Applications of Deep Learning for Composites Materials

Abstract

Composite materials are highly sought-after in various industries for their extraordinary properties. These materials are created by combining two or more different substances, resulting in a novel material with improved characteristics. However, composites materials and laminates display intricate structure patterns, which can be considered as unstructured data. Currently, deep learning is experiencing rapid advancements in the field of composite materials, providing advancements in prediction enhancement, material characterization, structural health monitoring, process optimization, and more. It facilitates the analysis of such complex data patterns and effectively automates the identification of features. This chapter begins with a high-level overview of deep learning methods. It then explores recent developments in the use of deep learning and machine learning for composite materials in depth. To conclude this chapter review, we discuss revolutionary approaches to designing and optimizing composites for the next generation of materials with unprecedented properties, as well as the limitations, challenges, and potential growth areas for deep learning methods in the context of composite materials. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.