Inconel 718 Turning Process Parameters Optimization with MQL Nanofluid Based on CuO Nanoparticles

Author:

Mane Pravin A.1ORCID,Kallol Anupama N.2,Doiphode Rajendra L.3,Manjunath G. A.2ORCID,Saleh Bahaa4,Abbas Mohamed56ORCID,Saleel C. Ahamed7ORCID,Alarifi Ibrahim M.8ORCID

Affiliation:

1. Gogte Institute of Technology, Belagavi, Karnataka, India

2. Department of Mechanical Engineering, Gogte Institute of Technology, Belagavi, India

3. Department of Mechanical Engineering, Government Polytechnic, Kolhapur, India

4. Mechanical Engineering Department, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia

5. Electrical Engineering Department, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia

6. Computers and Communications Department, College of Engineering, Delta University for Science and Technology, Gamasa 35712, Egypt

7. Department of Mechanical Engineering, College of Engineering, King Khalid University, PO Box 394, Abha 61421, Saudi Arabia

8. Department of Mechanical and Industrial Engineering, College of Engineering, Majmaah University, Al-Majmaah, Riyadh 11952, Saudi Arabia

Abstract

This study examined the effects of a minimum quantity lubrication (MQL) and a Cupric oxide- (CuO-) based nanofluid on Inconel 718 machinability. Additionally, by using an MQL CuO-based nanofluid during the turning process, Inconel 718’s tribological characteristics are optimised. The experimentation was done using the minimum quantity lubrication (MQL) method. With the aid of magnetic stirring and an ultrasonic bath process, CuO nanoparticles were dispersed in distilled water, sunflower oil, and soyabean oil to create nanofluid. Soyabean oil contains uniformly distributed CuO nanoparticles. All the experimental trials are designed based on the L18 Taguchi-based orthogonal arrays and performed on CNC turning under MQL and nanofluid environment. There are four input parameters that were selected at mixed level, namely, cutting speed, feed rate, weight % of CuO in the nanofluid, and flow rate to analyze surface roughness and tool wear. In addition to that, the response surface method was used to identify the optimum condition for better surface roughness and tool wear. Surface roughness and tool wear were measured using the surface roughness tester and toolmaker’s microscope, respectively. Experimental results observed that cutting speed and weight % highly affect surface roughness whereas cutting speed and flow rate affect tool wear. The predicted optimal values for lower surface roughness are 160 ml/hr flow rate, 92.99 m/min cutting speed, 3 weight % of CuO, and 0.1 mm/min feed rate and for low tool wear 80 ml/hr flow rate, 92.99 m/min cutting speed, 3 weight % of CuO, and 0.1 mm/min feed rate.

Funder

Taif University

Publisher

Hindawi Limited

Subject

General Materials Science

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