|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
||Efficient Optimization of Variable and Uncertain Additive Manufacturing Processes Using Machine Learning
||Maher Alghalayini, Ali Khosravani, Surya Kalidindi, Chris Paredis, Fadi Abdeljawad
|On-Site Speaker (Planned)
Recent experimental studies have shown considerable variations in the properties of additively manufactured (AM) materials, even under the same processing conditions. Accounting for such variabilities in materials design is of critical importance. However, probing the AM process parameters is an experimentally intensive task due to the large AM design space. In this study, we develop a sequential design approach that learns from previously tested AM design points and surveys the AM process parameter design space in order to adaptively select the next AM process parameters that result in maximum information gain. The novelty lies in the use of Value-of-Information Theory to define the optimization criteria, and the flexibility in the number of design points and AM samples added in each iteration. Method performance is tested on synthetic data. This work is expected to result in a novel efficient sequential method that optimizes variable and uncertain processes.
||Additive Manufacturing, Machine Learning, Other