Identification of Lighting Strike Damage and Prediction of Residual Strength of Carbon Fiber-Reinforced Polymer Laminates Using a Machine Learning Approach.

ABSTRACT: Due to the complex and uncertain physics of lightning strike on carbon fiber-reinforced polymer (CFRP) laminates, conventional numerical simulation methods for assessing the residual strength of lightning-damaged CFRP laminates are highly time-consuming and far from pretty. To overcome these challenges, this study proposes a new prediction method for the residual strength of CFRP laminates based on machine learning. A diverse dataset is acquired and augmented from photographs of lightning strike damage areas, C-scan images, mechanical performance data, layup details, and lightning current parameters. Original lightning strike images, preprocessed with the Sobel operator for edge enhancement, are fed into a UNet neural network using four channels to detect damaged areas. These identified ar

SUBMITTER: Dong RZ 

PROVIDER: S-EPMC11768333 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature