Acoustic emission based deep learning technique to predict adhesive bond strength of laser processed CFRP composites

Abstract

The high degree of inhomogeneity in material and intricacies created by machining of carbon fiber reinforced plastic (CFRP) composites hinder the accurate prediction of residual strength of the adhesive bond joint using analytical models. Recently, artificial intelligence techniques are effectively utilized as an alternative method for predicting the results of complex phenomena. In this paper, attempts were made to predict the bond strength of laser surface treated and adhesively bonded CFRP composite specimens using the artificial neural network (ANN) from the acoustic emission (AE) parameter recorded during the shear test. Twelve adhesively bonded specimens whose surfaces were pre-processed with 3W Nd:YAG laser at different processing parameters. ANN was trained using segregated AE data according to the failure mechanism and the percentage of failure load (5 to 100%). Predicted values were compared with experimental values and the results were analysed for the suitability of ANN with AE in the application.