Model of atomized droplets average particle size and verification of eco-friendly hybrid lubrication (CAMQL) Article Swipe
YOU?
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· 2024
· Open Access
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· DOI: https://doi.org/10.26599/frict.2025.9440960
In the precision cutting of difficult-to-process metals, surface thermal damage to a workpiece is a significant technical challenge. Although clean minimum quantity lubrication (MQL) technology, which replaces traditional pouring cooling, is used, inadequate heat dissipation remains an issue. Cryogenic air MQL (CAMQL), an eco-friendly technology, can enhance the heat transfer performance of the lubricating film in the cutting zone, offering excellent cooling and lubrication effects. However, the influence of jet and temperature parameters on the average particle size and distribution characteristics of atomized droplets is not well understood. This study first analyzes the evolution of lubricant physical properties and establishes a quantitative mapping relationship between cryogenic air temperature and physical parameters of lubricant. Next, the unstable fluctuation in the annular liquid film at the two-phase flow nozzle exit is observed and analyzed. A thickness model of annular liquid film is developed, revealing the effect of airflow field on the annular liquid film. Finally, a model for the average particle size of atomized droplets under CAMQL is established. Numerical analysis and validation experiments under different working conditions show that the measured values align with the theoretical values. Under an air pressure of 0.4 MPa and an air flow temperature of −50 °C, the droplet particle size is 133.5 μm, with an error of 8.2%. The effect of air pressure on particle size is greater than that of air flow temperature. Additionally, the distribution spans of droplet size under different conditions are analyzed, and the results demonstrated that low temperatures help shorten the interval between particle sizes and improve the relative uniformity of particle size distribution. This research provides a theoretical basis for the application of CAMQL technology in the cutting process.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.26599/frict.2025.9440960
- https://sciopen.com/article_pdf/1807579973231919105.pdf
- OA Status
- diamond
- Cited By
- 5
- References
- 63
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405121185
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4405121185Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.26599/frict.2025.9440960Digital Object Identifier
- Title
-
Model of atomized droplets average particle size and verification of eco-friendly hybrid lubrication (CAMQL)Work title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-12-06Full publication date if available
- Authors
-
Mingzheng Liu, Changhe Li, Dongzhou Jia, Xinjun Liu, Yanbin Zhang, Min Yang, Xin Cui, Teng Gao, Yusuf Suleiman Dambatta, Runze LiList of authors in order
- Landing page
-
https://doi.org/10.26599/frict.2025.9440960Publisher landing page
- PDF URL
-
https://sciopen.com/article_pdf/1807579973231919105.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://sciopen.com/article_pdf/1807579973231919105.pdfDirect OA link when available
- Concepts
-
Lubrication, Materials science, Lubricant, Nozzle, Airflow, Mechanics, Particle size, Particle (ecology), Spray characteristics, Flow (mathematics), Thermal, Heat transfer, Mechanical engineering, Spray nozzle, Composite material, Thermodynamics, Chemical engineering, Engineering, Physics, Oceanography, GeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 5Per-year citation counts (last 5 years)
- References (count)
-
63Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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