System Identification of a Discrete Repetitive Process Model for Electrohydrodynamic Jet Printing
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 manuscrip...
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Published in | 2018 Annual American Control Conference (ACC) pp. 4464 - 4471 |
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Main Authors | , , , , |
Format | Conference Proceeding |
Language | English |
Published |
AACC
01.06.2018
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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ISSN: | 2378-5861 |
DOI: | 10.23919/ACC.2018.8431303 |