基于IDBO-BP的喷墨印刷液滴质量预测的方法研究

李莹; 娄杨伟; 李海山; 何自芬; 刘梦莲

doi:10.19554/j.cnki.1001-3563.2025.11.019

PDF(4207 KB)

包装工程（技术栏目） ›› 2025, Vol. 46 ›› Issue (11) : 174-184. DOI: 10.19554/j.cnki.1001-3563.2025.11.019

自动化与智能化技术

基于IDBO-BP的喷墨印刷液滴质量预测的方法研究

李莹, 娄杨伟, 李海山, 何自芬, 刘梦莲

作者信息 +

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

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

Author information +

文章历史 +

摘要

目的实现喷墨印刷液滴质量的精准预测和控制,提升喷墨印刷质量。方法提出一种改进的蜣螂优化器（Improved dung beetle optimizer,IDBO）来优化反向传播（Back propagation,BP）神经网络的模型,以精确预测喷墨印刷过程中的液滴质量。首先,采用动态反向学习策略初始化种群,以增强种群的多样性和均匀性;其次,引入黄金正弦因子,提升算法的收敛速度和寻优精度,同时平衡局部和全局搜索能力。结果通过对9个基准测试函数的性能评估,IDBO算法展现出更优的收敛精度和更快的收敛速度。应用IDBO优化的BP神经网络进行液滴质量预测,IDBO-BP模型显著降低了均方根误差（Root mean square error,RMSE）和平均绝对误差（Mean bsolute error,MAE）,最高分别降低了48%和38%,同时拟合优度（）提升了3%。结论结果证实IDBO-BP模型在预测喷墨印刷液滴质量方面的优越性能,并验证了其在喷墨印刷领域的应用潜力。

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.

导出引用

李莹, 娄杨伟, 李海山, 何自芬, 刘梦莲. 基于IDBO-BP的喷墨印刷液滴质量预测的方法研究[J]. 包装工程（技术栏目）. 2025, 46(11): 174-184 https://doi.org/10.19554/j.cnki.1001-3563.2025.11.019

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

中图分类号： TP391.9

参考文献

[1] 李世凯, 王雨欣, 黄蓓青. 喷墨打印技术在印刷电子制造中的应用进展[J]. 丝网印刷, 2024, 42(2): 32-36.
LI S K, WANG Y X, HUANG B Q.Application Progress of Inkjet Printing Technology in Printing and Electronic Manufacturing[J]. Screen Printing, 2024, 42(2): 32-36.
[2] 符晓, 姚日晖, 杨跃鑫, 等. 喷墨打印电子用功能墨水的研究进展[J]. 印刷与数字媒体技术研究, 2023(3): 1-16.
FU X, YAO R H, YANG Y X, et al.Research Progress of Functional Inks for Inkjet-Printed Electronics[J]. Printing and Digital Media Technology Study, 2023(3): 1-16.
[3] 曾亭元. 数字印刷技术在包装印刷中的应用[J]. 上海轻工业, 2023(1): 126-128.
ZENG T Y.Application of Digital Printing Technology in Packaging Printing[J]. Shanghai Light Industry, 2023(1): 126-128.
[4] KIEFER O, BREITKREUTZ J.Comparative Investigations on Key Factors and Print Head Designs for Pharmaceutical Inkjet Printing[J]. International Journal of Pharmaceutics, 2020, 586: 119561.
[5] SUGIYAMA Y, ISHIBASHI H, OSHIMA T, et al.RF Printed Circuit Partially Combined with the Inkjet-Printing Technology[C]//2021 IEEE Asia-Pacific Microwave Conference (APMC). Brisbane: IEEE, 2021: 166-168.
[6] 赵泽贤, 徐萌, 彭聪, 等. 喷墨打印高迁移率铟锌锡氧化物薄膜晶体管[J]. 物理学报, 2024, 73(12): 365-372.
ZHAO Z X, XU M, PENG C, et al.Inkjet Printing High Mobility Indium-Zinc-Tin Oxide Thin Film Transistor[J]. Acta Physica Sinica, 2024, 73(12): 365-372.
[7] RAVIKUMAR K, DANGATE M S.Advancements in Stretchable Organic Optoelectronic Devices and Flexible Transparent Conducting Electrodes: Current Progress and Future Prospects[J]. Heliyon, 2024, 10(13): e33002.
[8] 陈飞宇, 秦世清, 钱乐洋, 等. 一种抑制GaN HEMT栅极尖峰电压的RCD电路设计[J/OL]. 电源学报, 2024: 1-16[2024-12-05]. https://kns.cnki.net/KCMS/detail/detail.aspxfilename=DYXB20241204001&dbname=CJFD&dbcode=CJFQ.
CHEN F Y, QIN S Q, QIAN L Y, et al. An RCD Circuit Design to Suppress Gate Peak Voltage of GaN HEMT[J/OL]. Journal of Power Supply, 2024: 1-16[2024-12-05]. https://kns.cnki.net/KCMS/detail/detail.aspxfilename=DYXB20241204001&dbname=CJFD&dbcode=CJFQ.
[9] 曾德兴, 杜明星. 基于关断漏源极电压的SiC MOSFET结温监测法[J/OL]. 天津理工大学学报, 2024: 1-10[2024-09-14]. https://kns.cnki.net/KCMS/detail/detail.aspxfilename=TEAR2024091300N&dbname=CJFD&dbcode=CJFQ.
ZENG D X, DU M X. Junction Temperature Monitoring Method of SiC MOSFET Based on Turn-off Drain-Source Voltage[J/OL]. Journal of Tianjin University of Technology, 2024: 1-10 [2024-09-14]. https://kns.cnki.net/KCMS/detail/detail.aspxfilename=TEAR2024091300N&dbname=CJFD&dbcode=CJFQ.
[10] KRAINER S, SMIT C, HIRN U.The Effect of Viscosity and Surface Tension on Inkjet Printed Picoliter Dots[J]. RSC Advances, 2019, 9(54): 31708-31719.
[11] JIA Y H, MEI Y, ZHANG M J.A Bilevel Ant Colony Optimization Algorithm for Capacitated Electric Vehicle Routing Problem[J]. IEEE Transactions on Cybernetics, 2022, 52(10): 10855-10868.
[12] LI H, CHEN J J, LI X Y, et al.Artificial Neural Network and Genetic Algorithm Coupled Fermentation Kinetics to Regulate L-Lysine Fermentation[J]. Bioresource Technology, 2024, 393: 130151.
[13] KATIPOĞLU O M, KEBLOUTI M, MOHAMMADI B. Application of Novel Artificial Bee Colony Optimized ANN and Data Preprocessing Techniques for Monthly Streamflow Estimation[J]. Environmental Science and Pollution Research International, 2023, 30(38): 89705-89725.
[14] MA W, WANG W, CAO Y.Mechanical Properties of Wood Prediction Based on the NAGGWO-BP Neural Network[J]. Forests, 2022, 13(11): 1870.
[15] KENNEDY J.Particle Swarm Optimization[J]. Proceedings of ICNN'95-International Conference on Neural Networks, 1995, 4(8): 1942-1948.
[16] XUE J K, SHEN B.Dung Beetle Optimizer: A New Meta-Heuristic Algorithm for Global Optimization[J]. The Journal of Supercomputing, 2023, 79(7): 7305-7336.
[17] 王文州, 魏文华, 卫德林. 基于EMD-DBO-GRU的降水预测模型研究及应用[J]. 甘肃水利水电技术, 2023, 59(9): 9-13.
WANG W Z, WEI W H, WEI D L.Research and Application of Precipitation Prediction Model Based on EMD-DBO-GRU[J]. Gansu Water Resources and Hydropower Technology, 2023, 59(9): 9-13.
[18] ZHANG Y H, MA T Y, LI T, et al.Short-Term Load Forecasting Based on DBO-LSTM Model[C]//2023 3rd International Conference on Energy Engineering and Power Systems (EEPS). Dali: IEEE, 2023: 972-977.
[19] LI S T, LI J H.Chaotic Dung Beetle Optimization Algorithm Based on Adaptive T-Distribution[C]//2023 IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA). Chongqing: IEEE, 2023: 925-933.
[20] HE Y, WANG W, LI M, et al.A Short-Term Wind Power Prediction Approach Based on an Improved Dung Beetle Optimizer Algorithm, Variational Modal Decomposition, and Deep Learning[J]. Computers and Electrical Engineering, 2024, 116: 109182.
[21] ZHU X, NI C, CHEN G L, et al.Optimization of Tungsten Heavy Alloy Cutting Parameters Based on RSM and Reinforcement Dung Beetle Algorithm[J]. Sensors, 2023, 23(12): 5616.
[22] XU Y L, YANG Z L, LI X P, et al.Dynamic Opposite Learning Enhanced Teaching-Learning-Based Optimization[J]. Knowledge-Based Systems, 2020, 188: 104966.
[23] TANYILDIZI E, DEMIR G.Golden Sine Algorithm: A Novel Math-Inspired Algorithm[J]. Advances in Electrical and Computer Engineering, 2017, 17(2): 71-78.
[24] LI G B, HU T Y, BAI D W.BP Neural Network Improved by Sparrow Search Algorithm in Predicting Debonding Strain of FRP-Strengthened RC Beams[J]. Advances in Civil Engineering, 2021(1): 9979028.
[25] WOLPERT D H, MACREADY W G.No Free Lunch Theorems for Optimization[J]. IEEE Transactions on Evolutionary Computation, 1997, 1(1): 67-82.
[26] 刘艺梦, 丁小明, 王会强, 等. 基于蜣螂算法优化BP的冬夏生菜根区温度预测模型[J]. 农业工程学报, 2024, 40(5): 231-238.
LIU Y M, DING X M, WANG H Q, et al.Prediction Model for Winter and Summer Lettuce Root Zone Temperature Based on Dung Beetle Algorithm to Optimize BP[J]. Transactions of the Chinese Society of Agricultural Engineering, 2024, 40(5): 231-238.
[27] 赵丙秀, 董宁. 基于WOA-BP神经网络下马铃薯产量预测分析模型[J]. 农机化研究, 2024, 46(3): 47-51.
ZHAO B X, DONG N.Potato Yield Prediction Analysis Model Based on WOA-BP Neural Network[J]. Journal of Agricultural Mechanization Research, 2024, 46(3): 47-51.
[28] LIU X R, YANG J M, YUAN L J.Predicting the High Heating Value and Nitrogen Content of Torrefied Biomass Using a Support Vector Machine Optimized by a Sparrow Search Algorithm[J]. RSC Advances, 2023, 13(2): 802-807.
[29] CHEN J, WEI Y L, MA X H.Forecasting Slope Displacement of the Agricultural Mountainous Area Based on the ACO-SVM Model[J]. Computational Intelligence and Neuroscience, 2022, 2022: 2519035.
[30] 李淑锋, 李加, 张玉峰, 等. 基于粒子群优化的支持向量机停电预测研究[J]. 南京理工大学学报, 2022, 46(4): 460-466.
LI S F, LI J, ZHANG Y F, et al.Research on Power Outage Prediction of Support Vector Machine Based on Particle Swarm Optimization[J]. Journal of Nanjing University of Science and Technology, 2022, 46(4): 460-466.
[31] 鲍伟, 任超. 基于GWO-BP神经网络的电池SOC预测方法研究[J]. 计算机应用与软件, 2022, 39(9): 65-71.
BAO W, REN C.Research on Prediction Method of Battery Soc Based on gwo-Bp Network[J]. Computer Applications and Software, 2022, 39(9): 65-71.
[32] 阳诚平, 刘奇, 刘曙, 等. 基于PSO-BP神经网络的回转窑喷煤量预测[J]. 烧结球团, 2024, 49(5): 47-55.
YANG C P, LIU Q, LIU S, et al.Prediction of Coal Injection Rate in Rotary Kiln Based on PSO-BP Neural Network[J]. Sintering and Pelletizing, 2024, 49(5): 47-55.