Droplet Mass Prediction Method of Inkjet Printing Based on IDBO-BP

LI Ying; LOU Yangwei; LI Haishan; HE Zifen; LIU Menglian

doi:10.19554/j.cnki.1001-3563.2025.11.019

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Packaging Engineering ›› 2025, Vol. 46 ›› Issue (11) : 174-184. DOI: 10.19554/j.cnki.1001-3563.2025.11.019

Automatic and Intelligent Technology

Droplet Mass Prediction Method of Inkjet Printing Based on IDBO-BP

LI Ying, LOU Yangwei, LI Haishan, HE Zifen, LIU Menglian

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Abstract

The work aims to achieve precise prediction and control of droplet mass in inkjet printing and improve the quality of inkjet printing. An improved dung beetle optimizer (IDBO) was proposed to optimize back propagation (BP) neural network model to accurately predict the mass of the droplets in ink jet printing. First, the dynamic reverse learning strategy was used to initialize the population to enhance the diversity and uniformity of the population. Secondly, the golden sine factor was introduced to improve the convergence speed and optimization accuracy of the algorithm, while balancing the local and global search capabilities. Through the performance evaluation of nine benchmark functions, the IDBO algorithm showed better convergence accuracy and faster convergence speed. The IDBO optimized BP neural network was used to predict droplet mass. The results showed that the root mean square error (RMSE) and mean absolute error (MAE) of the IDBO-BP model were significantly reduced by 48% and 38% respectively, and the goodness of fit was increased by 3%. These results confirm the superior performance of the IDBO-BP model in predicting the droplet mass of inkjet printing, and verify its application potential in the field of inkjet printing.

Key words

improved dung beetle optimizer / dynamic reverse learning strategy / golden sine factor / IDBO-BP model / prediction of inkjet droplet mass

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LI Ying, LOU Yangwei, LI Haishan, HE Zifen, LIU Menglian. Droplet Mass Prediction Method of Inkjet Printing Based on IDBO-BP[J]. Packaging Engineering. 2025, 46(11): 174-184 https://doi.org/10.19554/j.cnki.1001-3563.2025.11.019

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