Development of Charge Control Optimization Technology for Continous Inkjet Printer Using Bayesian Optimiztion

Abstract

Conventionally, to improve printing quality of continuous-type inkjet printer, it has been necessary to repeat many experiments manually. Therefore, to streamline the improvement process, we developed an bayesian optimization system to determine an appropriate control. This system is composed of ink-droplet flight simulation using OpenFOAM, response surface method with Kriging model, and multi-objetive genetic algorithm. In this study, expected improvement and mutual information were implemented as aqusition functions. Optimizations with four different settings were performed. As a result, an optimization with expected improvement was most efficient and one with mutual information had advantage in terms of solution diversity.