Version 1
: Received: 4 November 2024 / Approved: 5 November 2024 / Online: 6 November 2024 (12:26:41 CET)
How to cite:
Roy, R.; Islam, M.; Shazida, M. J.; Islam, M. A.; Hasan SK, A.; Toaha, M. S. I. Developed Sunflower Oil-Based Nano-cutting Fluid and Optimized Vertical Milling Process Parameters of Mild Steel Using Response Surface Methodology. Preprints2024, 2024110360. https://doi.org/10.20944/preprints202411.0360.v1
Roy, R.; Islam, M.; Shazida, M. J.; Islam, M. A.; Hasan SK, A.; Toaha, M. S. I. Developed Sunflower Oil-Based Nano-cutting Fluid and Optimized Vertical Milling Process Parameters of Mild Steel Using Response Surface Methodology. Preprints 2024, 2024110360. https://doi.org/10.20944/preprints202411.0360.v1
Roy, R.; Islam, M.; Shazida, M. J.; Islam, M. A.; Hasan SK, A.; Toaha, M. S. I. Developed Sunflower Oil-Based Nano-cutting Fluid and Optimized Vertical Milling Process Parameters of Mild Steel Using Response Surface Methodology. Preprints2024, 2024110360. https://doi.org/10.20944/preprints202411.0360.v1
APA Style
Roy, R., Islam, M., Shazida, M. J., Islam, M. A., Hasan SK, A., & Toaha, M. S. I. (2024). Developed Sunflower Oil-Based Nano-cutting Fluid and Optimized Vertical Milling Process Parameters of Mild Steel Using Response Surface Methodology. Preprints. https://doi.org/10.20944/preprints202411.0360.v1
Chicago/Turabian Style
Roy, R., Abul Hasan SK and Md. Sadrul Islam Toaha. 2024 "Developed Sunflower Oil-Based Nano-cutting Fluid and Optimized Vertical Milling Process Parameters of Mild Steel Using Response Surface Methodology" Preprints. https://doi.org/10.20944/preprints202411.0360.v1
Abstract
The article addresses the development and evaluation of a novel environmentally-friendly cutting fluid prepared using sunflower oil and ZnO nanoparticles, specifically developed for use in metal machining processes. An experimental investigation was conducted to examine the impact of three adjustable input process factors: spindle speed, feed rate, and depth of cut on Mild steel. The investigation was carried out under two conditions: one with a 1%(weight) ZnO nano cutting fluid and the other with a 1.5%(weight) ZnO nano cutting fluid. The study focused on two factors: surface roughness and material removal rate (MRR), which were measured during the process of face-milling mild steel. The research aims to optimize the milling machine settings to improve the efficiency of machining mild steel as the work material. This will be achieved by using Response Surface Methodology (RSM). The ZnO nano-cutting fluid was created using a meticulous technique and its stability was assessed by several investigations. An investigation indicates that a ZnO nano-cutting fluid with a weight concentration of 1.5% is more stable. The process was enhanced by the use of a newly created integrated approach grounded on response surface methodology (RSM). The suitability of the model was confirmed by an analysis of variance (ANOVA). The analysis revealed that, when considering equal weights of responses, the ideal values for the input parameters of spindle speed, feed rate, and depth of cut were determined to be 408.081 rpm, 146.667 m/min, and 0.4878 mm accordingly, under the circumstances of using 1.5% weight of ZnO nano cutting fluid. The projected output responses were as follows: 777.920 for the MRR (Material Removal Rate) and 3.5297 for surface roughness. The composite attractiveness was calculated to be 0.61433. The suggested model in this research shows a fitting of over 80% with our studied values. This model may be used to discover the ideal states and enhance the efficiency of the milling process. The enhanced milling parameters exhibit the potential to enhance machining performance, offering useful insights for firms seeking environmentally sustainable alternatives for metal-cutting operations. This work contributes to the advancement of sustainable machining practices by developing a novel formulation for cutting fluid and providing a systematic approach to improving machining parameters.
Engineering, Industrial and Manufacturing Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
