Neural network architecture and training data for prediction of porous material mechanical properties based on their microstructure

This repository contains data used for predicting the mechanical response of porous natural materials based on their microstructural features. CT scans of porous natural materials were segmented using ImageJ (and the custom ImageJ script SegmentJ: https://github.com/JPELO/SegmentJ), and microstructural geometric features such as volume density, surface area density, and asymmetry were extracted from the scans. Mesh .STL files were also created using ImageJ for each sub-scan. The open source parallelizable finite element software MOOSE was used to simulate uniaxial monotonic compression testing and produce a stress-strain response for each sub-scan. Additionally, the mesh files were used to 3D print physical microstructures and were tested experimentally in uniaxial monotonic compression at a rate of 5mm/min. The material used to print the structures was Formlabs Grey Pro with a Formlabs Form2 printer. MATLAB was then used to create and train a neural network capable of predicting the mechanical stress-strain curve of a porous microstructure based on its geometric features.

本数据集仓库包含用于基于多孔天然材料微观结构特征预测其力学响应的相关数据。针对多孔天然材料的计算机断层扫描（CT）图像，采用ImageJ软件及自定义ImageJ脚本SegmentJ（https://github.com/JPELO/SegmentJ）完成图像分割，并从扫描图像中提取体积密度、表面积密度、不对称性等微观结构几何特征。同时，通过ImageJ为每个子扫描图像生成网格.STL文件。使用开源可并行化有限元软件MOOSE，对每个子扫描图像开展单轴单调压缩仿真，得到对应试样的应力-应变响应。此外，利用上述网格文件进行三维打印，制备物理微观结构试样，并以5mm/min的加载速率开展单轴单调压缩实验。试样打印所用材料为Formlabs Grey Pro树脂，打印设备为Formlabs Form2打印机。随后使用MATLAB构建并训练了一款神经网络，可基于多孔微观结构的几何特征预测其力学应力-应变曲线。