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 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.