<p>Inkjet printing has been extensively employed across various industrial applications due to its capability to produce high-resolution outputs and accommodate a diverse range of materials. However, its printing speed (volumetric printing rate or volumetric productivity) has remained a bottleneck for large-scale manufacturing. This study introduces a new strategy aimed at enhancing the speed of piezoelectric inkjet printing by optimizing waveform parameters. Expanding on our prior work, which analyzed the relationship between driving signal parameters and key factors like droplet volume and jetting frequency, we now focus on optimizing these parameters to maximize printing speed. To establish a fair comparison across different setups, an equivalent printing speed metric was first defined, normalizing for configuration differences such as nozzle size. Using this metric, a benchmark driving signal was designed to achieve the equivalent printing speed of a selected commercial printhead under comparable conditions. The benchmark driving signal was then optimized to enhance printing performance, and the results were validated experimentally with a custom-built setup equipped with a high-speed camera for real-time droplet formation analysis. Our findings demonstrate that the optimized signal achieves successful jetting and improves printing speed, showing up to a five-fold increase compared to the benchmark signal in our experimental setup.</p>

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Optimizing driving waveforms to enhance inkjet printing speed

  • Chao Sui,
  • Wenchao Zhou

摘要

Inkjet printing has been extensively employed across various industrial applications due to its capability to produce high-resolution outputs and accommodate a diverse range of materials. However, its printing speed (volumetric printing rate or volumetric productivity) has remained a bottleneck for large-scale manufacturing. This study introduces a new strategy aimed at enhancing the speed of piezoelectric inkjet printing by optimizing waveform parameters. Expanding on our prior work, which analyzed the relationship between driving signal parameters and key factors like droplet volume and jetting frequency, we now focus on optimizing these parameters to maximize printing speed. To establish a fair comparison across different setups, an equivalent printing speed metric was first defined, normalizing for configuration differences such as nozzle size. Using this metric, a benchmark driving signal was designed to achieve the equivalent printing speed of a selected commercial printhead under comparable conditions. The benchmark driving signal was then optimized to enhance printing performance, and the results were validated experimentally with a custom-built setup equipped with a high-speed camera for real-time droplet formation analysis. Our findings demonstrate that the optimized signal achieves successful jetting and improves printing speed, showing up to a five-fold increase compared to the benchmark signal in our experimental setup.