| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
Aluminum Alloys: Development and Manufacturing
|
| Presentation Title |
The Use of Artificial Intelligence When Planning the Composition and Production of Wrought Aluminum Alloys with a Majority Share of Post-Consumed Scrap |
| Author(s) |
Varuzan M. Kevorkijan |
| On-Site Speaker (Planned) |
Varuzan M. Kevorkijan |
| Abstract Scope |
The demand for wrought aluminum alloys (WAAs) with a high proportion of post-consumed scrap (PCS) is steadily increasing due to their characteristic of sustainability. However, due to the differences in composition between primary aluminum and PCS, the design and production of these alloys dictate certain changes to the chemical composition and process parameters.
In this research we wanted to answer the question of whether, by increasing the permissible concentration limits of selected alloying elements and optimizing the process parameters, we could achieve the target set of WAA properties. We used artificial intelligence (IWM-IBM Watson Metallurgy) to design the alloy composition and the process parameters.
We experimented with selected alloys of the 6xxx and 2xxx series in such a way as to compare the predicted and actually achieved mechanical properties and formability. We found a very good match, which is a promising starting point for the further development of such alloys. |
| Proceedings Inclusion? |
Planned: Light Metals |
| Keywords |
Aluminum, Machine Learning, Process Technology |