Optimization of the tool wear and surface roughness in the high-speed dry turning of Inconel 800

Abstract

Machining of Inconel 800 superalloy is associated with inherent issues like lower tool life and lower quality of machined surface owing to the work-hardening nature of the superalloy and increased mechanical and thermal stresses. The employment of cutting fluid in machining negatively affects the locale. Hence, dry machining is a feasible alternate solution. This work aims to optimize the cutting parameters (CP): cutting speed, feed rate and depth of cut in the high-speed dry turning of Inconel 800 employing an uncoated carbide insert to minimize the responses: tool wear (TW) and surface roughness (SR). The superalloy machining is conducted as per the experimental runs designed applying the Taguchi analysis. Then, the effects and contributions of the CP on the outputs were examined employing the signal-to-noise (S/N) ratio and the analysis of variance (ANOVA). Additionally, the multi-objective optimization (MOO) method grey relational analysis was employed to optimize CP. The results of the research work showed that cutting speed, feed rate, and depth of cut have noteworthy sway on TW and SR with a % contribution of 33.3, 13.8 and 23.7, respectively. Additionally, evaluation of SEM images of the cutting insert revealed that the abrasion, adhesion and diffusion are the primary wear mechanisms leading to abrasion groves, crater, chipping, built-up-edge and notch formation.

Keywords:

• Dry turning
• grey relational analysis
• Inconel 800
• flank wear
• surface roughness

REVIEWING EDITOR:

• D T Pham, University of Birmingham, United Kingdom

SUBJECTS:

• Aerospace Engineering
• Mechanical Engineering Design
• Materials Science
• Production Engineering
• Manufacturing Engineering
• Technology

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Ganesha Prasad

Ganesha Prasad serves as a Research Scholar in the Department of Mechanical and Industrial Engineering at MIT Manipal, Karnataka. He holds a B.E. in Mechanical Engineering and an M. Tech. in Machine Design, both from NMAM Institute of Technology Nitte, Karnataka. With three and a half years of teaching experience, he has contributed significantly to the academic field. Ganesha Prasad has an impressive record of publications in reputable journals and holds a patent. His research interests span machining science, dynamics of machine structures, machine learning, image processing, and deep learning.

G. S. Vijay

Dr. VIJAY G. S. is a Professor with the Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India. His research interests include bearing diagnostics, application of soft computing techniques to engineering and non-engineering domains, machine learning, applied numerical methods, meta-heuristic based multi-objective optimization, mechanical vibrations, finite element analysis, material science and metallurgy.

Raghavendra C. Kamath

Dr. Raghavendra Kamath C. is a professor in the department of Mechanical and Industrial Engineering, MIT Manipal, India. He has more than 20 years of teaching and research experience. He has published a patent on machine vision. He also co-authored a book titled “Data Analysis Theoretical concepts for non-IT engineers”. He also has received a grant for research proposal titled “Cryogenic machining of elastomers” in 2019. He has presented and published more than 50 papers in various national and international journals and conferences. His research areas include machine/deep learning, image processing, non-conventional machining, cryogenic machining, difficult to machine materials, composite materials, optimization techniques, Operations research, Simulation modeling and analysis, Modeling of machining processes.

Harshit Jairaj Hemmady

Harshit Jairaj Hemmady is an undergraduate student in thedepartment of Mechanical and Industrial Engineering, MIT Manipal, India. He was part of the present study.