Deep Learning-Based Inkjet Droplet Detection for Jetting Characterizations and Multijet Synchronization-Reference-Cited by-同舟云学术

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Deep Learning-Based Inkjet Droplet Detection for Jetting Characterizations and Multijet Synchronization

Published:2024-03-26 Issue:14 Volume:16 Page:18040-18051
ISSN:1944-8244
Container-title:ACS Applied Materials & Interfaces
language:en
Short-container-title:ACS Appl. Mater. Interfaces

Author:

Choi Eunsik¹^ORCID,Choi Suwon²,An Kunsik²^ORCID,Kang Kyung-Tae¹

Affiliation:

1. Digital Transformation R&D Department, Korea Institute of Industrial Technology (KITECH), Ansan 15588, Republic of Korea

2. Department of Mechatronics Engineering, Konkuk University Glocal Campus, 268 Chungwon-daero, Chungju 27478, Republic of Korea

Funder

National Research Foundation of Korea

Ministry of SMEs and Startups

Korea Institute of Industrial Technology

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acsami.4c00972

Reference66 articles.

1. High-resolution electrohydrodynamic jet printing

2. Inkjet Printing-Process and Its Applications

3. Inkjet Printing for Materials and Devices

4. Inkjet printing as a deposition and patterning tool for polymers and inorganic particles

5. Inkjet Printing of Polymer Thin-Film Transistor Circuits

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1. An intelligent droplet sorter using optimized liquid-metal electrodes for droplet sorting under a low voltage;Sensors and Actuators B: Chemical;2024-11

2. Temporal and Spatial Distribution Characteristics of Crosstalk Lines Generated by Irradiating Progressive Scan Charge-Coupled Device Camera with Continuous Laser;Sensors;2024-06-19

3. Autonomous optimization of process parameters and in-situ anomaly detection in aerosol jet printing by an integrated machine learning approach;Additive Manufacturing;2024-04

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