Open Access Open Access  Restricted Access Subscription Access

Modeling the Height Profile in Drop-on-Demand Printing of UV Curable Ink: Optimization and Precision Control

Adeoye Ibrahim

Abstract


Drop-on-demand (DOD) inkjet printing of UV curable inks offers significant advantages for precision manufacturing, particularly in the fabrication of intricate patterns and structures. However, achieving optimal control over the height profile of printed droplets remains a critical challenge for enhancing print quality, resolution, and material usage. This study focuses on modeling the height profile of UV curable ink droplets in DOD printing, aiming to optimize the printing process for improved accuracy and consistency. The research integrates computational fluid dynamics (CFD) simulations with experimental validation to investigate the dynamics of droplet formation, deposition, and curing. Key process parameters such as ink viscosity, nozzle geometry, and printing speed are systematically varied to assess their impact on droplet height and uniformity. Additionally, the role of UV curing time and intensity on the final droplet shape is analyzed to ensure optimal curing without compromising the print quality. The findings from this study provide valuable insights into the precise control of droplet deposition, offering strategies for enhancing the performance and efficiency of DOD inkjet printing with UV curable inks. This work contributes to the advancement of high-precision, low-waste additive manufacturing techniques applicable in electronics, biomedical devices, and microfabrication industries.


Full Text:

PDF

References


Tejani, Ankitkumar. (2024). AI-Driven Predictive Maintenance in HVAC Systems: Strategies for Improving Efficiency and Reducing System Downtime. 2. 6-19. 10.56472/25839233/IJAST-V2I3P102.

Wu, Y., & Chiu, G. (2019, October). Modeling height profile for drop-on-demand print of uv curable ink. In Dynamic Systems and Control Conference (Vol. 59155, p. V002T13A006). American Society of Mechanical Engineers.

Wu, Y., & Chiu, G. (2021). An improved model of height profile for drop-on-demand print of ultraviolet curable ink. ASME Letters in Dynamic Systems and Control, 1(3), 031010.

Abedini, F. (2023). 2D and 3D Halftoning for Appearance Reproduction (Doctoral dissertation, Linköping University Electronic Press).

Tejani, Ankitkumar & Toshniwal, Vinay. (2023). Differential Energy Consumption Patterns of HVAC Systems in Residential and Commercial Structures: A Comparative Study. 1. 47-58. 10.56472/25839233/IJAST-V1I3P107.

Hu, W. C., Pillai, S. E. V. S., & ElSaid, A. A. (2022). Mobile Health Text Misinformation Identification Using Mobile Data Mining. International Journal of Mobile Devices, Wearable Technology, and Flexible Electronics (IJMDWTFE), 12(1), 114.

Pillai, S. E. V. S., & Hu, W. C. (2022). Using Dummy Locations to Conceal Whereabouts of Mobile Users in Location-Based Services. International Journal on Engineering, Science and Technology, 4(4), 406-418.

Pillai, S. E. V. S., ElSaid, A. A., & Hu, W. C. (2022, May). A Self-Reconfigurable System for Mobile Health Text Misinformation Detection. In 2022 IEEE International Conference on Electro Information Technology (eIT) (pp. 242-247). IEEE.

Pillai, S. E. V. S., & Hu, W. C. (2023). Mobile Text Misinformation Detection Using Effective Information Retrieval Methods. In Information Security and Privacy in Smart Devices: Tools, Methods, and Applications (pp. 234-256). IGI Global.

Pillai, S. E. V. S., & Hu, W. C. (2023, May). Misinformation detection using an ensemble method with emphasis on sentiment and emotional analyses. In 2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA) (pp. 295-300). IEEE.


Refbacks

  • There are currently no refbacks.