About this Abstract |
Meeting |
MS&T22: Materials Science & Technology
|
Symposium
|
Advances in Dielectric Materials and Electronic Devices
|
Presentation Title |
D-4: Neural Network Design for Video Based Automation of Drop-on-Demand Inkjet Drop Formation Optimization |
Author(s) |
Maximilian Estrada, Ruyan Guo, Amar Bhalla, Sean Garnsey, Paul Flynn, Wasim Dipon, Matthew Trippy, Melinda Duong, Carlos Acosta, Bryan Gamboa |
On-Site Speaker (Planned) |
Maximilian Estrada |
Abstract Scope |
Additive manufacturing has applications in medicine, electronics, transportation, and education—just to name a few. One of the main advantages of additive manufacturing is that it provides an inexpensive, rapid prototyping solution. There is, currently, much interest in using drop-on-demand (DOD) inkjet printing to explore functional material applications. Each novel ink possesses its own DOD drop ejection behavior—associated with the ink’s rheological properties. Currently, printhead voltage profiles are adjusted, manually, to compensate for these differences. An RDC network with custom basis functions is explored as a means of vision-based automation of printhead voltage profile optimization with respect to drop quality. |