| About this Abstract |
| Meeting |
13th International Conference on the Technology of Plasticity (ICTP 2021)
|
| Symposium
|
13th International Conference on the Technology of Plasticity (ICTP 2021)
|
| Presentation Title |
Artificial Neural Network Approach for Predicting Yield Strength with Microstructure and Texture in Magnesium Alloys |
| Author(s) |
Joung Sik Suh, Byeong-Chan Suh, Jun Ho Bae, Sang Eun Lee, Byoung-Gi Moon, N.S. Reddy |
| On-Site Speaker (Planned) |
Joung Sik Suh |
| Abstract Scope |
The present study investigates the relationship between microstructure, texture and tensile properties of magnesium (Mg) alloys. In Mg alloys, microstructural and texture factors have a decisive influence on mechanical properties due to their specific c/a ratios for hexagonal close-packed structure. It is well known that grain refinement significantly improves mechanical properties, especially tensile yield strength. On the other hand, the yield strength of Mg alloys is also considerably influenced by texture because of their insufficient deformation mechanisms. For this reason, there is a growing need to quantify the contribution of texture and microstructure development to the tensile properties of Mg alloys. This study aims to contribute to quantitative understanding of microstructure and texture effects on yield strength of Mg alloy based on artificial neural network. |
| Proceedings Inclusion? |
Definite: At-meeting proceedings |