| About this Abstract |
| Meeting |
6th World Congress on Integrated Computational Materials Engineering (ICME 2022)
|
| Symposium
|
6th World Congress on Integrated Computational Materials Engineering (ICME 2022)
|
| Presentation Title |
Data-driven Design, Discovery, and Development (D5)TM of Novel Corrosion-Resistant Coating Alloys for Galvanizing of New Advanced High Strength Steels (AHSS) |
| Author(s) |
Rohit Bardapurkar, Christopher K. H. Borg, John G Speer, Sridhar Seetharaman |
| On-Site Speaker (Planned) |
Rohit Bardapurkar |
| Abstract Scope |
Designing materials to achieve multiple performance requirements simultaneously is an often-time-consuming process due to the enormous number of possible experiments. Advancements in materials informatics seek to reduce the time to realize high-performing materials through data-driven modeling. Building upon these advancements, we apply a Data-Driven Design, Discovery, and Development (D5)TM approach to identify novel corrosion-resistant coating alloys with low liquidus temperature (TL) for galvanizing of new Advanced High Strength Steels (AHSS). Machine-learning (ML) algorithms trained on a database containing TL data (computed via CALPHAD modeling) and experimental corrosion data (collected from the literature) were employed to predict properties of novel coating alloys. A “Materials Selection Map” was developed to visualize the current status of design space and potential future opportunities related to the key performance criteria: corrosion-current (Icorr), corrosion-potential (Ecorr), and TL. |
| Proceedings Inclusion? |
Definite: Other |