| About this Abstract |
| Meeting |
2024 TMS Annual Meeting & Exhibition
|
| Symposium
|
Printed Electronics and Additive Manufacturing: Advanced Functional Materials, Processing Concepts, and Emerging Applications
|
| Presentation Title |
Machine Learning-enabled Feature Classification of Evaporation-driven Multi-scale 3D Printing |
| Author(s) |
Samannoy Ghosh, Marshall V Johnson, James Hardin, John Daniel Berrigan, Surya R Kalidindi, Yong Lin Kong |
| On-Site Speaker (Planned) |
Samannoy Ghosh |
| Abstract Scope |
The freeform generation of active electronics through additive manufacturing (AM) techniques can impart advanced functional capabilities to an otherwise passive construct. Indeed, existing AM approaches to 3D print active electronics with the highly complex multiphase evaporative-driven assembly process remain challenging. Here, we demonstrate a synergistic integration of a microfluidics-driven multi-scale 3D printer with a machine learning algorithm that can precisely tune colloidal ink composition to explore multi-dimensional parameter space and classify complex internal features. The integration of the printer with an image-processing algorithm and a support vector machine-guided classification model enables automated, in-situ pattern classification. We envision that such integration will provide valuable insights into understanding the complex evaporative-driven assembly process and ultimately enable the print platform to adapt to unexpected perturbations. |
| Proceedings Inclusion? |
Planned: |