Prediction of Both E-Jet Printing Ejection Cycle Time and Droplet Diameter Based on Random Forest Regression

Chen, Yuanfen; Lao, Zongkun; Wang, Renzhi; Li, Jinwei; Gai, Jingyao; You, Hui

doi:10.3390/mi14030623

Cited by 3 publications

(1 citation statement)

References 35 publications

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“…EHD jet printing now has a rich system of applicable manufacturing materials [17,73], and although researchers have summarized the manufacturing process and industry applications of EHD jet printing, analyses of this technology's associated printing materials are still scarce [74][75][76][77][78][79]. Therefore, a comprehensive and systematic review of this technology's implementable printing materials is urgently needed [80][81][82][83][84].…”

Section: Introductionmentioning

confidence: 99%

Recent Progress in Electrohydrodynamic Jet Printing for Printed Electronics: From 0D to 3D Materials

Wang

Han

et al. 2023

Coatings

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Advanced micro/nano-flexible sensors, displays, electronic skins, and other related devices provide considerable benefits compared to traditional technologies, aiding in the compactness of devices, enhancing energy efficiency, and improving system reliability. The creation of cost-effective, scalable, and high-resolution fabrication techniques for micro/nanostructures built from optoelectronic materials is crucial for downsizing to enhance overall efficiency and boost integration density. The electrohydrodynamic jet (EHD) printing technology is a novel additive manufacturing process that harnesses the power of electricity to create fluid motion, offering unparalleled benefits and a diverse spectrum of potential uses for microelectronic printing in terms of materials, precision, accuracy, and cost-effectiveness. This article summarizes various applications of EHD printing by categorizing them as zero-dimensional (0D), one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) printing materials. Zero-dimensional (quantum dot) materials are predominantly utilized in LED applications owing to their superb optoelectronic properties, high color fidelity, adjustable color output, and impressive fluorescence quantum yield. One- and two-dimensional materials are primarily employed in FET and sensor technologies due to their distinctive physical structure and exceptional optoelectronic properties. Three-dimensional materials encompass nanometals, nanopolymers, nanoglass, and nanoporous materials, with nanometals and nanopolymers finding widespread application in EHD printing technology. We hope our work will facilitate the development of small-feature-size, large-scale flexible electronic devices via EHD printing.

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Section: Introductionmentioning

confidence: 99%

Recent Progress in Electrohydrodynamic Jet Printing for Printed Electronics: From 0D to 3D Materials

Wang

Han

et al. 2023

Coatings

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Regulation of drop-on-demand e-jet printing based on minimum pulse width and critical frequency

Li,

Lao,

et al. 2024

Journal of Manufacturing Processes

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Data-driven prediction of flow fields in a needle-ring-net electrohydrodynamic pump system

Wang,

Cai,

Chen

et al. 2024

Physics of Fluids

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In this paper, a data-mechanism hybrid modeling method for efficiently obtaining an electrohydrodynamic flow field is proposed. First, a backpropagation (BP) model with high accuracy is trained to get the value of essential parameter q0 for the mechanism simulation of flow fields. Subsequently, the mechanism model is used to generate a database for flow field reconstruction. Three machine learning algorithms, namely, BP neural network, random forest regression (RFR), and convolutional neural network (CNN), are employed to predict and reconstruct the flow behaviors of a needle-ring-net electrohydrodynamic pump. The RFR model demonstrates higher accuracy and precision in predicting velocity and pressure in the flow field compared to the BP and CNN models. The use of machine learning models for flow field prediction can significantly reduce the computational time while maintaining the computational accuracy. Additionally, an analysis assessing the impact of varying dataset sizes on the prediction accuracy of the model is conducted. The results indicate that the size of the dataset significantly influences the model predictive performance. Specifically, larger datasets are suggested to enhance both the accuracy and the generalization capabilities of the model. This observation highlights the critical role of dataset size in optimizing the performance of machine learning models for predictive tasks in engineering applications. These results offer important references for improving the design and optimization of electrohydrodynamic pumps.

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Prediction of Both E-Jet Printing Ejection Cycle Time and Droplet Diameter Based on Random Forest Regression

Cited by 3 publications

References 35 publications

Recent Progress in Electrohydrodynamic Jet Printing for Printed Electronics: From 0D to 3D Materials

Recent Progress in Electrohydrodynamic Jet Printing for Printed Electronics: From 0D to 3D Materials

Regulation of drop-on-demand e-jet printing based on minimum pulse width and critical frequency

Data-driven prediction of flow fields in a needle-ring-net electrohydrodynamic pump system

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