About this Abstract |
Meeting |
MS&T22: Materials Science & Technology
|
Symposium
|
AI for Big Data Problems in Advanced Imaging, Materials Modeling and Automated Synthesis
|
Presentation Title |
A Feasibility Study of Machine Learning-assisted Alloy Design Using Wrought Aluminum Alloys as An Example |
Author(s) |
Yasaman Jamalipour Soofi, Md Asad Rahman, Yijia Gu, Jinling Liu |
On-Site Speaker (Planned) |
Yasaman Jamalipour Soofi |
Abstract Scope |
Machine learning (ML) typically requires large datasets for reliable predictions, which may not be realistic for most commercial alloy systems. Also, the alloy development requires a full set of balanced properties, most of which have not been studied by ML yet. In this study, we focused on the practicality and reliability of ML in alloy design using commercial wrought aluminum alloys as an example. The dataset used in this study contains 236 entries and 15 alloy properties. We systematically evaluated various ML models with a focus on the bias-variance trade-off. We further explored the possibility of engineering the feature space to improve the ML models. Lastly, our feature importance analysis suggested the soundness of the developed models and provided new insights into the underlying processing-structure-property relations. This study demonstrated that it is feasible to use machine learning and data mining techniques to assist the alloy design using realistic small datasets. |