A review of artificial neural networks in the constitutive modeling of composite materials. (1st November 2021)

• Record Type: Journal Article
• Authors: Liu, Xin; Tian, Su; Tao, Fei; Yu, Wenbin
• Is Part Of: Composites. Number 224(2021)
• Publication Date: 2021-11-01
• Subjects: Constitutive modeling -- Composite materials -- Multiscale modeling -- Neural networks; Composite materials -- Periodicals; Materials science -- Periodicals; Composite materials; Periodicals; Electronic journals; 620.118
• DOI: 10.1016/j.compositesb.2021.109152
• Languages: English
• ISSNs: 1359-8368

Abstract

Abstract: Machine learning models are increasingly used in many engineering fields thanks to the widespread digital data, growing computing power, and advanced algorithms. The most popular machine learning model in recent years is artificial neural networks (ANN). Although many ANN models are used in the constitutive modeling of composite materials, 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 posing 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 constitutive modeling of composite materials, focusing on discovering unknown constitutive laws and accelerating multiscale modeling. This review focuses on the general frameworks, benefits, and challenges and opportunities of ANN models to the constitutive modeling of composite materials. Moreover, potential applications of ANN-based constitutive models in composite materials and structures are also discussed. This review is intended to initiate discussion of future research scope and new directions to enable efficient, robust, and accurate data-driven design and analysis of composite materials and structures. Highlights: Applications of ANN models in composites constitutive modeling are reviewed. Challenges of discovering constitutive laws are discussed. Challenges of accelerating multiscale modeling are summarized. Potential solutions to the challenges of ANN constitutive models are proposed. Future opportunities for ANN-based constitutive models are discussed.