This research investigates the machinability of Inconel 718 under conventional machining speeds using three different tool coatings in comparison with uncoated tool during milling operation. Cutting speed, feed rate and depth of cut were selected as variable machining parameters to analyze output responses including surface roughness, burr formation and tool wear. It was found that uncoated and AlTiN coated tools resulted in lower tool wear than nACo and TiSiN coated tools. On the other hand, TiSiN coated tools resulted in highest surface roughness and burr formation. Among the three machining parameters, feed was identified as the most influential parameter affecting burr formation. Grey relational analysis identified the most optimal experimental run with a speed of 14 m/min, feed of 1 μm/tooth, and depth of cut of 70 μm using an AlTiN coated tool. ANOVA of the regression model identified the tool coating parameter as most effective, with a contribution ratio of 41.64%, whereas cutting speed and depth of cut were found to have contribution ratios of 18.82% and 8.10%, respectively. Experimental run at response surface optimized conditions resulted in reduced surface roughness and tool wear by 18% and 20%, respectively.

Department of Machining Assembly and Engineering Metrology Mechanical Engineering Faculty VŠB Technical University of Ostrava 17 listopadu 2172 15 708 00 Ostrava Czech Republic

Department of Mechanical Engineering College of Electrical and Mechanical Engineering Islamabad 44000 Pakistan

Department of Mechanical Engineering Shaqra University Shaqra 11911 Saudi Arabia

School of Mechanical and Manufacturing Engineering Islamabad 44000 Pakistan

See more in PubMed

Hazzan K.E., Pacella M., See T.L. Laser processing of hard and ultra-hard materials for micro-machining and surface engineering applications. Micromachines. 2021;12:895. doi: 10.3390/mi12080895. PubMed DOI PMC

Wojciechowski S. Estimation of Minimum Uncut Chip Thickness during Precision and Micro-Machining Processes of Various Materials—A Critical Review. Materials. 2021;15:59. doi: 10.3390/ma15010059. PubMed DOI PMC

M’Saoubi R., Axinte D., Soo S.L., Nobel C., Attia H., Kappmeyer G., Engin S., Sim W.M. High performance cutting of advanced aerospace alloys and composite materials. CIRP Ann.-Manuf. Technol. 2015;64:557–580. doi: 10.1016/j.cirp.2015.05.002. DOI

Chen N., Li H.N., Wu J., Li Z., Li L., Liu G., He N. Advances in micro milling: From tool fabrication to process outcomes. Int. J. Mach. Tools Manuf. 2021;160:103670. doi: 10.1016/j.ijmachtools.2020.103670. DOI

Kladovasilakis N., Charalampous P., Tsongas K., Kostavelis I., Tzovaras D., Tzetzis D. Influence of Selective Laser Melting Additive Manufacturing Parameters in Inconel 718 Superalloy. Materials. 2022;15:1362. doi: 10.3390/ma15041362. PubMed DOI PMC

Approaches A. Drilling Force Characterization during Inconel 718 Drilling: A Comparative Study between Numerical and. Materials. 2021;14:4820. PubMed PMC

Bronis M., Miko E., Nowakowski L., Bartoszuk M. A Study of the Kinematics System in Drilling Inconel 718 for Improving of Hole Quality in the Aviation and Space Industries. Materials. 2022;15:5500. doi: 10.3390/ma15165500. PubMed DOI PMC

Kiswanto G., Azmi M., Mandala A., Ko T.J. The Effect of Machining Parameters to the Surface Roughness in Low Speed Machining Micro-milling Inconel 718. IOP Conf. Ser. Mater. Sci. Eng. 2019;654:012014. doi: 10.1088/1757-899X/654/1/012014. DOI

Schuster R., Kirchner V., Allongue P., Ertl G. Electrochemical micromachining. Science. 2000;289:98–101. doi: 10.1126/science.289.5476.98. PubMed DOI

Kock M., Kirchner V., Schuster R. Electrochemical micromachining with ultrashort voltage pulses-a versatile method with lithographical precision. Electrochim. Acta. 2003;48:3213–3219. doi: 10.1016/S0013-4686(03)00374-8. DOI

Attanasio A. Tool run-out measurement in micro milling. Micromachines. 2017;8:221. doi: 10.3390/mi8070221. PubMed DOI PMC

Liu Y., Zhu D., Zhu L. Micro electrochemical milling of complex structures by using in situ fabricated cylindrical electrode. Int. J. Adv. Manuf. Technol. 2012;60:977–984. doi: 10.1007/s00170-011-3682-y. DOI

Xu L., Zhao C. Nanometer-scale accuracy electrochemical micromachining with adjustable inductance. Electrochim. Acta. 2017;248:75–78. doi: 10.1016/j.electacta.2017.07.111. DOI

Ling S., Li M., Liu Y., Wang K., Jiang Y. Improving machining localization and surface roughness in wire electrochemical micromachining using a rotating ultrasonic helix electrode. Micromachines. 2020;11:698. doi: 10.3390/mi11070698. PubMed DOI PMC

Allegri G., Colpani A., Ginestra P.S., Attanasio A. An experimental study on micro-milling of a medical grade Co-Cr-Mo alloy produced by selective laser melting. Materials. 2019;12:2208. doi: 10.3390/ma12132208. PubMed DOI PMC

Wu M., Saxena K.K., Guo Z., Qian J., Reynaerts D. Fast fabrication of complex surficial micro-features using sequential lithography and jet electrochemical machining. Micromachines. 2020;11:948. doi: 10.3390/mi11100948. PubMed DOI PMC

Marrocco V., Modica F., Bellantone V., Medri V., Fassi I. Pulse-type influence on the micro-edm milling machinability of si3 n4–tin workpieces. Micromachines. 2020;11:932. doi: 10.3390/mi11100932. PubMed DOI PMC

Mian A.J., Driver N., Mativenga P.T. Identification of factors that dominate size effect in micro-machining. Int. J. Mach. Tools Manuf. 2011;51:383–394. doi: 10.1016/j.ijmachtools.2011.01.004. DOI

Bissacco G., Hansen H.N., de Chiffre L. Size effects on surface generation in micro milling of hardened tool steel. CIRP Ann.-Manuf. Technol. 2006;55:593–596. doi: 10.1016/S0007-8506(07)60490-9. DOI

Markopoulos A.P., Karkalos N.E., Mia M., Pimenov D.Y., Gupta M.K., Hegab H., Khanna N., Balogun V.A., Sharma S. Sustainability assessment, investigations, and modelling of slot milling characteristics in eco-benign machining of hardened steel. Metals. 2020;10:1650. doi: 10.3390/met10121650. DOI

Azhdari Tadavani S., Shoja Razavi R., Vafaei R. Pulsed laser-assisted machining of Inconel 718 superalloy. Opt. Laser Technol. 2017;87:72–78. doi: 10.1016/j.optlastec.2016.07.020. DOI

D’Addona D.M., Raykar S.J., Narke M.M. High Speed Machining of Inconel 718: Tool Wear and Surface Roughness Analysis. Procedia CIRP. 2017;62:269–274. doi: 10.1016/j.procir.2017.03.004. DOI

Ucun I., Aslantas K., Bedir F. An experimental investigation of the effect of coating material on tool wear in micro milling of Inconel 718 super alloy. Wear. 2013;300:8–19. doi: 10.1016/j.wear.2013.01.103. DOI

Bandhu D., Kumari S., Prajapati V., Saxena K.K., Abhishek K. Experimental investigation and optimization of RMDTM welding parameters for ASTM A387 grade 11 steel. Mater. Manuf. Process. 2020;36:1524–1534. doi: 10.1080/10426914.2020.1854472. DOI

Bandhu D., Abhishek K. Assessment of weld bead geometry in modified shortcircuiting gas metal arc welding process for low alloy steel. Mater. Manuf. Process. 2021;36:1384–1402. doi: 10.1080/10426914.2021.1906897. DOI

Chatterjee S., Mahapatra S.S., Behera A. Nickel-Titanium Smart Hybrid Mater. Elsevier; Amsterdam, The Netherlands: 2022. NiTi joining with other metallic materials; pp. 379–398. DOI

Lu X., Jia Z., Wang H., Si L., Wang X. Surface roughness prediction model of micro-milling Inconel 718 with consideration of tool wear. Int. J. Nanomanuf. 2016;12:93–108. doi: 10.1504/IJNM.2016.076161. DOI

Aslantas K., Hopa H.E., Percin M., Ucun I., Çicek A. Cutting performance of nano-crystalline diamond (NCD) coating in micro-milling of Ti6Al4V alloy. Precis. Eng. 2016;45:55–66. doi: 10.1016/j.precisioneng.2016.01.009. DOI

Özel T., Thepsonthi T., Ulutan D., Kaftanolu B. Experiments and finite element simulations on micro-milling of Ti-6Al-4V alloy with uncoated and cBN coated micro-tools. CIRP Ann.-Manuf. Technol. 2011;60:85–88. doi: 10.1016/j.cirp.2011.03.087. DOI

Aramcharoen A., Mativenga P.T., Yang S., Cooke K.E., Teer D.G. Evaluation and selection of hard coatings for micro milling of hardened tool steel. Int. J. Mach. Tools Manuf. 2008;48:1578–1584. doi: 10.1016/j.ijmachtools.2008.05.011. DOI

Devillez A., Le Coz G., Dominiak S., Dudzinski D. Dry machining of Inconel 718, workpiece surface integrity. J. Mater. Process. Technol. 2011;211:1590–1598. doi: 10.1016/j.jmatprotec.2011.04.011. DOI

Tansel I.N., Arkan T.T., Bao W.Y., Mahendrakar N., Shisler B., Smith D., McCool M. Tool wear estimation in micro-machining. Int. J. Mach. Tools Manuf. 2000;40:609–620. doi: 10.1016/S0890-6955(99)00074-7. DOI

Attanasio A., Gelfi M., Pola A., Ceretti E., Giardini C. Influence of material microstructures in micromilling of Ti6Al4V alloy. Materials. 2013;6:4268–4283. doi: 10.3390/ma6094268. PubMed DOI PMC

Sun Z., To S. Effect of machining parameters and toolwear on surface uniformity in micro-milling. Micromachines. 2018;9:268. doi: 10.3390/mi9060268. PubMed DOI PMC

Aurich J.C., Bohley M., Reichenbach I.G., Kirsch B. Surface quality in micro milling: Influences of spindle and cutting parameters. CIRP Ann.-Manuf. Technol. 2017;66:101–104. doi: 10.1016/j.cirp.2017.04.029. DOI

Tayade P.M., Sorte P. Multi Objective Optimization in Micro Milling: Literature Review. 2021. [(accessed on 4 November 2022)]. Available online: https://www.researchgate.net/profile/Madhukar-B-Sorte/publication/352169609_Multi_Objective_Optimization_in_Micro_Milling_Literature_Review/links/60bcd890299bf10dff9d8a29/Multi-Objective-Optimization-in-Micro-Milling-Literature-Review.pdf.

Joshi M., Ghadai R.K., Madhu S., Kalita K., Gao X.Z. Comparison of NSGA-II, MOALO and MODA for Multi-Objective Optimization of Micro-Machining Processes. Materials. 2021;14:5109. doi: 10.3390/ma14175109. PubMed DOI PMC

Tien D.H., Duc Q.T., Van T.N., Nguyen N.T., Do Duc T., Duy T.N. Online monitoring and multi-objective optimisation of technological parameters in high-speed milling process. Int. J. Adv. Manuf. Technol. 2021;112:2461–2483. doi: 10.1007/s00170-020-06444-x. DOI

Kumar R., Bilga P.S., Singh S. Multi objective optimization using different methods of assigning weights to energy consumption responses, surface roughness and material removal rate during rough turning operation. J. Clean. Prod. 2017;164:45–57. doi: 10.1016/j.jclepro.2017.06.077. DOI

Warsi S.S., Agha M.H., Ahmad R., Jaffery S.H.I., Khan M. Sustainable turning using multi-objective optimization: A study of Al 6061 T6 at high cutting speeds. Int. J. Adv. Manuf. Technol. 2019;100:843–855. doi: 10.1007/s00170-018-2759-2. DOI

Hughes J.I., Sharman A.R.C., Ridgway K. The effect of cutting tool material and edge geometry on tool life and workpiece surface integrity. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 2006;220:93–107. doi: 10.1243/095440506X78192. DOI

Barry J., Byrne G., Lennon D. Observations on chip formation and acoustic emission in machining Ti-6Al-4V alloy. Int. J. Mach. Tools Manuf. 2001;41:1055–1070. doi: 10.1016/S0890-6955(00)00096-1. DOI

Liu C.R., Mittal S. Single-step superfinish hard machining: Feasibility and feasible cutting conditions. Robot. Comput. Integr. Manuf. 1996;12:15–27. doi: 10.1016/0736-5845(95)00029-1. DOI

Zhou M., Chen Y., Zhang G. Force prediction and cutting-parameter optimization in micro-milling Al7075-T6 based on response surface method. Micromachines. 2020;11:766. doi: 10.3390/mi11080766. PubMed DOI PMC

Karna S.K. An Overview on Taguchi Method. [(accessed on 1 October 2022)];Int. J. Eng. Math. Sci. 2016 Available online: https://www.researchgate.net/publication/301356730_An_overview_on_Taguchi_method.

Alghamdi S.S., John S., Choudhury N.R., Dutta N.K. Additive manufacturing of polymer materials: Progress, promise and challenges. Polymers. 2021;13:753. doi: 10.3390/polym13050753. PubMed DOI PMC

Khan M.A., Jaffery S.H.I., Khan M., Younas M., Butt S.I., Ahmad R., Warsi S.S. Multi-objective optimization of turning titanium-based alloy Ti-6Al-4V under dry, wet, and cryogenic conditions using gray relational analysis (GRA) Int. J. Adv. Manuf. Technol. 2020;106:3897–3911. doi: 10.1007/s00170-019-04913-6. DOI

Khan M.A., Jaffery S.H.I., Khan M., Younas M., Butt S.I., Ahmad R., Warsi S.S. Statistical analysis of energy consumption, tool wear and surface roughness in machining of Titanium alloy (Ti-6Al-4V) under dry, wet and cryogenic conditions. Mech. Sci. 2019;10:561–573. doi: 10.5194/ms-10-561-2019. DOI

Komanduri R., Chandrasekaran N., Raff L.M. Some aspects of machining with negative-rake tools simulating grinding: A molecular dynamics simulation approach. Philos. Mag. B Phys. Condens. Matter Stat. Mech. Electron. Opt. Magn. Prop. 1999;79:955–968. doi: 10.1080/13642819908214852. DOI

Lu X., Jia Z., Wang H., Hu X., Li G., Si L. Measurement-based modelling of cutting forces in micro-milling of Inconel 718. Int. J. Nanomanuf. 2017;13:1–11. doi: 10.1504/IJNM.2017.082406. DOI

Platt T., Meijer A., Biermann D. Conduction-based thermally assisted micromilling process for cutting difficult-to-machine materials. J. Manuf. Mater. Process. 2020;4:34. doi: 10.3390/jmmp4020034. DOI

Jaffery S.H.I., Khan M., Ali L., Mativenga P.T. Statistical analysis of process parameters in micromachining of Ti-6Al-4V alloy. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 2016;230:1017–1034. doi: 10.1177/0954405414564409. DOI

Solheid J.S., Elkaseer A., Wunsch T., Scholz S., Seifert H.J., Pfleging W. Multiobjective Optimization of Laser Polishing of Additively Manufactured Ti-6Al-4V Parts for Minimum Surface Roughness and Heat-Affected Zone. Materials. 2022;15:3323. doi: 10.3390/ma15093323. PubMed DOI PMC

Singh H., Patrange P., Saxena P. Multi-Objective Optimization of the Process Parameters in Electric Discharge Machining of 316L Porous Stainless Steel Using Metaheuristic Techniques. Materials. 2022;15:6571. doi: 10.3390/ma15196571. PubMed DOI PMC

Algorithm A.B.C. Multiobjective Optimization of Heat-Treated Copper Tool Electrode on EMM Process Using Artificial Bee Colony (ABC) Algorithm. Materials. 2022;15:4831. PubMed PMC

Chen H., Lu C., Liu Z., Shen C., Sun M. Multi-Response Optimisation of Automotive Door Using Grey Relational Analysis with Entropy Weights. Materials. 2022;15:5339. doi: 10.3390/ma15155339. PubMed DOI PMC

Tan X., Deng J., Chen X. Generalized grey relational grade and grey relational order test; Proceedings of the IEEE International Conference on Systems, Man and Cybernetics; Montreal, QC, Canada. 7–10 October 2007; pp. 3928–3931. DOI

Bademlioglu A.H., Canbolat A.S., Kaynakli O. Multi-objective optimization of parameters affecting Organic Rankine Cycle performance characteristics with Taguchi-Grey Relational Analysis. Renew. Sustain. Energy Rev. 2020;117:109483. doi: 10.1016/j.rser.2019.109483. DOI

Ziegel E.R. Taguchi Techniques for Quality Engineering. Technometrics. 1997;39:109–110. doi: 10.1080/00401706.1997.10485460. DOI

Sustainability assessment of machining Al 6061-T6 using Taguchi-grey relation integrated approach