About this Abstract |
Meeting |
5th International Congress on 3D Materials Science (3DMS 2021)
|
Symposium
|
5th International Congress on 3D Materials Science (3DMS 2021)
|
Presentation Title |
Automatic Segmentation of 3D Microstructures of Steel Using Machine Learning Methods |
Author(s) |
Hoheok Kim, Yuuki Arisato, Junya Inoue |
On-Site Speaker (Planned) |
Hoheok Kim |
Abstract Scope |
Microstructure greatly influences mechanical and chemical properties, so many endeavors have been made to characterize it. Especially, 3D microstructural characterization of steel is very challenging due to the existence of various phases as well as the high cost for delicate 3D observations. Recently, researches using machine learning methods have shown good performances in classifying 3D microstructures. However, those approaches are generally based on supervised learning algorithms that require preparation of labeled datasets, which is not only time-consuming but also difficult even for experts. In this study, we propose an unsupervised algorithm that performs 3D microstructure segmentation without the need for labeled datasets. The new method, which is a combination of feature extraction with filters for texture analysis and a clustering algorithm, is applied to a 3D image obtained with a serial sectioning technique and optical microscope. The segmentation result shows that 3D microstructures are well divided into constituent phases. |
Proceedings Inclusion? |
Definite: Other |