Abstract Scope |
Aluminum has been increasingly the sustainable material of choice for aerospace, automotive and beverage cans due to its high strength-to-weight ratio and infinite recyclability. Aluminum recycling is expected to surge in the coming years, and more end of automotive life aluminum scrap will be available in the market. It is of great importance to design aluminum alloys that can consume such scrap to minimize prime aluminum use, and simultaneously satisfy the performance requirements for desired applications, such as strength, formability, corrosion, etc. In the present work, we have developed a physics-informed machine-learning framework coupled to a scrap flow and blending model to accelerate sustainable aluminum alloy design workflows. We have used computational thermodynamics methods to estimate microstructural features pertaining to strength, formability and corrosion properties and integrated them into our materials informatics framework. Our model predictions for high recycle content alloy chemistries was then validated through lab trials. |