MODELING AND CONTROL OF DROP VOLUME AND DROP JETTING VELOCITY IN DROP-ON-DEMAND INKJET PRINTING

Drop-on-demand (DoD) inkjet printing is a maskless, additive, low-cost, flexible, and digital process for delivering materials/inks that can be adapted to a wide range of additive and functional scenarios, including, but not limited to, pharmaceutical manufacturing, electronics

fabrication, and scalable patterning. However, producing consistent small dosages with high placement accuracy can be challenging due to variations in ink properties and environmental conditions, leading to variations in printed content and drop placement errors. Since the current open-loop calibrations are time-consuming and subject to frequent line stoppage, this work aims to develop a modeling and control framework for regulating these features, drop volume and drop jetting velocity, which also can benefit the integration of commercial

printheads with different actuations into wide inkjet printing applications. 

An equivalent circuit model based on the hydraulic analogy is proposed to characterize the drop volume growth at the nozzle and motivate the development of a data-driven autoregressive-exogenous (ARX) model integrated with a drop volume adjuster for drop volume estimation. Given the limited control access in the commercial printer, a two-input-two-output plant model is derived from experimental data which relates two control parameters of the firing waveform to two features, drop volume and drop jetting velocity, with stochastic parameters. The plant model then is synthesized into the control design, which uses drop-image-based one-step look ahead estimation of process model parameters. Boundedness and convergence of the parameter estimation error and stability of the closed-loop system are analyzed. Experimental results demonstrate the effectiveness of the proposed controller in retaining consistent drop volume and drop jetting velocity for thousands of drops with narrow variances.