The drive waveform has a critical role in the operation of the printhead with the ink to be jetted 18. It is widely assumed that Z is required to be in the 1 < Z < 14 range for proper drop formation 16, 17.Ī drive waveform is a set of timed actuator wall movements that generate and propagate acoustic pressure waves within a channel. These phenomena can be characterized by dimensionless parametric groups such as Reynolds number ( Re), Weber number ( We), and Bond number ( Bo) Inertia, capillary force, and viscous dissipation are generally used to govern the generation of inkjet drops 15. The ink-jetting process of fluids involves a variety of free surface deformation. Many of the inkjet printing applications necessitate a reliable jetting behavior of functional ink, which is mainly determined by the ink fluid characteristics and drive waveform design 11, 12, 13, 14. The displacement of the chamber wall controlled by a drive waveform produces the pressure required for a drop to form and eject from the nozzle. As the name implies, a piezoelectric inkjet printhead uses a piezoelectric actuator to convert applied electrical energy into mechanical deformation of an ink chamber 10. A digitally controlled nozzle produces a series of drops with a diameter of typically 10–100 μm. Unlike other printing processes that produce printed patterns by the transfer of ink from pre-defined master patterns 7, 8, 9, inkjet printing builds up patterns directly on a substrate by depositing tiny drops of functional inks that contain a wide range of functional components such as metal nanoparticles, conducting polymers, or biological materials. As a maskless, non-contact patterning method, this additive manufacturing technology is increasingly being considered a crucial technology for new applications, such as next-generation sensors, circuits, displays, and biological tissues 2, 3, 4, 5, 6. Similar content being viewed by othersĭrop-on-demand inkjet printing has been widely embraced as a versatile production technology for both small and large formats of graphical and text printing 1. The proposed method was confirmed through the printing of an unknown model ink with a recommended waveform. A closed-loop prediction algorithm that determined the optimal set of waveform parameters for satellite-free drop formation at a target velocity and employed the most superior learning model was established. Among a variety of machine learning models, Multi-layer Perceptron affords the highest prediction accuracy. Five machine learning models were examined and compared to predict the characteristics of jetting behavior. The high-speed images of their jetting behaviors were acquired and the big datasets of the resulting drop formation and velocity were extracted from the jetting images. Each of the representative 11 model inks with different fluid properties was ink-jetted with 1100 distinct waveform designs. This work presents a closed-loop machine learning approach to designing an optimal drive waveform for satellite-free inkjet printing at a target velocity. A generally adopted rule of thumb for its design is mostly dependent on time-consuming and repetitive manual manipulation of its parameters. A drive waveform, which needs to be optimized with ink’s fluid properties, is critical to reliable inkjet printing.
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