RESEARCH PAPER
High-Speed Machining of Nickel-Based Alloy INVAR 36: Optimization of Machining Vibration, Tool, and Alloy Surface Characteristics
1
Department of Mechanical Engineering, Nitte (Deemed to be University), NMAM Institute of Technology (NMAMIT), India
2
Department of Mechanical Engineering, College of Science and Engineering, Ritsumeikan University,, Japan
3
Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India, India
These authors had equal contribution to this work
Submission date: 2026-04-28
Final revision date: 2026-07-27
Acceptance date: 2026-07-27
Publication date: 2026-08-14
Corresponding author
Grynal D'MELLO
Department of Mechanical Engineering, Nitte (Deemed to be University), NMAM Institute of Technology (NMAMIT), Deralakatte, 574110, Mangaluru, India
Department of Mechanical Engineering, Nitte (Deemed to be University), NMAM Institute of Technology (NMAMIT), Deralakatte, 574110, Mangaluru, India
Acta Mechanica et Automatica 2026;20(3):647-661
HIGHLIGHTS
- 1.Optimising of cutting speed, feed rate and DOC in High speed maching of INVAR 36
- 2.Cutting speed is the dominant factor which controls the tool wear and vibration
- 3.Tool Wear and Cutting tool vibration exhibit strong positive corelation
- 4.MCDM techniques used: GRA, EDAS and ARAS
- 5.Optimal condition was identified to Vc=150 m/min, vf=0.18 mm/rev, ap=1.2 mm
KEYWORDS
Invar 36dry turningmachining vibrationroot mean square (RMS)tool wearfast Fourier transform (FFT)multi-criteria decision-making (MCDM)process optimization
TOPICS
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.
REFERENCES (52)
1.
Yonezawa T. Nickel alloys: Properties and characteristics. In: Comprehensive Nuclear Materials. Elsevier; 2012. p. 233–66.
2.
Shetty SM, Marakini V, Mello G, Pai SP, Dsouza EW, Dsouza LA, et al. Comparative Analysis of Surface Roughness and Tool Wear under Dry, MQL, and Cryogenic Machining Conditions in Invar 36 Turning Operations. Journal of Mines Metals and Fuels. 2025. Available from: https://doi.org/10.18311/jmmf/....
3.
Pedroso AFV, Sebbe NPV, Costa RDFS, Barbosa MLS, Sales-Contini RCM, Silva FJG, et al. INCONEL® alloy machining and Tool Wear Finite Element Analysis assessment: An extended review. J Manuf Mater Process [Internet]. 2024;8(1):37. Available from: http://dx.doi.org/10.3390/jmmp....
4.
Sousa VFC, Fernandes F, Silva FJG, Costa RDFS, Sebbe N, Sales-Contini RCM. Wear behavior phenomena of TiN/TiAlN HiPIMS PVD-coated tools on milling Inconel 718. Metals (Basel) [Internet]. 2023;13(4):684. Available from: http://dx.doi.org/10.3390/met1....
5.
Martinho RP, Silva FJG, Martins C, Lopes H. Comparative study of PVD and CVD cutting tools performance in milling of duplex stainless steel. Int J Adv Manuf Technol [Internet]. 2019;102(5–8):2423–39. Available from: http://dx.doi.org/10.1007/s001....
6.
Altintas Y. Manufacturing automation: Metal cutting mechanics, machine tool vibrations, and CNC design [Internet]. 2nd ed. Cambridge, England: Cambridge University Press; 2012. Available from: http://dx.doi.org/10.1017/cbo9....
7.
Huang G, He G, Gong X, He Y, Liu Y, Huang K. Additive manufacturing of Invar 36 alloy. J Mater Res Technol [Internet]. 2024;30:1241–68. Available from: http://dx.doi.org/10.1016/j.jm....
8.
Eissel A, Engelking L, Gustus R, Treutler K, Wesling V, Schroepfer D, et al. Alloy modification for additive manufactured Ni alloy components—part I: effect on microstructure and hardness of Invar alloy. Weld World [Internet]. 2023;67(4):1049–57.Available from: http://dx.doi.org/10.1007/s401....
9.
Khanna N, Mistry S, Rahman Rashid RA, Gupta MK. Investigations on density and surface roughness characteristics during selective laser sintering of Invar-36 alloy. Mater Res Express [Internet]. 2019;6(8):086541. Available from: http://dx.doi.org/10.1088/2053....
10.
Yakout M, Phillips I, Elbestawi MA, Fang Q. In-situ monitoring and detection of spatter agglomeration and delamination during laser-based powder bed fusion of Invar 36. Opt Laser Technol [Internet]. 2021;136(106741):106741. Available from: http://dx.doi.org/10.1016/j.op....
11.
Huang G, He G, Peng X, He Y, Chen H, Huang K, et al. Effect of processing parameters on the microstructure, mechanical properties and thermal expansion behavior of Invar 36 alloy manufactured by laser powder bed fusion. Mater Sci Eng A Struct Mater [Internet]. 2024;897(146329):146329. Available from: http://dx.doi.org/10.1016/j.ms....
12.
Nguyen N-T, Nguyen A-T, Nguyen T-S, Le N-T. Using VIKOR and RSM-DA in the optimization of dry turning of 9XC steel. Istraz Proj Za Privredu [Internet]. 2024;22(2):10–8. Available from: http://dx.doi.org/10.5937/jaes....
13.
Deppermann M, Puls H, Burghold M, Kneer R, Klocke F. Experimental investigation of the work piece temperatures in dry orthogonal metal turning. In: Proceedings of the 15th International Heat Transfer Conference. Connecticut: Begellhouse; 2014.
14.
Korpysa J, Kuczmaszewski J, Zagórski I. Surface quality of AZ91D magnesium alloy after precision milling with coated tools. Strojniski Vestn [Internet]. 2023;69(11–12):497–508. Available from: http://dx.doi.org/10.5545/sv-j....
15.
Ghazali MHM, Mazlan AZA, Wei LM, Tying CT, Sze TS, Jamil NIM. Effect of machining parameters on the surface roughness for different type of materials. IOP Conf Ser Mater Sci Eng [Internet]. 2019;530(1):012008. Available from: http://dx.doi.org/10.1088/1757....
16.
Evgeny B, Hughes T, Eskin D. Effect of surface roughness on corrosion behaviour of low carbon steel in inhibited 4 M hydrochloric acid under laminar and turbulent flow conditions. Corros Sci [Internet]. 2016;103:196–205.Available from: http://dx.doi.org/10.1016/j.co....
17.
Zhang J, Li R, Zhang L, Yang H, Tang H, Chen S. On the tool wear mechanism of machining Zr-based bulk metallic glasses under varying corner radii. J Non Cryst Solids [Internet]. 2024;624(122722):122722. Available from: http://dx.doi.org/10.1016/j.jn....
18.
Arslan H, Osman Er A, Orhan S, Aslan E. Tool condition monitoring in turning using statistical parameters of vibration signal. Int J Acoust Vib [Internet]. 2016;21(4). Available from: http://dx.doi.org/10.20855/ija....
19.
Serra R, Rmili W. Experimental evaluation of flank wear in dry turning from accelerometer data. Int J Acoust Vib [Internet]. 2016;21(1). Available from: http://dx.doi.org/10.20855/ija....
20.
Kim H, Lee S, Lee J, Park K-H, Nam S. Enhancing tool wear prediction accuracy by integrating multi-task learning with cutting force estimation. Int J Precis Eng Manuf-Green Technol [Internet]. 2025;12(3):969–89. Available from: http://dx.doi.org/10.1007/s406....
21.
Susac F, Stan F, Fetecau C, Besliu I. Prediction and optimization of surface roughness and cutting force in turning of UHMWPE by using Taguchi method, Response Surface Methodology and neural networks. In: Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability. American Society of Mechanical Engineers; 2020.
22.
Majumder H, Saha A. Application of MCDM based hybrid optimization tool during turning of ASTM A588. Decis Sci Lett [Internet]. 2018;143–56. Available from: http://dx.doi.org/10.5267/j.ds....
23.
Das PP, Chakraborty S. A comparative analysis on optimization of end milling processes using multi-criteria decision making methods. Int J Interact Des Manuf (IJIDeM) [Internet]. 2023;17(4):1611–32. Available from: http://dx.doi.org/10.1007/s120....
24.
Zhang P, Chen Z, Guo L, Li C, Lu S, Sun J. Machining of Invar 36 alloy: Chip-flow behaviors and formation mechanisms. J Manuf Process [Internet]. 2024;132:477–93. Available from: http://dx.doi.org/10.1016/j.jm....
25.
Khanna N, Gandhi A, Nakum B, Srivastava A. Optimization and analysis of surface roughness for INVAR-36 in end milling operations. Mater Today [Internet]. 2018;5(2):5281–8. Jovicic G, Milosevic A, Kanovic Z, Sokac M, Simunovic G, Savkovic B, et al. Optimization of dry turning of Inconel 601 alloy based on surface roughness, tool wear, and material removal rate. Metals (Basel) [Internet]. 2023;13(6):1068. Available from: http://dx.doi.org/10.3390/met1....
26.
Mahir A. A comparative study on the cutting performance of uncoated, AlTiN and TiCN-Al2 O3 coated carbide inserts in turning of Invar 36 alloy. Journal of Engineering Research and Applied Science. 2022;11(1):2045–61.
27.
Jovicic G, Milosevic A, Kanovic Z, Sokac M, Simunovic G, Savkovic B, et al. Optimization of dry turning of Inconel 601 alloy based on surface roughness, tool wear, and material removal rate. Metals (Basel) [Internet]. 2023;13(6):1068.Available from: http://dx.doi.org/10.3390/met1....
28.
Grynal, Mello NA, Pai PS, Puneet NP, Fang N. Surface roughness evaluation using cutting vibrations in high speed turning of Ti-6Al-4V - an experimental approach. Int J Mach Mach Mater [Internet]. 2016;18(3):288.Available from: http://dx.doi.org/10.1504/ijmm....
29.
Mastan Rao P, Deva Raj C, Dhoria SH, Vijaya M, Chowdary JRR. Multi-objective optimization of turning for nickel-based alloys using Taguchi-GRA and TOPSIS approaches. J Inst Eng (India) Ser D [Internet]. 2024;105(3):1473–84.Available from: http://dx.doi.org/10.1007/s400....
30.
Eskandari B, Davoodi B, Ghorbani H. Multi-objective optimization of parameters in turning of N-155 iron-nickel-base superalloy using gray relational analysis. J Braz Soc Mech Sci Eng [Internet]. 2018;40(4). Available from: http://dx.doi.org/10.1007/s404....
31.
Abbed K, Kribes N, Yallese MA, Chihaoui S, Boutabba S. Effects of tool materials and cutting conditions in turning of Ti-6Al-4V alloy: statistical analysis, modeling and optimization using CoCoSo, MABAC, ARAS and CODAS methods. Int J Adv Manuf Technol [Internet]. 2023;128(3–4):1535–57. Available from: http://dx.doi.org/10.1007/s001....
32.
Sivalingam V, Poogavanam G, Natarajan Y, Sun J. Optimization of atomized spray cutting fluid eco-friendly turning of Inconel 718 alloy using ARAS and CODAS methods. Int J Adv Manuf Technol [Internet]. 2022;120(7–8):4551–64. Available from: http://dx.doi.org/10.1007/s001....
33.
Tan H, Wang Y, Wang G, Zhang F, Fan W, Feng Z, et al. Investigation on microstructure and properties of laser solid formed low expansion Invar 36 alloy. J Mater Res Technol [Internet]. 2020;9(3):5827–39. Available from: http://dx.doi.org/10.1016/j.jm....
34.
Pan L, Wu ZR, Fang L, Song YD. Investigation of surface damage and roughness for nickel-based superalloy GH4169 under hard turning processing. Proc Inst Mech Eng B J Eng Manuf [Internet]. 2020;234(4):679–91.Available from: http://dx.doi.org/10.1177/0954....
35.
Veerappan G, Pritima D, Parthsarathy NR, Ramesh B, Jayasathyakawin S. Experimental investigation on machining behavior in dry turning of nickel based super alloy-Inconel 600 and analysis of surface integrity and tool wear in dry machining. Mater Today [Internet]. 2022;59:1566–70. Available from: http://dx.doi.org/10.1016/j.ma....
36.
Lakshmikanthan P, Prabu B. Optimization of CNC turning operations with multiple performance characteristics using Taguchi based grey relational analysis. Int J Veh Struct Syst [Internet]. 2017;9(4). Available from: http://dx.doi.org/10.4273/ijvs....
37.
Gökkaya H. The effects of machining parameters on cutting forces, surface roughness, Built-Up Edge (BUE) and Built-Up Layer (BUL) during machining AA2014 (T4) alloy. Strojniski Vestnik-journal of Mechanical Engineering. 2010;56(9):584–93.
38.
Tan L, Yao C, Li X, Fan Y, Cui M. Effects of machining parameters on surface integrity when turning inconel 718. J Mater Eng Perform [Internet]. 2022;31(5):4176–86. Available from: http://dx.doi.org/10.1007/s116....
39.
Marakini V, Pai SP, Bhat UK, Thakur D, Achar B. Surface integrity investigation and VIKOR optimisation during the milling of aluminium–lithium alloy using uncoated and PVD-coated carbide tools. Can Metall Q [Internet]. 2024;63(3):767–78. Available from: http://dx.doi.org/10.1080/0008....
40.
Zhou X, Liu H, Yu T, Guo R, Wang G, Sun Y, et al. Experimental and modeling study of surface topography generation considering tool-workpiece vibration in high-precision turning. Chin J Aeronaut [Internet]. 2023;36(7):194–212. Available from: http://dx.doi.org/10.1016/j.cj....
41.
Samin R, Nuawi MZ, Haris SM, Ghani JA. Stability analysis of regenerative vibration in turning operation using I-kaz3DTM signal processing approach. J Phys Conf Ser [Internet]. 2019;1262(1):012029. Available from: http://dx.doi.org/10.1088/1742....
42.
Vijayendra S, Marakini V, Pai K. R, Pai P. S, Bolar G. Cryogenic and conventional machining using CVD-coated inserts to improve the surface characteristics of low-carbon steel alloy E350. Cogent Eng [Internet]. 2025;12(1). Available from: http://dx.doi.org/10.1080/2331....
43.
Pedroso AFV, Sousa VFC, Sebbe NPV, Silva FJG, Campilho RDSG, Sales-Contini RCM, et al. A comprehensive review on the conventional and non-conventional machining and tool-wear mechanisms of INCONEL®. Metals (Basel) [Internet]. 2023;13(3):585. Available from: http://dx.doi.org/10.3390/met1....
44.
Tu L, Ming W, Xu X, Cai C, Chen J, An Q, et al. Wear and failure mechanisms of SiAlON ceramic tools during high-speed turning of nickel-based superalloys. Wear [Internet]. 2022;488–489(204171):204171. Available from: http://dx.doi.org/10.1016/j.we....
45.
Silva F, Martinho R, Martins C, Lopes H, Gouveia R. Machining GX2CrNiMoN26-7-4 DSS alloy: Wear analysis of TiAlN and TiCN/Al2O3/TiN coated carbide tools behavior in rough end milling operations. Coatings [Internet]. 2019;9(6):392. Available from: http://dx.doi.org/10.3390/coat....
46.
Hao Z, Cheng G, Fan Y, Li L, Kong L. Research on tool stick-slip erosion wear mechanism in cutting nickel-based alloy GH4169. Tribol Int [Internet]. 2023;188(108803):108803. Available from: http://dx.doi.org/10.1016/j.tr....
47.
Prasad BS, Babu MP. Correlation between vibration amplitude and tool wear in turning: Numerical and experimental analysis. Eng Sci Technol Int J [Internet]. 2017;20(1):197–211. Available from: http://dx.doi.org/10.1016/j.je....
49.
Quintana G, Ciurana J. Chatter in machining processes: A review. Int J Mach Tools Manuf [Internet]. 2011;51(5):363–76.Available from: http://dx.doi.org/10.1016/j.ij....
50.
Siddhpura M, Paurobally R. A review of chatter vibration research in turning. Int J Mach Tools Manuf [Internet]. 2012;61:27–47. Available from: http://dx.doi.org/10.1016/j.ij....
51.
Dimla DE Snr. Sensor signals for tool-wear monitoring in metal cutting operations—a review of methods. Int J Mach Tools Manuf [Internet]. 2000;40(8):1073–98. Available from: http://dx.doi.org/10.1016/s089....
52.
Xia C, Wu Y, Lu Q. Transversal vibration analysis of an axially moving string with unilateral constraints using the HHT method. Mech Syst Signal Process [Internet]. 2013;39(1–2):471–88. Available from: http://dx.doi.org/10.1016/j.ym....
| eISSN: | 2300-5319 |
| ISSN: | 1898-4088 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
