-36.
HUANG J S, LIEW J X, LIEW K M. Data-Driven Machine Learning Approach for Exploring and Assessing Mechanical Properties of Carbon Nanotube-Reinforced Cement Composites [J]. Composite Structures, 2021, 267: 113917.
YANG C X, MENG K P, YANG L T, et al. Transfer Learning-Based Crashworthiness Prediction for the Composite Structure of a Subway Vehicle [J]. International Journal of Mechanical Sciences, 2023, 248: 108244.
LOUTAS T, OIKONOMOU A, REKATSINAS C. Bio-Inspired Discontinuous Composite Materials with a Machine Learning Optimized Architecture [J]. Composite Structures, 2025, 351: 118597.
钱奇伟, 张昕, 杨贞军, 等. 基于CT图像深度学习的三维编织C/C复合材料微观组分与缺陷智能识别[J]. 复合材料学报, 2024, 41(7): 3536-3543.
FONSECA J H, JANG W, HAN D, et al. Strength and Manufacturability Enhancement of a Composite Automotive Component via an Integrated Finite Element/Artificial Neural Network Multi-Objective Optimization Approach [J]. Composite Structures, 2024, 327: 117694.