How machine learning can help the design and analysis of composite materials and structures?

TL;DR

This paper reviews how artificial neural networks are applied to the design and analysis of composite materials, highlighting current frameworks, benefits, challenges, and future research directions in data-driven engineering.

Contribution

It provides a comprehensive review of ANN applications in nonlinear modeling, multiscale surrogate modeling, and optimization of composite materials, identifying challenges and opportunities.

Findings

ANN models are widely used but face practical acceptance issues.

Emerging machine learning techniques offer new opportunities and challenges.

The review suggests future research directions for robust data-driven design.

Abstract

Machine learning models are increasingly used in many engineering fields thanks to the widespread digital data, growing computing power, and advanced algorithms. Artificial neural networks (ANN) is the most popular machine learning model in recent years. Although many ANN models have been used in the design and analysis of composite materials and structures, there are still some unsolved issues that hinder the acceptance of ANN models in the practical design and analysis of composite materials and structures. Moreover, the emerging machine learning techniques are posting new opportunities and challenges in the data-based design paradigm. This paper aims to give a state-of-the-art literature review of ANN models in the nonlinear constitutive modeling, multiscale surrogate modeling, and design optimization of composite materials and structures. This review has been designed to focus on the discussion of the general frameworks and benefits of ANN models to the above problems. Moreover, challenges and opportunities in each key problem are identified and discussed. This paper is expected to open the discussion of future research scope and new directions to enable efficient, robust, and accurate data-driven design and analysis of composite materials and structures.