Damage Precursor Identification in Composite Laminates using Data Driven Approach

Abstract

**Please note that the full text is embargoed** ABSTRACT: Composite materials are rapidly being used in commercial aviation and other day to day applications. The individual damage modes in composites are very well understood but it is the interaction of these local damage modes that leads to global failure. In the current research we intend to identify the damage precursors and the initiation of failure events in off axis unidirectional composite laminates loaded in quasi static uniaxial tension by measuring the dielectric response of the material by an in-situ technique using Broadband Dielectric Spectroscopy (BbDS). Using the variation of permittivity with strain, we are able to classify the stages of damage and predict the current material state. These data were then used to develop artificial intelligence models to identify the material state change and further use this data to predict the damage precursor stage and initiation of failure events. Different artificial intelligence models such as multi-layer perceptron, random forest regression and recurrent neural networks developed are discussed.