Temperature Analysis in Cubic Boron Nitrate Cutting Tool during Minimum Quantity Lubrication Turning with a Coconut-Oil-Based Nano-Cutting Fluid Using Computational Fluid Dynamics

润滑 椰子油 切削液 纳米- 材料科学 计算流体力学 流体力学 纳米流体 机械 冶金 机械工程 石油工程 复合材料 工程类 纳米技术 化学 机械加工 纳米颗粒 物理 食品科学 有机化学
作者
Subhash Khetre,Arunkumar Bongale,Satish Kumar,B. T. Ramesh
出处
期刊:Coatings [Multidisciplinary Digital Publishing Institute]
卷期号:14 (3): 340-340 被引量:3
标识
DOI:10.3390/coatings14030340
摘要

The minimum quantity lubrication (MQL) approach is used for improving tool life at a low cost, and it is environmentally friendly. When compared to traditional flood cooling technology, the flow rate in MQL is thought to be 10,000 times lower. The workpiece’s surface smoothness is enhanced by continuous chip formation during turning, but because the tool is always in touch with the chip, a crater wear zone is formed on the rake face due to high friction and thermal stress. While adding nanoparticles to MQL enhances cutting performance, a high concentration of these nanoparticles causes burr adhesion and decreased chip evacuation capability due to the agglomeration of nanoparticles, which affects the surface finish of the workpiece. A novel “coconut-oil-based SiC–MWCNT nano-cutting fluid for a CBN insert cutting tool” is proposed in this approach to overcome these issues. Silicon carbide (SiC) and multi-walled carbon nanotubes (MWCNTs) are added to coconut oil with an appropriate volume fraction for better lubrication. The thermal properties of the proposed nano-cutting fluid are compared with those of some existing nano MQL cutting fluids, and it was found that the MQL cutting fluid under consideration exhibits an elevated thermal conductivity and convective heat transfer coefficient that efficiently reduce tool temperature and improve tool life. The comparative study between the Finite Element Simulation using computational fluid dynamics (CFD) predicted variation in tool temperature and the corresponding experimental values revealed a remarkable alignment with a marginal error ranging from 1.27% to 3.44%.
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