A review on recent applications of machine learning in mechanical properties of composites

Abstract Composites are undergoing extensive research and utilization due to their excellent mechanical properties, driven by human needs. Traditionally, the research methods in materials science predominantly rely on empirical theory or experimental trial and error approaches. However, the increased complexity of composite materials results in a greater intricacy in their mechanical behavior. Consequently, the utilization of traditional research methods may not achieve sufficient efficiency. Materials science is rapidly transitioning into a data‐driven era, with machine learning (ML) emerging as a potent tool to expedite materials development and enhance properties prediction. Significant advancements have been achieved in the application of ML to the study of composite mechanics. In this review article, we elucidate various ML methods employed in the construction of constitutive models for isotropic and anisotropic composites, and delve into the research on construction ML models that leverage input data derived from composite processes, structures, and environmental conditions to predict material mechanical properties. Additionally, we summarize recent noteworthy ML applications in composite design and optimization. Finally, possible prospective viewpoints are proposed for future development, with the aim of providing essential scientific guidance for advancing material science and technology through ML. Highlights Machine learning can address complexity in constitutive model of the anisotropic composites. Machine learning predicts mechanical properties of composites well by process and structure. Machine learning enhances efficiency in inverse design to optimize composites. Limitations, challenges, development trends of ML in composites.