Experimental analysis on Feed force reduction performance by Nanofluid of Graphene Nano platelets enriched Waste Coconut Oil in wet Machining of SAE 1045 Steel Shaft. (2022)

• Record Type: Journal Article
• Authors: Maran, M.; Saravanan, R.; Rajesh, S.; Raja, S.; Senthilkumar, R.; Nagarathi, D.K.
• Is Part Of: Materials today. Volume 62:Part 2(2022)
• Page Start: 1336
• Page End: 1341
• Publication Date: 2022
• Subjects: Waste coconut oil -- Nano fluid -- Graphene nano platelets -- SAE 1045 -- Feed force; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2022.04.750
• Languages: English
• ISSNs: 2214-7853

Abstract

Highlights: Waste oil is utilized as novel Nano fluid for machining purposes. Nano fluid used for lubricating purpose for machining the precision shaft. Force reduction reduces the heat generation at the cutting zone. Clean manufacturing reduced the feed force by 26.54% than green machining method. Abstract: The Waste utilization is one of the prime importance and possess good social impacts. Here the used coconut oil which disposal as waste at Kerala chips shop is utilized for preparing a novel Nano fluid for machining purposes. Such Nano fluid can be alternatively used for lubricating purpose for machining the precision shaft. This piece of research work prepared Nano fluid by concentrating Graphene Nanoplatelets with used coconut oil (waste). The synthesized nanofluid used as coolant in machining SAE1045 steel work material. The range of inputs were adapted from the conventional dry machining practices and manufacturer catalogue of the material. As planned to investigate the three key input variables at 4 levels from minimum to maximum in the selected range, the Taguchi L16 orthogonal array was preferred to design the experiments. The t -test employed for comparing the machining environment (a kind of input variable) performance through 16 different input combinations and found that the proposed intervention is outperformed significantly. So, the parameters for the proposed machining practice was optimized and input factors' contributions were analyzed by means of Taguchi analysis and ANOVA.