About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Symposium
|
First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Presentation Title |
Machine Learning Enabled Model to Predict Mechanical Properties of Refractory Alloys |
Author(s) |
Trupti Mohanty, K. S. Ravi Chandran, Taylor D Sparks |
On-Site Speaker (Planned) |
Trupti Mohanty |
Abstract Scope |
Refractory metal alloys with targeted mechanical properties find numerous high-temperature applications. However, experimental determination of mechanical properties particularly at high temperatures is quite challenging. In view of this, the present study is focused on harnessing the power of machine learning with an objective to predict yield strength (YS) and ultimate tensile strength (UTS) of novel refractory alloys at varying temperatures. Since only a limited number of experimental YS and UTS data is available therefore it is attempted to improve the predictive accuracy of the machine learning model by augmenting the number of training datasets by incorporating YS and UTS of multi-principal element alloys. In this work, the composition-based features are utilized for the development of machine learning models. The intended use of the developed model is to derive novel alloy composition with desired mechanical properties for high-temperature applications. |
Proceedings Inclusion? |
Undecided |