Inkjet printing technology for fabricating organic light-emitting diode display panels offers advantages such as high material utilization and the capability for large-area manufacturing. When printing display panels ...
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Inkjet printing technology for fabricating organic light-emitting diode display panels offers advantages such as high material utilization and the capability for large-area manufacturing. When printing display panels with varying resolutions, ejecting droplets of different sizes from the nozzle is often necessary to balance print quality and efficiency. However, due to nozzle size limitations, the volume range of stable droplets produced by a single-pulse driving waveform is relatively narrow, with the maximum volume being less than twice the minimum volume. Therefore, approaches based on superposition of multi-pulse waveforms have attracted attention, but existing studies only implement manual design of waveforms based on experimental laws and rarely involve automatic regulation of multi-pulse waveform parameters, which is not favorable for industrial applications. Based on combining a meniscus vibration model with industrial ejection data, this paper extracts control strategies from historical data using deep reinforcement learning, and recommends initial waveform parameters through a fuzzy system. Then, the multi-pulse waveform parameters are automatically adjusted in real-time based on the observed droplet volume to fuse the droplets at the nozzle, enabling a wider range of droplet volume closed-loop control. Experiments on industrial inkjet printing equipment implemented intelligent closed-loop regulation of multi-pulse driving waveforms, successfully controlling droplets of different sizes such as 2, 4, and 8 picoliter with an error accuracy of less than +/- 4%. This approach applies artificial intelligence algorithms to inkjet printing engineering and intelligently adjusts the multi-pulse waveform parameters to enhance the controllable range of droplet volumes.
Optimizing an appropriate design of decoupling capacitors (decaps) is a primary challenge in the field of power delivery network (PDN). A fast PDN impedance acquisition method will expedite the process and enhance the...
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ISBN:
(纸本)9798350330991;9798350331004
Optimizing an appropriate design of decoupling capacitors (decaps) is a primary challenge in the field of power delivery network (PDN). A fast PDN impedance acquisition method will expedite the process and enhance the efficiency within an expansive search space for decaps optimization. In this paper, we propose a model and data fusion method to analyze the PDN impedance. The fusion method incorporates the polynomial similarity of the PDN impedance into the deep learning (DL) framework. Moreover, we reduce the dimensionality of the input to compact the network structure and decrease the training time. The experimental results demonstrate that our proposed method promotes the accuracy by 8% compared to other DL methods. In the time of generating outputs, our method is 80 times faster than conventional electronic design automation (EDA) simulation.
The monitoring of cutting tool wear has a great significance for the processing quality and stability. To overcome the difficulty to reflect all the wear mechanisms for the monitoring method based on finite element mo...
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The monitoring of cutting tool wear has a great significance for the processing quality and stability. To overcome the difficulty to reflect all the wear mechanisms for the monitoring method based on finite element model or the excessive dependence on data extraction quality for the monitoring method based on sensor data, this paper proposes a model and data fusion method based on particle filter algorithm. Two different kinds of materials AISI 1045 and AISI 4340 are chosen to carry out the turning experiments. The mean absolute percentage error (MAPE) of the fusion method is 0.6%similar to 4.1% lower and the coefficient of determination (R-2) is more close to 1 compared with the finite element model method or sensor data method individually. Experimental results verify the feasibility and the superiority of the fusion method.
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