Copper nanofluids under minimum quantity lubrication during drilling of AISI 4140 steel

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ABSTRACT

A significant quantity of power consumed in drilling process is converted into heat energy due to friction between the surfaces of tool and work piece. This extreme heat generated affects the performance of cutting tool which, leads to poor surface finish on drilled surfaces. The cutting fluid plays a vital function in improving the tool life and surface finish since it acts as a coolant and lubrication. In this research, the experiments have been carried out with difference combination of three operating parameters such as cutting speed, machining environment and feed rate using minimal quantity lubrication technique in drilling of AISI 4140 steel. The conventional coolant (CC) in flood lubrication condition, coconut oil and copper nanofluid are used as the levels of machining environments. The surface roughness and flank wear are observed as the responses and analysed using an integrated technique Average S/N ratio-based response surface methodology (ASN-RSM). The experimental results of copper nanofluid are compared with CC and coconut oil. The surface roughness, tool wear and chip morphology are also evaluated under conventional coolant, coconut oil and nanofluid. The confirmation tests are executed with the near optimal setting obtained from ASN-RSM technique and results are compared with the initial setting. It was observed that copper nanofluid-based optimal setting predicted using ASN-RSM has reduced the surface roughness and flank wear by 71% and 53% respectively over the responses obtained at coconut oil based initial setting. It is confirmed that copper nanofluid has a significant contribution in reducing surface roughness and flank wear.

KEYWORDS:

• Surface roughness
• tool wear
• response surface method
• orthogonal array
• minimum quantity lubrication
• drilling

Additional information

Notes on contributors

Santhanakumar Muthuvel

M.Santhanakumar has completed his bachelors degree in Mechanical Engineering at Dr.Sivanthi Aditanar College of Engineering, Tiruchendur, Anna University, Tamilnadu, India in the year 2005 and Master degree in Industrial Engineering, College of Engineering Guindy, Anna University, Chennai, Tamilnadu, India in 2008. He has completed his Doctoral Degree in the Department of Industrial Engineering, College of Engineering Guindy, Anna University, Chennai, Tamilnadu, India in 2018. He has 10 years of teaching/research experience in handling core Mechanical and Industrial Engineering subjects, and published more than 32 research articles in reputed international journals; 20 article in various conferences and filed a patent on passenger's safety of four wheeler . He has received “Corps of Engineers” award from Institution of Engineers (India) for the year 2016. He is expertise the areas such as Abrasive Machining Processes, Multi Response Optimisation and Operations Research. He is a Life Member of IEI, IAENG and ISAME.

M. Naresh Babu

M. Naresh Babu is working as a professor with Saveetha Engineering College since 2006. He received his MTech in Production Engineering from the MS University, Tirunelveli in 2002. He received his PhD in Mechanical Engineering from Anna University Chennai, India in 2015. He has 13 years of experience in teaching. His research interests include unconventional machining process, optimisation and minimum quantity lubrication.

N. Muthukrishnan

N. Muthukrishnan is currently working as a professor in the Department of Mechanical Engineering at the Sri Venkateswara College of Engineering, Irungattukottai. In1986, he received his BE in Production Engineering from Anna University, Chennai. In 1993, he completed his MS in Technological Operations from BITS, Pilani, Rajasthan. He received his PhD in Mechanical Engineering from the JNT University; Hyderabad in 2009. He is acting as a reviewer for Elsevier, Springer, Inderscience, Taylor and Francis publications journals, etc. His research fields include metal cutting,composite machining, optimising of machinability parameters, application of artificial neural network and refractory metals.