| About this Abstract |
| Meeting |
2021 TMS Annual Meeting & Exhibition
|
| Symposium
|
Algorithm Development in Materials Science and Engineering
|
| Presentation Title |
Automatic Segmentation of Microstructures in Steel Using Machine Learning Methods |
| Author(s) |
Hoheok Kim, Junya Inoue, Tadashi Kasuya |
| On-Site Speaker (Planned) |
Hoheok Kim |
| Abstract Scope |
Microstructure of a material greatly influences its mechanical and chemical properties, so many endeavors have been made to characterize microstructure. Especially, microstructural characterization of steel materials is very challenging due to the existence of various constituent phases such as ferrite, pearlite, bainite, martensite, etc. Recently, researches using machine learning methods have actively conducted and shown good performances in classifying 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 microstructure segmentation without the need for labeled datasets. The new method, which is a combination of convolutional neural networks and a superpixel algorithm, is applied to various steel microstructure images and the results show that microstructures are well divided into their constituent phases. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Machine Learning, Iron and Steel, Computational Materials Science & Engineering |