About this Abstract |
Meeting |
2025 TMS Annual Meeting & Exhibition
|
Symposium
|
Artificial Intelligence Applications in Integrated Computational Materials Engineering
|
Presentation Title |
Design of High-Strength Steel Using Machine Learning Techniques |
Author(s) |
Rajani Jaiswal, Shiv Brat Singh, Saurabh Kundu, Itishree Mohanty |
On-Site Speaker (Planned) |
Rajani Jaiswal |
Abstract Scope |
Eutectoid and near-eutectoid steels belong to a special class of high-strength, wear-resistant steels used for several applications. The composite structure of soft ferrite and hard cementite phases gives a good combination of strength and ductility. The mechanical properties of pearlitic steel depend on interlamellar spacing of pearlite. In the present study, an ANN model is created to predict the interlamellar spacing as a function of Gibbs free energy of pearlite transformation and the isothermal transformation temperature. Another model has been developed to correlate the Gibbs free energy of isothermal pearlite transformation with the alloy composition and the transformation temperature. The result shows the impact of several parameters that are beneficial in the design of high-strength steels. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, |