About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Symposium
|
First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Presentation Title |
Machine Learning Guided Design of Aluminium Alloys |
Author(s) |
Ninad Bhat, Amanda Barnard, Nick Birbilis |
On-Site Speaker (Planned) |
Ninad Bhat |
Abstract Scope |
Aluminium (Al) alloys continue to remain critical in the aerospace and transportation industries due to their high specific strength and low density. The discovery of new Al-alloys is still primarily guided through the empirical and "trial and error" process. This approach creates a bottleneck in alloy development, as it is time-consuming and expensive. In the present work, machine learning techniques have been used to screen and predict new Al-alloys targeting Yield Strength, Tensile Strength and Elongation. A dataset of aluminium alloys, including the processing condition and alloy concentration, was curated. A Random Forest Regressor was used to predict mechanical properties and analyse the features contributing to the mechanical properties. Further, these regression models can be combined with Evolutionary Algorithms to predict new aluminium alloys. |
Proceedings Inclusion? |
Undecided |