Dataset and code for machine learning prediction of effective thermal conductivity of a hexagonal metal-matrix composite

This record contains the dataset and source code used in the study on machine learning prediction of the effective thermal conductivity of a metal-matrix composite with hexagonal fiber packing.

The dataset was generated using finite element modeling in ANSYS APDL. It includes 1000 numerical experiments with input parameters describing the relative fiber radius and thermal conductivities of the fiber and matrix. The target variables are the effective thermal conductivity coefficients Ky and Kz.

The record also includes the ANSYS APDL script, Python scripts for training and evaluating a multilayer perceptron model, GroupKFold validation metrics, and a neural network architecture figure.

本数据集归档包含了针对六边形纤维排布金属基复合材料有效导热系数开展机器学习预测研究所需的数据集与源代码。
该数据集通过ANSYS APDL有限元建模生成，涵盖1000组数值实验数据，输入参数包含纤维相对半径、纤维与基体的导热系数，目标变量为有效导热系数Ky与Kz。
本归档还包含ANSYS APDL脚本、用于训练与评估多层感知机模型的Python脚本、GroupKFold验证指标，以及神经网络架构示意图。