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  • Open Access

    REVIEW

    A Comprehensive Review on Bridging the Research Gap in AI-Driven Material Simulation for FRP Composites

    Alin Diniță1, Cosmina-Mihaela Rosca2, Maria Tănase1,*, Adrian Stancu3

    CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 147-199, 2025, DOI:10.32604/cmes.2025.066276 - 31 July 2025

    Abstract Fiber-reinforced polymer (FRP) composites are renowned for their high mechanical strength, durability, and lightweight properties, making them integral to civil engineering, aerospace, and automotive manufacturing. Traditionally, the simulation and optimization of FRP materials have relied on finite element (FE) methods, which, while effective, often fall short in capturing the intricate behaviors of these composites under diverse conditions. Concrete examples in this regard involve modeling interfacial cracks, delaminations, or environmental effects that involve nonlinear phenomena. These degradation mechanisms exceed the capacity of classical FE models, as they are not detailed to the required level of detail.… More > Graphic Abstract

    A Comprehensive Review on Bridging the Research Gap in AI-Driven Material Simulation for FRP Composites

  • Open Access

    ARTICLE

    Quasi-Phase Equilibrium Prediction of Multi-Element Alloys Based on Machine Learning and Deep Learning

    Changsheng Zhu1,2,*, Borui Zhao1, Naranjo Villota Jose Luis1, Zihao Gao1, Li Feng3

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 49-64, 2023, DOI:10.32604/cmc.2023.036729 - 08 June 2023

    Abstract In this study, a phase field model is established to simulate the microstructure formation during the solidification of dendrites by taking the Al-Cu-Mg ternary alloy as an example, and machine learning and deep learning methods are combined with the Kim-Kim-Suzuki (KKS) phase field model to predict the quasi-phase equilibrium. The paper first uses the least squares method to obtain the required data and then applies eight machine learning methods and five deep learning methods to train the quasi-phase equilibrium prediction models. After obtaining different models, this paper compares the reliability of the established models by… More >

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