Classification of barely visible impact damage in composite laminates using deep learning and pulsed thermographic inspection

Abstract

With the increasingly comprehensive utilisation of Carbon Fibre-Reinforced Polymers (CFRP) in modern industry, defects detection and characterisation of these materials have become very important and draw significant research attention. During the past 10 years, Artificial Intelligence (AI) technologies have been attractive in this area due to their outstanding ability in complex data analysis tasks. Most current AI-based studies on damage characterisation in this field focus on damage segmentation and depth measurement, which also faces the bottleneck of lacking adequate experimental data for model training. This paper proposes a new framework to understand the relationship between Barely Visible Impact Damage features occurring in typical CFRP laminates to their corresponding controlled drop-test impact energy using a Deep Learning approach. A parametric study consisting of one hundred CFRP laminates with known material specification and identical geometric dimensions were subjected to drop-impact tests using five different impact energy levels. Then Pulsed Thermography was adopted to reveal the subsurface impact damage in these specimens and recorded damage patterns in temporal sequences of thermal images. A convolutional neural network was then employed to train models that aim to classify captured thermal photos into different groups according to their corresponding impact energy levels. Testing results of models trained from different time windows and lengths were evaluated, and the best classification accuracy of 99.75% was achieved. Finally, to increase the transparency of the proposed solution, a salience map is introduced to understand the learning source of the produced models.

Advances in Embedded Sensor Technologies for Impact Monitoring in Composite Structures

“…AI techniques, including image-, vibration-, and acoustic-based methods, are increasingly employed for detecting BVID in composites [84][85][86][87][88][89][90][91]. Research has effectively utilized Deep Learning (DL) and Machine Learning models to characterize impact damage, with applications ranging from analyzing wave propagation to infrared thermography.…”

A New Method to Predict Damage to Composite Structures Using Convolutional Neural Networks

“…Acoustic-based techniques utilize acoustic sensors to detect changes in acoustic emission signals generated by impact damage [22,[24][25][26]. Numerous studies have investigated the application of AI-based methods for detecting impact-induced damage in polymer composite materials [27][28][29][30][31][32][33]. Beyond impact damage detection, AI-based techniques have wider applications in damage classification, damage quantification, and predicting the remaining useful life of composite materials [34].…”