Affiliation:
1. School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan, Hubei China
2. Wuhan National Innovation Technology Optoelectronics Equipment Co., LTD Wuhan, Hubei China
Abstract
Inkjet printing technology is an emerging manufacturing technique for OLED display devices, where changes in droplet volume after continuous printing are a common issue. Changes in ink properties and disturbances from external environmental factors inevitably lead to variations in droplet volume, which may result in printing defects. Therefore, it is necessary to monitor the volume of droplets during production to ensure they meet production requirements. However, existing techniques for measuring droplet volume are time‐consuming and cannot complete the inspection of all nozzles within a limited time interval.This paper proposes a droplet volume distribution detection method based on Bayesian Theorem. By acquiring historical droplet volume data from the nozzles to design a prior distribution, and inspecting a subset of nozzle volumes, the posterior distribution of ink droplet volume is derived using Bayesian Theorem in conjunction with the prior distribution. Finally, the accuracy of this method was verified on the NEJ‐PR200 inkjet printing equipment. The results show that after sampling 10% of the nozzles, the estimated distribution trend is consistent with the actual situation, and after sampling 20% of the nozzles, the estimation error is less than 3% in the mean volume of ink droplets. Moreover, as the number of samples increases, the accuracy of the estimation also gradually improves.