| About this Abstract |
| Meeting |
MS&T22: Materials Science & Technology
|
| Symposium
|
Ceramics and Glasses Modeling by Simulations and Machine Learning
|
| Presentation Title |
Data Driven Design and Enhancement of Machinable Glass Ceramics
|
| Author(s) |
Prachi Garg, Scott Broderick, Baishakhi Mazumder |
| On-Site Speaker (Planned) |
Prachi Garg |
| Abstract Scope |
Machinable glass ceramics (MGC) are composed of a borosilicate glassy matrix with a crystalline phase, and with excellent machinability and high temperature working conditions. Machinability arises due to the crack propagation in the individual crystals, making crystallization a critical point of consideration. However, a significant challenge is that there are limited hardness values available along with a nearly infinite chemical search space. To address this challenge, we developed an ensemble data mining approach to model the hardness of over 250 new MGC compositions with high accuracy and robustness. Thus, our model guides future experiments based on the precious limited data available. Designing an experiment is challenging due to limited hardness values and infinite chemical search base. Our model was instead trained on the extracted data from different sources to guide future experiments. Coupling these results with microstructural studies provides a unique feedback on the fabrication of state-of-the-art machinable glass ceramics. |