Multi-Response Optimization of Thermal Conductivity and Rheological Behavior in Nanoparticle-Enhanced Vegetable Oil Emulsions
In metal cutting industries, optimizing the thermal conductivity and viscosity of vegetable oil-based cutting fluids is critical for ensuring efficient heat dissipation, effective lubrication, and sustainability, directly influencing tool life and machining performance. This study presents a comprehensive experimental analysis employing statistical methods, particularly Taguchi’s Design of Experiments, to evaluate the thermal conductivity and viscosity of Pongamia pinnata, sunflower, and coconut oil incorporated with Silicon Dioxide (SiO2), Hexagonal Boron Nitride (hBN), and Cupric Oxide (CuO) nanoparticles across different emulsion ratios and nanoparticle volume fractions. The results revealed that Pongamia pinnata oil containing 0.5 (Vol.%) SiO2 nanoparticles at an emulsion ratio of 1:7 achieved the maximum thermal conductivity, measured at 0.637 W/mK. Additionally, the results revealed that Pongamia pinnata oil at an emulsion ratio of 1:13 exhibited the highest viscosity of 1.33 mPa·S, confirming that both the type of cutting oil and the emulsion ratio are the primary factors influencing viscosity. Further, the ANOVA analysis for thermal conductivity and viscosity highlights that the type of cutting fluid is the dominant factor, accounting for 90.58% of the total variance in thermal conductivity and 70.47% in viscosity, each with a highly significant p-value of 0.00, underscoring its decisive impact on the stability of both properties. Overall, this research offers important guidance for the selection and formulation of vegetable oil-based emulsions with nanoparticle additives. The results support the development of advanced nano lubricants with enhanced performance, catering to the increasing requirements of diverse industrial applications.
Authors
- Srinivas Shenoy Heckadka (ORCID: https://orcid.org/0000-0003-0271-9062)
- Adithya Hegde (ORCID: https://orcid.org/0000-0003-2930-9602)
- J P Supriya
- Vishal Shenoy P (ORCID: https://orcid.org/0000-0002-2616-7744)
- Raviraj Shetty
- Vijay Kini M
- Raghuvir Pai B
Institutions
- Manipal Academy of Higher Education (IN)
- Centre for Artificial Intelligence and Robotics (IN)
Publication Details
- Journal
- Journal of Composites Science
- Published
- 2026-01-25
- DOI
- https://doi.org/10.3390/jcs10020063
- Primary Topic
- Lubricants and Their Additives
- Type
- article
- Field-Weighted Citation Impact
- 0.00