Assessment of tensile behaviour of plain weave fabric CFRP composites using acoustic emission technique and deep learning

Abstract The applications of Carbon Fibre Reinforced Plastic (CFRP) composites in aerospace have increased exponentially the last decade. CFRP are now replacing conventional materials used high temperature applications. Different types and strategies reinforcements these to improve their applicability One reinforcement that has been frequently recent years is plain weave fabric configuration reinforcing fibres polymer matrix. A comprehensive damage assessment essential evaluate characteristics at elevated temperatures. Acoustic Emission (AE) signals generated during evolution stages for studying when tested temperatures close glass transition state. new information-theoretic parameter, Lempel-Ziv (LZ) complexity deep learning neural network understanding stages. Furthermore, validating utilization LZ as a potential AE parameter classification results promising with strategy using exhibits an accuracy 85.1% which validated through network.