| About this Abstract |
| Meeting |
MS&T22: Materials Science & Technology
|
| Symposium
|
Materials Processing and Fundamental Understanding Based on Machine Learning and Data Informatics
|
| Presentation Title |
Microstructure Characterization and Reconstruction by Deep Learning Methodology |
| Author(s) |
Satoshi Noguchi, Junya Inoue |
| On-Site Speaker (Planned) |
Satoshi Noguchi |
| Abstract Scope |
For the establishment of process–structure–property linkage, we propose an image-based general methodology for the characterization and reconstruction of material microstructures using two deep learning networks, a vector quantized variational auto-encoder a vector quantized variational auto-encoder (VQVAE) and a pixel convolutional neural network (PixelCNN). VQVAE is used for the extraction of spatial arrangements of geometrical features corresponding to input micrographs, and PixelCNN is used for the determination of spatial correlation among the extracted geometrical features depending on process parameters and/or material properties. We applied our framework in the generation of low-carbon-steel microstructures from the given material processing. The results show good agreement with the experimental observation qualitatively in terms of the basic topology and quantitatively in terms of the volume fraction and the average grain size, demonstrating the potential of applying the proposed methodology to forward/inverse material design. |