About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Printed Electronics and Additive Manufacturing of Advanced Functional Materials and Devices—From Processing Concepts to Applications
|
| Presentation Title |
Optimizing Inkless Printed Electronics with Machine Learning Models |
| Author(s) |
Colton Bevel, Masoud Mahjouri-Samani, Sumner Harris, Adib Taba |
| On-Site Speaker (Planned) |
Colton Bevel |
| Abstract Scope |
We present a machine learning approach to optimize inkless printed electronics so that users may enter a target resistivity and receive optimized printing parameters to achieve it. The process is based on a dry additive nanomanufacturing (Dry-ANM) technique that produces pure nanoparticles via pulsed laser ablation. These particles are directed via argon through a nozzle and sintered in-situ onto a substrate. For this study, pure silver and copper were printed on a Polyamide substrate. Design of experiment algorithms were developed and optimized for incorporating new materials in the machine learning algorithm. Multiple machine learning models were trained to obtain relationships between process parameters and final resistivity. A search strategy study was then conducted ML algorithm to predict printing conditions with user-specified parameter ranges and target resistivity. Ultimately, it enables rapid data-driven development of custom-printed circuits for flexible and environmentally friendly electronic applications. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Additive Manufacturing, Machine Learning, Electronic Materials |