Data encoding and reconstruction of thermal imaging maps of impact damaged composite Structures using feature space and neural networks

Abstract

A new approach to characterizing and predicting impact damage level in (Reaction Injection Molding) RIM structures is presented. The technique encodes thermal images maps and extracts features from presented thermal images. Complex Neural Networks structure is employed to reconstruct thermal imaging maps and predict the extent of damage an impact can cause. Neural network weigh elimination algorithm is used and proved effective in predicting areas of damage.

Pattern Recognition of Barely Visible Impact Damage in Carbon Composites Using Pulsed Thermography

“…Without manual intervention, defects caused by impact damage can be detected in the infrared image. Iskandarani et al [18] proposed a method for characterising and predicting the impact damage of edge structures. However, the literature review suggests that most of the existing AI-based methods in this area are for depth estimation [19] of flat-bottom hole samples or defects classification of artificial defects.…”