The Role of Artificial Neural Networks in Prediction of Mechanical and Tribological Properties of Composites-A Comprehensive Review

Abstract

The artificial neural network (ANN) approach motivated by the biological nervous system is an inspiring mathematical tool that simulates many complicated engineering applications. ANN learn from data and model real-life nonlinear and complex relationships; they can infer hidden relationships, thus making a generalized model and predicting unseen data. Unlike other prediction methods, ANN does not impose any restrictions on the variables and yields an accurate linear or nonlinear relationship between input and output parameters. Composite material properties depend on the composition, processing, and heat treatment relationships, and it is difficult to explain in terms of traditional methods. Implementing ANN in composites can significantly improve two major aspects: accuracy in modeling nonlinear relations and estimating the influence of many input parameters on material's performance. Moreover, many studies have shown that ANNs are highly accurate in modeling the mechanical behavior and tribological characteristics of composite materials as a function of various process parameters. The primary goal of this paper is to provide the state of art literature review on the application of ANNs in modeling and predicting composites' properties and a direction for future researchers in the field.