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

Sidharth Patro,Vikram Kishore Murty,Trupti Ranjan Mahapatra,Sushmita Dash,S Tummala,S Kosaraju,P Bobba,S Singh

doi:10.1051/e3sconf/202130901083

Sidharth Patro, Vikram Kishore Murty + Show 6 more

Open Access

https://doi.org/10.1051/e3sconf/202130901083

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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.

Highlights

Composite materials are types of substances produced by combining two or more different chemical or physical ingredients in a visible level, without chemical reactions or melting, for instance as the final materials carrying the qualities of their properties
Structural health monitoring (SHM) is divided into two ways: (a) passive SHM which considers the operational parameters of the system and draws the inference from it accessing the structural health of the system
We reviewed the skills of planning, integrating, and measuring Artificial neural network (ANN), support vector support (SVM), auto-regress models (AR models), K methods, and neighbours near K in a variety of critical aspects of damage in a controlled and uncontrolled manner

Summary

Introduction

Composite materials are types of substances produced by combining two or more different chemical or physical ingredients in a visible level, without chemical reactions or melting, for instance as the final materials carrying the qualities of their properties. The commencement and development of damage to the composite structures is in accordance with a complicated pattern and cannot be inspected by vision testing and generalised ways This is due to the complex and hidden nature of the damage for example matrix crack, delamination. The effective SHM is responsible for accurately measuring the health status of a building by trying to determine the presence and magnitude of structural damage. 5. Is the damage low the remaining useful life (RUL) By identifying the type of injury and the extent we need pre-ethical views of the structure, with the predictable methods of failures of future assessment information and integration, which are often done using analytical models. Unlike supervised reading, unregulated learning algorithms do not require output or visual label

Fault diagnosis techniques using Artificial Intelligence

Neural Network

Additional Techniques

Fuzzy inference technique

Hybrid technique

Conclusion