Classification of barely visible impact damage in composite laminates using deep learning and pulsed thermographic inspection
نویسندگان
چکیده
Abstract With the increasingly comprehensive utilisation of Carbon Fibre-Reinforced Polymers (CFRP) in modern industry, defects detection and characterisation these materials have become very important draw significant research attention. During past 10 years, Artificial Intelligence (AI) technologies been attractive this area due to their outstanding ability complex data analysis tasks. Most current AI-based studies on damage field focus segmentation depth measurement, which also faces bottleneck lacking adequate experimental for model training. This paper proposes a new framework understand relationship between Barely Visible Impact Damage features occurring typical CFRP laminates corresponding controlled drop-test impact energy using Deep Learning approach. A parametric study consisting one hundred with known material specification identical geometric dimensions were subjected drop-impact tests five different levels. Then Pulsed Thermography was adopted reveal subsurface specimens recorded patterns temporal sequences thermal images. convolutional neural network then employed train models that aim classify captured photos into groups according Testing results trained from time windows lengths evaluated, best classification accuracy 99.75% achieved. Finally, increase transparency proposed solution, salience map is introduced learning source produced models.
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ژورنال
عنوان ژورنال: Neural Computing and Applications
سال: 2023
ISSN: ['0941-0643', '1433-3058']
DOI: https://doi.org/10.1007/s00521-023-08293-7