Pattern Recognition of Barely Visible Impact Damage in Carbon Composites Using Pulsed Thermography
Jia Mei Zhou, Weixiang Du, Lichao Yang, Kailun Deng, Sri Addepalli, Yifan Zhao
Abstract
This article proposes a novel framework to characterize the morphological pattern of barely visible impact damage using machine learning. Initially, a sequence of image processing methods is introduced to extract the damage contour, which is then described by a Fourier descriptor-based filter. The uncertainty associated with the damage contour under the same impact energy level is then investigated. A variety of geometric features of the contour are extracted to develop an artificial intelligence model, which effectively groups the tested 100 samples impacted by 5 different impact energy levels with an accuracy of 96%. Predictive polynomial models are finally established to link the impact energy to the three selected features. It is found that the major axis length of the damage has the best prediction performance, with an R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> value up to 0.97. Additionally, impact damage caused by low energy exhibits higher uncertainty than that of high energy, indicating lower predictability.