| About this Abstract |
| Meeting |
Materials Science & Technology 2020
|
| Symposium
|
Additive Manufacturing Modeling and Simulation: AM Materials, Processes, and Mechanics
|
| Presentation Title |
Feature Engineering for Surrogate Models of Consolidation Degree in Additive Manufacturing |
| Author(s) |
Mriganka Roy, Olga Wodo |
| On-Site Speaker (Planned) |
Olga Wodo |
| Abstract Scope |
The limited in-situ control, process optimization, and quality assurance are hindering AM’s widespread acceptance. A fast and accurate process evaluation could alleviate these challenges. In recent years there has been an effort to achieve this goal by developing surrogate models (SM). In this work, we utilize the knowledge of the underlying process to engineer features that efficiently parameterizes the geometry and printing pattern. We quantify the localized behavior of the process by defining a heat influence zone that limits the search area for the features and the size of the feature set. The engineered features enabled the training of the SM which was 1000 times faster than the numerical model and highly accurate (90% accuracy). |