A Generative Machine Learning Model for Material Microstructure 3D
  Reconstruction and Performance Evaluation
Yilin Zheng Zhigong Song
The reconstruction of 3D microstructures from 2D slices is considered to hold
significant value in predicting the spatial structure and physical properties
of materials.The dimensional extension from 2D to 3D is viewed as a highly
challenging inverse problem from the current technological
perspective.Recently,methods based on generative adversarial networks have
garnered widespread attention.However,they are still hampered by numerous
limitations,including oversimplified models,a requirement for a substantial
number of training samples,and difficulties in achieving model convergence
during training.In light of this,a novel generative model that integrates the
multiscale properties of U-net with and the generative capabilities of GAN has
been proposed.Based on this,the innovative construction of a multi-scale
channel aggregation module,a multi-scale hierarchical feature aggregation
module and a convolutional block attention mechanism can better capture the
properties of the material microstructure and extract the image information.The
model's accuracy is further improved by combining the image regularization loss
with the Wasserstein distance loss.In addition,this study utilizes the
anisotropy index to accurately distinguish the nature of the image,which can
clearly determine the isotropy and anisotropy of the image.It is also the first
time that the generation quality of material samples from different domains is
evaluated and the performance of the model itself is compared.The experimental
results demonstrate that the present model not only shows a very high
similarity between the generated 3D structures and real samples but is also
highly consistent with real data in terms of statistical data analysis.