利用少量纳米颗粒富集植物油硬加工Inconel 690:一种可持续的方法

IF 2 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Jasgurpreet Singh Chohan, K. Suresh Babu, Ashutosh Pattanaik, J. Jayaprabakar, A. C. Umamaheshwer Rao, Harjot Singh Gill, Pragyan Senapati, Yashwant Singh Bisht, Lema Abate
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引用次数: 0

摘要

为了满足对可持续制造解决方案日益增长的需求,本研究侧重于开发旨在减少工具-工件界面摩擦的环保型润滑剂。该研究考察了植物油基纳米流体的性能,特别是使用大豆油作为基础流体,富集浓度为0%至1.4%的氧化铝和二氧化硅纳米颗粒。采用光谱表征确定最有效的纳米颗粒浓度。随后,在干式切削、压缩空气、含0.8%氧化铝纳米颗粒的大豆油和含0.8%二氧化硅纳米颗粒的大豆油四种润滑策略下对Inconel 690进行了硬加工实验。其中,氧化铝基纳米流体表现出优异的性能,相对于干式加工条件,其表面粗糙度降低43.70%,切削力降低22.70%,温度降低20.03%,刀具磨损降低45.65%。为了进一步优化工艺,在最佳润滑条件下进行了27次试验的田口实验设计。然后利用遗传算法对加工参数进行微调,实验验证表明,预测结果与实际结果具有较强的相关性,平均误差仅为3.04%。总的来说,研究结果强调了纳米颗粒增强生物润滑剂在提高加工性能、延长刀具寿命和支持环保制造实践方面的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Hard Machining of Inconel 690 Using Minimum Quantity Nanoparticle-Enriched Vegetable Oils: A Sustainable Approach

Hard Machining of Inconel 690 Using Minimum Quantity Nanoparticle-Enriched Vegetable Oils: A Sustainable Approach

In response to the growing demand for sustainable manufacturing solutions, this research focuses on developing environmentally friendly lubricants designed to reduce friction at the tool–workpiece interface. The study investigates the performance of vegetable oil-based nanofluids, specifically using soybean oil as the base fluid enriched with alumina and silica nanoparticles at concentrations ranging from 0% to 1.4%. Spectroscopic characterization was employed to determine the most effective nanoparticle concentration. Subsequently, hard machining experiments on Inconel 690 were carried out under four lubrication strategies: dry cutting, compressed air, soybean oil with 0.8% alumina nanoparticles, and soybean oil with 0.8% silica nanoparticles. Among these, the alumina-based nanofluid demonstrated superior performance, achieving reductions of 43.70% in surface roughness, 22.70% in cutting force, 20.03% in temperature, and 45.65% in tool wear relative to dry machining conditions. To further optimize the process, a Taguchi experimental design comprising 27 trials was applied under the optimal lubrication condition. A genetic algorithm was then used to fine-tune machining parameters, and experimental validation revealed a strong correlation between predicted and actual outcomes, with a mean error of only 3.04%. Overall, the findings highlight the effectiveness of nanoparticle-enhanced bio-lubricants in improving machining performance, extending tool life, and supporting environmentally responsible manufacturing practices.

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