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High-Speed Machining of Nickel-Based Alloy INVAR 36: Optimization of Machining Vibration, Tool, and Alloy Surface Characteristics Cover

High-Speed Machining of Nickel-Based Alloy INVAR 36: Optimization of Machining Vibration, Tool, and Alloy Surface Characteristics

Open Access
|Sep 2026

Abstract

INVAR 36 is considered a difficult to machine alloy because of its physical properties, but it has a low coefficient of thermal expansion and good dimensional stability that makes it suitable for aerospace, precision engineering, and tooling applications. Dry turning experiments have been done on INVAR 36 based on different cutting speeds (vc), feed rates (vf), and depth of cuts (ap) with respect to their influences on surface roughness (Ra), maximum flank wear (VBmax), and machining vibrations, Root Mean Square. Through Response Table Analysis, feed rate was found to be the highest influencing parameter for surface roughness, while cutting speed was the highest influencing parameter for tool wear and vibrations. Further, the vibration data have been studied through Fast Fourier Transform in frequency, domain with the dominant frequency being 105.6 Hz at optimal machining conditions. The optimization was carried out through multi-criteria decision-making by employing Grey Relational Analysis, Evaluation based on Distance from Average Solution, and Additive Ratio Assessment. It is seen that all three approaches gave the same optimum machining parameters, and hence the validity of the optimum machining parameters can be said to be reliable. The optimum combination of the parameters was found to be vc = 150 m/min, vf = 0.18 mm/rev, and ap = 1.2 mm, giving the minimum RMS vibration value of 14.246 g and flank wear of 0.110 mm.

DOI: https://doi.org/10.65731/ama/2026-0063 | Journal eISSN: 2300-5319 | Journal ISSN: 1898-4088
Language: English
Page range: 647 - 661
Submitted on: Apr 28, 2026
Accepted on: Jul 27, 2026
Published on: Sep 5, 2026
Published by: Bialystok University of Technology
In partnership with: Paradigm Publishing Services

© 2026 Sushmith Shetty M, Vikas Marakini, Grynal D’mello, Gururaj Bolar, published by Bialystok University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.