About this Abstract |
Meeting |
MS&T22: Materials Science & Technology
|
Symposium
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Additive Manufacturing Modeling, Simulation, and Machine Learning: Microstructure, Mechanics, and Process
|
Presentation Title |
Embracing Variability: Machine Learning-based Sequential Optimization of Additive Manufacturing Processes |
Author(s) |
Maher Alghalayini, Surya Kalidindi, Christiaan Paredis, Fadi Abdeljawad |
On-Site Speaker (Planned) |
Maher Alghalayini |
Abstract Scope |
The large variability of Additively Manufactured (AM) materials properties remains a challenge to these emerging techniques. Sequential design is a technique used to explore higher dimensional spaces such as the AM processing parameter space. We develop a sequential design method that implements machine learning to integrate the variability in the properties of AM materials. Our approach learns from previous experiments and adaptively proposes new sites that result in maximum expected information gain. The novelty of our approach lies in the use of Utility Theory to define the optimization criteria and the flexibility in identifying the number of new sites and AM samples. The proposed method is tested on synthetic data to showcase its performance. More specifically, a response surface with multiple peaks in terms of AM laser power and speed is used to benchmark our optimization approach. This study is expected to result in an efficient, variability-embracing adaptive optimization method. |