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Article

DGMN-MISABO: A Physics-Informed Degradation and Optimization Framework for Realistic Synthetic Droplet Image Generation in Inkjet Printing

by
Jiacheng Cai
1,
Jiankui Chen
1,2,*,
Wei Tang
2,
Jinliang Wu
1,
Jingcheng Ruan
1 and
Zhouping Yin
1
1
The State Key Laboratory of Intelligent Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
2
Wuhan National Innovation Technology Optoelectronics Equipment Co., Ltd., Wuhan 430078, China
*
Author to whom correspondence should be addressed.
Machines 2025, 13(8), 657; https://doi.org/10.3390/machines13080657
Submission received: 25 June 2025 / Revised: 23 July 2025 / Accepted: 25 July 2025 / Published: 27 July 2025
(This article belongs to the Section Advanced Manufacturing)

Abstract

The Online Droplet Inspection system plays a vital role in closed-loop control for OLED inkjet printing. However, generating realistic synthetic droplet images for reliable restoration and precise measurement of droplet parameters remains challenging due to the complex, multi-factor degradation inherent to microscale droplet imaging. To address this, we propose a physics-informed degradation model, Diffraction–Gaussian–Motion–Noise (DGMN), that integrates Fraunhofer diffraction, defocus blur, motion blur, and adaptive noise to replicate real-world degradation in droplet images. To optimize the multi-parameter configuration of DGMN, we introduce the MISABO (Multi-strategy Improved Subtraction-Average-Based Optimizer), which incorporates Sobol sequence initialization for search diversity, lens opposition-based learning (LensOBL) for enhanced accuracy, and dimension learning-based hunting (DLH) for balanced global–local optimization. Benchmark function evaluations demonstrate that MISABO achieves superior convergence speed and accuracy. When applied to generate synthetic droplet images based on real droplet images captured from a self-developed OLED inkjet printer, the proposed MISABO-optimized DGMN framework significantly improves realism, enhancing synthesis quality by 37.7% over traditional manually configured models. This work lays a solid foundation for generating high-quality synthetic data to support droplet image restoration and downstream inkjet printing processes.
Keywords: inkjet printing manufacturing; physics-informed image degradation; synthetic droplet image generation; subtraction-average-based optimizer; metaheuristic; optimization inkjet printing manufacturing; physics-informed image degradation; synthetic droplet image generation; subtraction-average-based optimizer; metaheuristic; optimization

Share and Cite

MDPI and ACS Style

Cai, J.; Chen, J.; Tang, W.; Wu, J.; Ruan, J.; Yin, Z. DGMN-MISABO: A Physics-Informed Degradation and Optimization Framework for Realistic Synthetic Droplet Image Generation in Inkjet Printing. Machines 2025, 13, 657. https://doi.org/10.3390/machines13080657

AMA Style

Cai J, Chen J, Tang W, Wu J, Ruan J, Yin Z. DGMN-MISABO: A Physics-Informed Degradation and Optimization Framework for Realistic Synthetic Droplet Image Generation in Inkjet Printing. Machines. 2025; 13(8):657. https://doi.org/10.3390/machines13080657

Chicago/Turabian Style

Cai, Jiacheng, Jiankui Chen, Wei Tang, Jinliang Wu, Jingcheng Ruan, and Zhouping Yin. 2025. "DGMN-MISABO: A Physics-Informed Degradation and Optimization Framework for Realistic Synthetic Droplet Image Generation in Inkjet Printing" Machines 13, no. 8: 657. https://doi.org/10.3390/machines13080657

APA Style

Cai, J., Chen, J., Tang, W., Wu, J., Ruan, J., & Yin, Z. (2025). DGMN-MISABO: A Physics-Informed Degradation and Optimization Framework for Realistic Synthetic Droplet Image Generation in Inkjet Printing. Machines, 13(8), 657. https://doi.org/10.3390/machines13080657

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