Abstract
With the booming prosperity of artificial intelligence (AI) technology, it triggers a paradigm shift in engineering fields including material science. The integration of AI and machine learning (ML) techniques in material science brings significant advancements in understanding and characterizing underlying physics. Due to the overall outstanding properties compared to conventional metallic materials, high-performance fiber reinforced polymer (FRP) composites have attracted great interest. This article aims to provide a comprehensive review of the state-of-the-art works of applying AI/ML methods in high-performance FRP composites, focusing on four critical stages throughout the product life cycle, i.e., design, manufacturing, testing, and monitoring. This present study covers the tasks of material development and selection, process modeling and optimization, material property prediction, and damage diagnosis and prognosis in the four stages, which are conducted with the aid of advanced AI/ML algorithms. An outlook for the incorporation of modern advanced AI/ML models into FRP composite research is provided by the identification of current challenges and potential future research directions.
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