Name
Title | ||
|---|---|---|
System Identification Of A Discrete Repetitive Process Model For Electrohydrodynamic Jet Printing |
Abstract | ||
|---|---|---|
Microscale additive manufacturing processes have a great potential to manufacture microscale sensors and devices in a layer-to-layer fashion with freeform control of device architecture. However, the layer-to-layer dynamics in microscale additive manufacturing are not well understood. This manuscript investigates layer-to-layer dynamics from a system identification perspective. This work defines a class of input signals, system identification algorithm for microscale additive manufacturing modeled as a discrete repetitive system, and the experimental protocol to empirically the plant model and validate the model for a different input signal. A case study applied to the microscale additive manufacturing process electrohydrodynamic jet printing demonstrates that the identified model from a training set is extensible to a validation data set, with less than 4% error between the system identification of the training and validation data sets. |
Year | Venue | Field |
|---|---|---|
2018 | 2018 ANNUAL AMERICAN CONTROL CONFERENCE (ACC) | Electrohydrodynamics,Training set,Data set,Computer science,Microscale chemistry,Control engineering,Three dimensional printing,System identification,Extensibility,Manufacturing process |
DocType | ISSN | Citations |
Conference | 0743-1619 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhi Wang | 1 | 76 | 14.27 |
| Patrick M. Sammons | 2 | 3 | 1.46 |
| Christopher P. Pannier | 3 | 0 | 0.34 |
| Kira Barton | 4 | 57 | 18.97 |
| David J. Hoelzle | 5 | 55 | 7.25 |