Machining Performance Investigation of AISI 304 Austenitic Stainless Steel under Different Turning Environments

Authors

  • Talwinder Singh Department of Mechanical Engineering, Punjabi University Patiala, India
  • J. S. Dureja Department of Mechanical Engineering, Punjabi University, Patiala 147002, Punjab, India
  • Manu Dogra Department of Mechanical Engineering, S S Giri, Panjab University Regional Center, Hoshiarpur, Punjab, India
  • Manpreet S. Bhatti Department of Botanical and Environmental Sciences, Guru Nanak Dev University, Amritsar, Punjab, India

DOI:

https://doi.org/10.15282/ijame.15.4.2018.10.0447

Keywords:

Turning; flank wear; nanofluid minimum quantity lubrication; MQL; surface roughness; dry machining

Abstract

Environment friendly machining calls for minimizing the use of cutting fluids to reduce their negative impact on environment and operator health. Present experimental work is aimed to investigate machining performance of AISI 304 austenitic stainless steel with PVD coated carbide tool under different turning environments viz. dry, flooded and nanofluid minimum quantity lubrication (NF-MQL). Optimum turning parameters obtained through desirability function optimisation are found as: cutting speed of 160.67 m/min, feed of 0.06 mm/rev and depth of cut of 0.25 mm with predicted tool flank wear of 100.001 μm and surface roughness of 0.509 μm at 0.808 desirability level. Confirmation tests show 3.22% and 3.41% error between predicted and experimental values of Vb and Ra, respectively. Present study has established the superiority of NF-MQL machining over dry and flooded machining. The most salient achievement of this investigation is the reduction of tool flank wear by 32.26% under NF-MQL machining compared to dry machining and 9.68% compared to flooded machining conditions. Similarly, NF-MQL exhibits improvement in surface finish by 34.72% and 7.59% over dry and flooded coolant environments respectively, thus providing a strong basis to replace flooded coolant machining for sustainable future.

Author Biographies

J. S. Dureja, Department of Mechanical Engineering, Punjabi University, Patiala 147002, Punjab, India

J.S. Dureja is working as Professor in the Department of Mechanical Engineering, Punjabi University Patiala. He obtained his doctoral degree in Mechanical Engineering from the Punjabi University Patiala, India. He is a life member of ISTE. His areas of interest are hard turning, tool wear, condition-based maintenance and monitoring apart from statistical modelling and optimisation, machining of aerospace alloys under minimum quantity lubrication machining/near dry machining, green manufacturing, etc.

Manu Dogra, Department of Mechanical Engineering, S S Giri, Panjab University Regional Center, Hoshiarpur, Punjab, India

Manu Dogra is working as an Associate Professor in Mechanical Engineering Department at UIET, PUSSGRC, Hoshiarpur, India. He received his PhD from Dr. B.R. Ambedkar National Institute of Technology (Deemed University – Government of India) Jalandhar, Punjab, India. He has contributed about 25 research papers in international journals. His area of interest includes machining, statistical modelling and welding.

Manpreet S. Bhatti, Department of Botanical and Environmental Sciences, Guru Nanak Dev University, Amritsar, Punjab, India

Manpreet S. Bhatti has 15 years research experience and working as an Associate Professor in the Department of Botanical and Environmental Sciences, Guru Nanak Dev University, Amritsar, India. He has published 16 international papers in reputed publishers like Elsevier (Journal of Hazardous Materials; Desalination), Royal Society of Chemistry (Soft Matter; RSC Advances), Springer (Polymer Bulletin), Taylor & Francis (Desalination & Water Treatment), and SAGE (Journal of Engineering Manufacture Part B). He received an Outstanding Reviewer Certificate from Desalination in 2010. He is presently handling one major research project and five PhD scholars are working under him. His special interests are design of experiments (DOE), statistical modelling, artificial neural network modelling, response surface methodology and process optimization.

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Published

2018-12-24

How to Cite

[1]
T. Singh, J. S. Dureja, M. Dogra, and M. S. Bhatti, “Machining Performance Investigation of AISI 304 Austenitic Stainless Steel under Different Turning Environments”, Int. J. Automot. Mech. Eng., vol. 15, no. 4, pp. 5837–5862, Dec. 2018.