Artificial intelligence techniques for fault assessment in laminated composite structure: a review

¹ Department of Production Engineering, Veer Surendra Sai University of Technology, Burla, Odisha, India
² Department of Mechanical Engineering, Gandhi Institute for Technological Advancement, Bhubaneswar, Odisha, India
³ AIML Architect, Bengaluru, Karnataka, India

Abstract

There is a continuous quest in the research community for superior and more accurate methodology for fault diagnosis and condition monitoring of diverse composite structure. This is because, these structures suffer from various nonlinear mode of failures while in service those are recognised as delamination, voids, matrix crack etc. Early detection of failures is what the most research mainly aims at. In this regard, the implementation of Artificial Intelligence (AI) techniques has been proved to be a versatile method for damage assessment. The collective inevitable use of composite materials in various high-performance engineering industries requires preliminary testing (detection, location, and quantification) for damage to these materials in order to improve their integrity and order. The present paper aims to bring out a concise review on various methodologies employed for damage/fault detection in composite materials with a special emphasis on supervised and unsupervised machine learning techniques. The major observations are outlined with an objective to put forward a broad perspective of the state of art related to laminated composite structural heath monitoring.