| About this Abstract |
| Meeting |
Materials Science & Technology 2020
|
| Symposium
|
Materials Design through AI Composition and Process Optimization
|
| Presentation Title |
Accelerating the Discovery of New DP-steel Using Machine Learning-based Multiscale Materials Simulations |
| Author(s) |
Abdallah Chehade, Tarek Belgasam, Georges Y. Ayoub |
| On-Site Speaker (Planned) |
Georges Y. Ayoub |
| Abstract Scope |
While it is of high interest for the transportation industry to design and discover different grades of DP steels exhibiting desirable mechanical properties, this requires exploring a large number of DP steel microstructure combinations. Expensive trial-and-error-based experimentations and multiscale materials simulations are two conventional approaches that have been widely adopted in the field of materials design and discovery. A Gaussian process is developed to accelerate the discovery of the mechanical properties of different DP steels by evolving the microstructure parameters using a limited number of numerical simulations (using a multiscale materials model). The proposed Gaussian process not only accelerates the prediction of the desired mechanical properties of millions of multiscale materials simulations but also offers uncertainty quantification around its predictions. The proposed framework combining multiscale simulations and the Gaussian process is used to discover the microstructural design of DP steel with maximum tensile toughness. |