Abstract Scope |
Duplex structural stainless steels (DSS) are used in chemical, petrochemical, marine, power generation, and nuclear industries due to their unique properties. The DSS nanophases (ferrite and austenite) are made of numerous topological properties, such as perimeter length, area, orientation, and centroid. The objective of this study is to characterize each nanophase units based on its topological properties. The nanophase units can be represented as Representative Volume Elements (RVE) and are extracted from the pixel image by computing approaching the contour through marching square algorithm. Topological properties then are calculated. A dataset linking each nanophase unit shape with topological properties are generated using clustering algorithms The results of RVE obtained from clustering techniques to demonstrate the deep learning approach in generating variety of RVEs. Currently, more results are being obtained in relating RVEs and their mechanical behavior and will be presented at the conference. |