Machine learning based multi-parameter droplet optimisation model study.

ABSTRACT: Continuous inkjet technology, as a key technology in the field of industrial printing, is favoured for its excellent printing speed, precision and versatility. In order to achieve the accurate generation of ideal droplets in continuous inkjet devices, this paper proposes a new parameter optimisation method, BO-GP, which combines the Bayesian optimisation algorithm with computer vision, and after 50 rounds of iterations, it can converge to the optimal values of the control parameters, and successfully constructs the Pareto frontier of the control parameters. In this paper, experiments were conducted on two different device droplet image datasets, a millimetre-scale inkjet device and a microfluidic device, respectively. Compared with the original BO in Loop method, the optimised minimum obje

SUBMITTER: Li T 

PROVIDER: S-EPMC12271370 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature