UNSUPERVISED MACHINE LEARNING ALGORITHMS FOR ANALYSIS OF LOW VELOCITY IMPACT DAMAGE IN COMPOSITE STRUCTURES FROM CT IMAGE DATA

In this work, novel unsupervised machine learning (ML) algorithms for automatic image segmentation for the analysis of the micro-CT data for impact damage assessment in the composite materials have been developed. The algorithms are based on the statistical distances including the Kullback-Leibler divergence, the Helling distance, and the Renyi divergence. The developed algorithms have been applied to the analysis of low velocity impact damage in carbon fiber reinforced polymer (CFRP) composites. The grayscale images from the CT scans of the impacted CFRP specimens have been analyzed to identify and isolate impact damage and optimal statistics-based damage thresholds have been found. The results show that the developed algorithms enable not only an automatic image segmentation, but also deliver statistics-based rigorous damage thresholds.