This study aims to integrate micromechanics-based analytical models with machine learning (ML) models to predict the effective properties of two-phase composites. A novel approach grounded in Maxwell’s effective field method (EFM) is proposed to address the accuracy limitations inherent in micromechanics-based models while minimizing the amount of data needed to fit ML models. Notably, this new approach requires only two macroscale data points to predict the effective properties. The approach is introduced for inhomogeneities of arbitrary shape, orientation, and properties and is applicable to effective thermal, electrical, elastic, and other properties. Two case studies focusing on the elasticity problem are presented to illustrate the applicability and accuracy of the new approach; one involving a particulate composite of copper reinforced with diamond particles, and the other a unidirectional composite of 3D-printed nylon reinforced with Kevlar fibers. The results of these case studies are compared with finite element models and demonstrate an excellent agreement.

中文翻译：

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通过基于微观力学的机器学习预测基复合材料的有效性能


本研究旨在将基于微观力学的分析模型与机器学习 （ML） 模型相结合，以预测两相复合材料的有效特性。提出了一种基于 Maxwell 有效场法 （EFM） 的新方法，以解决基于微力学的模型固有的准确性限制，同时最大限度地减少拟合 ML 模型所需的数据量。值得注意的是，这种新方法只需要两个宏观数据点来预测有效特性。该方法针对任意形状、方向和性质的不均匀性引入，适用于有效的热、电、弹性和其他性质。提出了两个专注于弹性问题的案例研究，以说明新方法的适用性和准确性;一个涉及用金刚石颗粒增强的铜颗粒复合材料，另一个是用凯夫拉纤维增强的 3D 打印尼龙的单向复合材料。将这些案例研究的结果与有限元模型进行了比较，并证明了极好的一致性。