Control of high-resolution electrohydrodynamic jet printing

Kira Barton, Sandipan Mishra, Andrew Alleyne, Placid Ferreira, John Rogers

Research output: Contribution to journalArticlepeer-review

87 Scopus citations

Abstract

This paper discusses the development of a sensing and feedback-feedforward control system for Electrohydrodynamic jet (E-jet) printing. E-jet printing is a nano-manufacturing process that uses electric field induced fluid jet printing through nano-scale nozzles for achieving better control and resolution than traditional jet printing processes. The printing process is controlled by changing the voltage potential between the nozzle and the substrate. However, it is difficult to maintain constant operating conditions such as standoff height during a run of the printing process. The change in operating conditions results in fluctuating jet frequency and droplet diameter. For stabilizing the jetting frequency across a single run, a two degree of freedom (2 DOF) control algorithm is implemented. The feedforward voltage signal is used to compensate for repeatable changes in the operating conditions and is obtained using an Iterative Learning Control (ILC) algorithm. The feedback controller compensates for uncertainty in jetting operating conditions. The jetting frequency is determined in real time by recording electric current pulses when ink droplets are released from the nozzle. This frequency measurement is then used to control the voltage profile across a run to compensate for changing operating conditions. Experimental results are presented to validate the proposed control method.

Original languageEnglish (US)
Pages (from-to)1266-1273
Number of pages8
JournalControl Engineering Practice
Volume19
Issue number11
DOIs
StatePublished - Nov 2011
Externally publishedYes

Bibliographical note

Funding Information:
The authors gratefully acknowledge the contribution and support of the NSF Nano-CEMMS Center under award number DMI-0328162 and CMMI-0749028 .

Keywords

  • Electrohydrodynamic jet printing
  • Iterative Learning Control
  • Nano-manufacturing
  • Two degree of freedom control

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