| About this Abstract |
| Meeting |
MS&T22: Materials Science & Technology
|
| Symposium
|
Materials Processing and Fundamental Understanding Based on Machine Learning and Data Informatics
|
| Presentation Title |
Comparison of Data Driven and Physics-informed Machine Learning Models for Temperature Prediction of Shear Assisted Processing and Extrusion |
| Author(s) |
Ethan King, Colby Wight, WoongJo Choi, Zhao Chen, Keerti Kappagantula, Shenyang Hu, Yulan Li, Tegan Emerson, Sarah Akers, Henry Kvinge, Eric Machorro, Jenna Pope, Erin Barker, Eric Smith |
| On-Site Speaker (Planned) |
Ethan King |
| Abstract Scope |
Shear assisted processing and extrusion (ShAPE™) uses friction to plasticize feedstock with a rotating die, which is then extruded into a consolidated tube. The microstructure and properties of the extruded product are dependent on the extrusion temperature, controlled by the tool rotation and traverse rate. The effect of these process parameters is in turn modulated by the temperature of the deforming material. Thus control of process temperature depends on complex thermo-mechanical feedbacks that are challenging to model. In this talk, we discuss two approaches developed to model the relationship between ShAPE process parameters and temperatures, namely a data-driven approach called DeepTemp and a physics informed ML model. We compare the accuracy and data requirements between the data driven and physics-informed ML models and demonstrate that ML models can closely predict process temperatures during ShAPE, which can accelerate and improve ShAPE process optimization. |