Prediction and Optimization of Dielectric Layer Thickness and Uniformity Based on Piezoelectric Valve Inkjet Printing

LIU Jiawang; SUN Jian; SUN Yanhui; LYU Jingxiang; ZHAO Yungui; AN Zhanjun

doi:10.3969/j.issn.1674-6457.2026.01.010

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Journal of Netshape Forming Engineering ›› 2026, Vol. 18 ›› Issue (1) : 96-109. DOI: 10.3969/j.issn.1674-6457.2026.01.010

Additive Manufacturing

Prediction and Optimization of Dielectric Layer Thickness and Uniformity Based on Piezoelectric Valve Inkjet Printing

LIU Jiawang¹, SUN Jian¹, SUN Yanhui^1,*, LYU Jingxiang¹, ZHAO Yungui², AN Zhanjun²

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Abstract

To address the critical issues of thickness control and uniformity optimization in dielectric layer fabrication, the work aims to establish relationship models between the piezoelectric valve inkjet printing process parameters and the printed thickness and uniformity of dielectric layers to complete parameter optimization, thereby achieving precise control of both thickness and uniformity in dielectric layer formation. The quantitative relationship model between the process parameters (delay time, printing speed, line spacing, and number of printed layers) and the thickness and uniformity of the dielectric layer was established with Response Surface Methodology (RSM) and intelligent algorithms (Backpropagation neural network (BP), Particle Swarm-optimized BP neural network (PSO-BP) and Improved Particle Swarm-optimized BP neural network (IPSO-BP)). Then, the Analysis of Variance (ANOVA) was applied to assess the statistical significance and interaction mechanisms of each parameter, and the prediction accuracy of various models was compared. The target thicknesses selected for dielectric layer printing were 200, 400, 600, and 800 μm. With the minimization of printing thickness error and the optimal control of uniformity as optimization objectives, the Response Surface Methodology (RSM) and Improved Particle Swarm Optimization (IPSO) algorithm were selected to solve the optimization problems to obtain the optimized process parameters and the optimization results were compared. The prediction accuracy of the IPSO-BP neural network surpassed that of all other models and achieved the lowest root mean square errors (10.935 7 for thickness and 2.457 4 for uniformity) and mean absolute errors (4.283 4 for thickness and 1.170 9 for uniformity), with the highest coefficients of determination (99.89% for thickness and 95.49% for uniformity). The mean relative errors of thickness and uniformity of the printed dielectric layer under process parameters obtained by the IPSO algorithm were 3.50% and 16.75%, much lower than those obtained by the RSM method (15.21% and 29.06%). Compared to SVM, RF, BP and PSO-BP neural networks, the IPSO-BP algorithm has better adaptability, stronger global search ability and higher prediction accuracy in dealing with the complex nonlinear problems in this study. In the process of printing dielectric layers with piezoelectric valves, the IPSO-BP-IPSO method can achieve precise control of dielectric layer thickness and the optimization of its uniformity by optimizing process parameters.

Key words

piezoelectric valve printing / dielectric layer / response surface methodology / BP neural network / improved particle swarm optimization

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LIU Jiawang, SUN Jian, SUN Yanhui, LYU Jingxiang, ZHAO Yungui, AN Zhanjun. Prediction and Optimization of Dielectric Layer Thickness and Uniformity Based on Piezoelectric Valve Inkjet Printing[J]. Journal of Netshape Forming Engineering. 2026, 18(1): 96-109 https://doi.org/10.3969/j.issn.1674-6457.2026.01.010

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Funding

National Key Research and Development Program of China (2022YFB4602800); Xi'an Municipal Science and Technology Plan Critical Core Technology Research Project for Key Industrial Chains (23LLRH0079)