Machine learning based multi-parameter droplet optimisation model study
Uloženo v:
| Vydáno v: | Scientific Reports (Nature Publisher Group) vol. 15, no. 1 (2025), p. 25966 |
|---|---|
| Hlavní autor: | |
| Další autoři: | , , |
| Vydáno: |
Nature Publishing Group
|
| Témata: | |
| On-line přístup: | Citation/Abstract Full Text Full Text - PDF |
| Tagy: |
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3231090506 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2045-2322 | ||
| 024 | 7 | |a 10.1038/s41598-025-09435-8 |2 doi | |
| 035 | |a 3231090506 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 274855 |2 nlm | ||
| 100 | 1 | |a Li, Ting |u Beijing Institute of Graphic Communication, Beijing Key Laboratory of Signal and Information Processing for High-End Printing Equipment, Beijing, China (GRID:grid.443253.7) (ISNI:0000 0004 1791 5856) | |
| 245 | 1 | |a Machine learning based multi-parameter droplet optimisation model study | |
| 260 | |b Nature Publishing Group |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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 objective function value is reduced from 0.378 to 0.331 in the millimetre-scale device, and from 0.073 to 0.046 in the microfluidic device. Moreover, the Pareto solution of the 10 sets of predicted parameters output using the BO-GP method tends to be stable with fluctuations around 0.1, and it takes only 1 h to derive the control conditions for achieving high roundness, high yield and uniform size droplets. | |
| 653 | |a Machine learning | ||
| 653 | |a Accuracy | ||
| 653 | |a Methods | ||
| 653 | |a Datasets | ||
| 653 | |a Bayesian analysis | ||
| 653 | |a Automation | ||
| 653 | |a Microfluidics | ||
| 653 | |a Computer vision | ||
| 653 | |a Electric fields | ||
| 653 | |a Objective function | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Lu, Likun |u Beijing Institute of Graphic Communication, Beijing Key Laboratory of Signal and Information Processing for High-End Printing Equipment, Beijing, China (GRID:grid.443253.7) (ISNI:0000 0004 1791 5856) | |
| 700 | 1 | |a Zeng, Qingtao |u Beijing Institute of Graphic Communication, Beijing Key Laboratory of Signal and Information Processing for High-End Printing Equipment, Beijing, China (GRID:grid.443253.7) (ISNI:0000 0004 1791 5856) | |
| 700 | 1 | |a Liao, Kexin |u Beijing Institute of Graphic Communication, Beijing Key Laboratory of Signal and Information Processing for High-End Printing Equipment, Beijing, China (GRID:grid.443253.7) (ISNI:0000 0004 1791 5856) | |
| 773 | 0 | |t Scientific Reports (Nature Publisher Group) |g vol. 15, no. 1 (2025), p. 25966 | |
| 786 | 0 | |d ProQuest |t Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3231090506/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3231090506/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3231090506/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |