Drop-on-Demand Inkjet Drop Control With One-Step Look Ahead Estimation of Model Parameters
Drop-on-Demand Inkjet Drop Control With One-Step Look Ahead Estimation of Model Parameters
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DOI：
10.1109/tmech.2023.3277455
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发表时间：
2023-08
期刊：
影响因子：
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通讯作者：
Jie Wang;G. Chiu
中科院分区：
文献类型：
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作者：
Jie Wang;G. Chiu
按需滴喷墨打印在剂量物质制造和可扩展图案化中的应用归因于其能够以高定位精度生产一致的剂量。实际上，在相同的液滴喷射轮廓的情况下，液滴体积和液滴喷射速度会受到墨水特性和环境条件的变化的影响。开环校准非常耗时，并且会导致生产线频繁停机或出现不可接受的产品变化。在这项工作中，基于喷墨校准数据推导了二输入二输出随机墨滴体积和喷射速度模型。开发了一种使用基于液滴图像的过程模型参数的一步前瞻估计的控制算法来调节液滴体积和喷射速度。提供了参数估计误差的有界性和收敛性以及闭环系统的稳定性。实验结果表明，使用所提出的控制算法，液滴体积和喷射速度的相对误差显着降低到 1% 以内。
Applications of drop-on-demand inkjet printing in dosage-matter manufacturing and scalable patterning are attributed to its capacity for producing consistent dosages with high placement accuracy. In practice, with the same drop jetting profile, drop volume and drop jetting velocity are affected by variations in ink properties and environmental conditions. Open-loop calibrations are time-consuming and contribute to frequent line stoppage or unacceptable product variations. In this work, a two-input two-output stochastic drop volume and jetting velocity model is derived based on ink jetting calibration data. A control algorithm using drop-image-based one-step look ahead estimation of process model parameters is developed to regulate drop volume and jetting velocity. Boundedness and convergence of the parameter estimation error and stability of the closed-loop system are provided. Experimental results demonstrate a significant reduction to within 1% relative error in the drop volume and jetting velocity using the proposed control algorithm.