Multi objective optimization methods for damage assessment of composite laminates: A review

Abstract

The goal of this paper is to present a review of multi-objective optimization (MOO) techniques used in the damage assessment of composite laminates, with regard to damage identification, damage detection, damage quantification, and damage prediction models or approaches. Prior recognition of damage is crucial to ensure the performance and integrity of a structure of high structural responsibility. It is only then catastrophic structural failure or massive property loss and even in human casualties, in extreme situations, can be avoided or at least reduced. As such damage assessment is required, especially using advanced optimization and multi-objective optimization techniques, to prevent the failure of structural laminated composites. According to researchers, AI-based optimization algorithms are preferable to programmed inspections, conventional methods (like ultrasonic and magnetic damage or acoustic damage assessment methods), model or non-model (feature-based methods), and both in the sense that they are quick, universal, and less expensive methods for damage assessment. Unfortunately, there have only been a few or a small number of research investigations, particularly for damage prediction.