Application of statistical and soft computational techniques in machining of Nickel based supper-alloy using cryogenically treated tools for estimation of surface roughness

ABSTRACT

In this paper, surface roughness prediction models are developed for turning of Inconel 718 using untreated and cryogenically treated inserts by using Dimensional Analysis, Response Surface Methodology (RSM) and Artificial Neural Network (ANN). Performance of untreated and treated tools is analysed using SEM, Energy-dispersive X-ray analysis, Vicker hardness test and electrical conductivity. For the established surface roughness models by dimensional analysis, RSM and ANN, the mean absolute errors for confirmation tests are 5.32%, 8.28% and 4.15% for untreated inserts and 4.95%, 6.01% and 4.20% for treated inserts, respectively. The effect of cutting parameters on surface roughness is analysed using the main effect plot and 3D surface plots. Based on correlation coefficient (R²) values, ANN modelling technique (R² = 99.68%) is more accurate for predicting surface roughness. Thus, it can be an effective tool for analysing machining responses. The study also noted that while cutting at v= 60 m/min, f= 0.1 mm/rev and d= 0.5 mm, surface roughness and flank wear values are 0.5 µm and 0.45 µm and 0.777 mm and 0.627 mm for untreated and treated inserts, respectively. The use of treated tools resulted in 10% and 19% improvement in surface quality and tool life than the untreated tools.

Graphical abstract

KEYWORDS:

• Inconel 718
• cryogenic treatment
• surface roughness
• Buckingham pi theorem of dimensional analysis
• response surface methodology
• artificial neural network

Acknowledgments

This research work was carried out within the scheme of Technical Education Quality Improvement Program, phase II (TEQIP-II) financially supported with the assistance of World Bank under Ministry of Human Resource Development (MHRD), Government of India, New Delhi.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Highlights

• Modelling techniques such as dimensional analysis, response surface methodology (RSM) and artificial neural network (ANN) used for estimation of surface quality in turning Inconel 718.

• Performance of untreated and cryogenically treated tools are analysed..

• The response models are formulated and effect of cutting parameters on surface roughness is also analysed using the main effect and 3D surface plots.

• ANN modelling technique is found to be more accurate for the prediction of surface roughness in comparison with dimensional analysis and RSM.

• Improvement in surface quality and tool life using the cryogenically treated tool.

Additional information

Notes on contributors

Yogesh V. Deshpande

Dr. Yogesh V. Deshpande is Assistant professor in the Department of Industrial Engineering, Shri Ramdeobaba College of Engineering and Management, Nagpur, India. He has done PhD in Mechanical Engineering from Visvesvaraya National Institute of Technology, Nagpur, India. His research area is machining science technology and completed research in cryogenic machining of Nickel based supper-alloy.

Atul B. Andhare

Dr. Atul B. Andhare is Associate professor in the Department of Mechanical Engineering, Visvesvaraya National Institute of Technology, Nagpur, India. He has done PhD in Mechanical Engineering from IIT, Bombay. His research area is condition monitoring, vibration analysis, machining science technology, nano-machining of titanium alloy and cryogenic machining of nickel alloy.

Pramod M. Padole

Dr. Pramod M. Padole, Professor in the Department of Mechanical Engineering, Visvesvaraya National Institute of Technology, Nagpur, India. He has taken charge as the Director of Visvesvaraya National Institute of Technology, Nagpur on 28th June, 2018. Prof. Padole is an erudite professor, popular teacher and eminent researcher with a dream to use science and technology for better community life. Prof. Padole is also an alumnus of VNIT, Nagpur. He did his BE (Mech.) & PhD from VNIT and Master’s in Machine Design from VJTI, Bombay. Mechanisms and Machine Design, finite element method and Bio mechanical engineering are his research areas.