| About this Abstract |
| Meeting |
2020 TMS Annual Meeting & Exhibition
|
| Symposium
|
ICME Gap Analysis in Materials Informatics: Databases, Machine Learning, and Data-Driven Design
|
| Presentation Title |
Training Data-driven Machine Learning Models Using Physics Simulations: Predicting Local Thermal Histories in Additive Manufactured Components |
| Author(s) |
Michael Groeber, Karthik Giriprasad |
| On-Site Speaker (Planned) |
Michael Groeber |
| Abstract Scope |
The processing parameter space in additive manufacturing (AM) is prohibitively large – driving the need for ICME processing tools. In powder bed fusion processes (e.g. LPBF or EBM) printing parameters such as beam power, focus, raster speed, and raster path affect thermal and mechanical stress states. Mapping influences of these parameters is daunting, preventing design of customized microstructures theoretically achievable via AM.
Energy input time series, listing relative distances, timings and intensities of energy input, represent the local scan path at locations in AM components. We investigate processing these time series through a 1D, deep convolutional neural network (CNN) to predict local thermal histories. A fast-acting analytical model predicts thermal histories for training the CNN. We aim to show even efficient thermal models can be replaced by machine learning models, derived from novel representations of local energy input. These models provide additional promise when trained against advanced physics. |
| Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |