Damage assessment in composite laminates using ANN-PSO-IGA and Cornwell indicator

A simple, yet powerful, new technique based on Artificial Neural Network (ANN) combined with Particle Swarm Optimization (PSO) for damage quantification in laminated composite plates using Cornwell indicator (CI) is proposed. The analysis is performed in two stages. In the first stage, IsoGeometric Analysis (IGA) is formulated for square laminated composite plates having three layers[0 degrees/90 degrees/0 degrees]. In the second stage, IGA model is coupled with PSO for damage quantification using an inverse problem approach and CI as an objective function to minimize the difference between calculated and measured values. This paper aims to assess the application of ANN-PSO for damage quantification in composite structures in order to achieve efficient computational time. IGA is used as modelling technique, CI is used as input data, whereas damage locations and severities are used as output data. The result indicates that high accuracy of damage quantification is achieved using ANN-PSO-IGACI. Furthermore, it is demonstrated that huge saving in computational time is achieved when using ANN-PSOIGA-CI compared with PSO-IGA-CI.