Azala

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

Gorde:
Argitaratua izan da:Machines vol. 13, no. 8 (2025), p. 657-675
Egile nagusia: Cai Jiacheng
Beste egile batzuk: Chen Jiankui, Tang, Wei, Wu, Jinliang, Ruan Jingcheng, Yin Zhouping
Argitaratua:
MDPI AG
Gaiak:
Accuracy
Physics
Printers (data processing)
Datasets
Learning
Optimization
Nozzles
Closed loops
Image restoration
Local optimization
Algorithms
Image quality
Degradation
Blurring
Ink jet printers
Image processing
Parameters
Feedback control
Inkjet printing
Droplets
Diffraction
Synthetic data
Sarrera elektronikoa:Citation/Abstract
Full Text + Graphics
Full Text - PDF
Etiketak: Etiketa erantsi
Etiketarik gabe, Izan zaitez lehena erregistro honi etiketa jartzen!
Laburpena: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.
ISSN:2075-1702
DOI:10.3390/machines13080657
Baliabidea:Engineering Database