Cutting Parameter Optimization in Finishing Milling of Ti-6Al-4V Titanium Alloy under MQL Condition using TOPSIS and ANOVA Analysis
Received: 29 December 2020 | Revised: 12 January 2021 | Accepted: 18 January 2021 | Online: 6 February 2021
Corresponding author: T. D. Nguyen
Abstract
Titanium and its alloys give immense specific strength, imparting properties such as corrosion and fracture resistance, making them the right candidate for medical and aerospace applications. There is a wide range of engineering applications that use titanium alloys in a variety of forms. The cost of these alloys is slightly higher in comparison to other variants due to the problematic extraction of the molten process. To reduce costs, titanium alloy products could be made by casting, isothermal forging, radial swaging, or powder metallurgy, although these techniques require some kind of finishing machining process. Titanium and its alloys are difficult to machine due to skinny chips leading to a small cutting tool-workpiece contact area. The thermal conductivity of titanium alloys is too low and the stress produced is too large due to the small contact area, which results in very high cutting temperatures. This paper deals with the experimental study of the influence of the Minimum Quantity Lubricant (MQL) environment in the milling of Ti-6Al-4V alloy considering the optimization of surface roughness and production rate. Taguchi-based TOPSIS and ANOVA were used to analyze the results. The experimental results show that MQL with vegetable oil is successfully applied in the milling of Ti-6Al-4V. The research confirms the suitability of TOPSIS in solving the Multiple Criteria Decision Making (MCDM) issue, by choosing the best alternative at Vc=120m/min, fz=0.065mm/tooth, and ap=0.2mm, where the surface roughness and material removal rate are 0.41µm and 44.1492cm3/min respectively. Besides, ANOVA can be used to predict the best parameters set in the milling process based on the regression model. The parameters predicted by ANOVA analysis do not coincide with any implemented parameters
Keywords:
surface milling, surface roughness, Taguchi-based TOPSIS, titalium alloy, Ti-6Al-4V, MCDM, MQLDownloads
References
M. A. Elfghi and M. Gunay, "Mechanical Properties of Powder Metallugry (Ti-6Al-4V) with Hot Isostatic Pressing," Engineering, Technology & Applied Science Research, vol. 10, no. 3, pp. 5637-5642, Jun. 2020. https://doi.org/10.48084/etasr.3522
M. Jamil et al., "Sustainable milling of Ti-6Al-4V: A trade-off between energy efficiency, carbon emissions and machining characteristics under MQL and cryogenic environment," Journal of Cleaner Production, vol. 281, Jan. 2021, Art. no. 125374. https://doi.org/10.1016/j.jclepro.2020.125374
D. Yang, Z. Liu, X. Xiao, and F. Xie, "The Effects of Machining-induced Surface Topography on Fatigue Performance of Titanium Alloy Ti-6Al-4V," Procedia CIRP, vol. 71, pp. 27-30, Jan. 2018. https://doi.org/10.1016/j.procir.2018.05.015
[4] M. I. Sadik and S. Isakson, "The role of PVD coating and coolant nature in wear development and tool performance in cryogenic and wet milling of Ti-6Al-4V," Wear, vol. 386-387, pp. 204-210, Sep. 2017. https://doi.org/10.1016/j.wear.2017.02.049
[5] A. Mamedov and I. Lazoglu, "Thermal analysis of micro milling titanium alloy Ti-6Al-4V," Journal of Materials Processing Technology, vol. 229, pp. 659-667, Mar. 2016. https://doi.org/10.1016/j.jmatprotec.2015.10.019
M. J. Bermingham, W. M. Sim, D. Kent, S. Gardiner, and M. S. Dargusch, "Tool life and wear mechanisms in laser assisted milling Ti-6Al-4V," Wear, vol. 322-323, pp. 151-163, Jan. 2015. https://doi.org/10.1016/j.wear.2014.11.001
N. E. Karkalos, N. I. Galanis, and A. P. Markopoulos, "Surface roughness prediction for the milling of Ti-6Al-4V ELI alloy with the use of statistical and soft computing techniques," Measurement, vol. 90, pp. 25-35, Aug. 2016. https://doi.org/10.1016/j.measurement.2016.04.039
A. Dadgari, D. Huo, and D. Swailes, "Investigation on tool wear and tool life prediction in micro-milling of Ti-6Al-4V," Nanotechnology and Precision Engineering, vol. 1, no. 4, pp. 218-225, Dec. 2018. https://doi.org/10.1016/j.npe.2018.12.005
A. Shokrani, V. Dhokia, and S. T. Newman, "Investigation of the effects of cryogenic machining on surface integrity in CNC end milling of Ti-6Al-4V titanium alloy," Journal of Manufacturing Processes, vol. 21, pp. 172-179, Jan. 2016. https://doi.org/10.1016/j.jmapro.2015.12.002
M. I. Sadik, S. Isakson, A. Malakizadi, and L. Nyborg, "Influence of Coolant Flow Rate on Tool Life and Wear Development in Cryogenic and Wet Milling of Ti-6Al-4V," Procedia CIRP, vol. 46, pp. 91-94, Jan. 2016. https://doi.org/10.1016/j.procir.2016.02.014
N. R. Dhar, M. Kamruzzaman, and M. Ahmed, "Effect of minimum quantity lubrication (MQL) on tool wear and surface roughness in turning AISI-4340 steel," Journal of Materials Processing Technology, vol. 172, no. 2, pp. 299-304, Feb. 2006. https://doi.org/10.1016/j.jmatprotec.2005.09.022
K. N. Ronoha, N. W. Karuri, F. M. Mwema, H. T. Ngethac, S. A. Akinlabi, and E. T. Akinlabi, "Evaluation of the Surface Roughness of Ti-6Al-4V for Surface Grinding under Different Cooling Methods Using Conventional and Vegetable Oil-based Cutting Fluids," Tribology in Industry, vol. 41, no. 4, pp. 634-647, Sep. 2019. https://doi.org/10.24874/ti.2019.41.04.15
N. Benmoussa, A. Elyamami, K. Mansouri, M. Qbadou, and E. Illoussamen, "A Multi-Criteria Decision Making Approach for Enhancing University Accreditation Process," Engineering, Technology & Applied Science Research, vol. 9, no. 1, pp. 3726-3733, Feb. 2019. https://doi.org/10.48084/etasr.2352
A. K. Parida and B. C. Routara, "Multiresponse Optimization of Process Parameters in Turning of GFRP Using TOPSIS Method," International Scholarly Research Notices, vol. 2014, 2014, Art. no. 905828. https://doi.org/10.1155/2014/905828
F. Jafarian and H. Samarikhalaj, "Experimental Investigation and Optimizing Geometrical Characteristics and Surface Quality in Drilling of AISI H13 Steel," Journal of Applied and Computational Mechanics, vol. 6, no. 2, pp. 332-343, Apr. 2020.
G. Maheedhara Reddy, V. Diwakar Reddy, B. Satheesh Kumar, and J. Shyamsunder, "Experimental Investigation on Radial Ball Bearing Parameters Using Taguchi Method," Journal of Applied and Computational Mechanics, vol. 4, no. 1, pp. 69-74, Jan. 2018. https://doi.org/10.1080/01430750.2019.1611635
M. Hassanzadeh and S. E. Moussavi Torshizi, "Multi-objective optimization of shot-peening parameters using design of experiments and finite element simulation: a statistical model," Journal of Applied and Computational Mechanics, May 2020.
K. Krishnaprasad, C. S. Sumesh, and A. Ramesh, "Numerical Modeling and Multi Objective Optimization of Face Milling of AISI 304 Steel," Journal of Applied and Computational Mechanics, vol. 5, no. 4, pp. 749-762, Jun. 2019.
H. Zheng, D. Si, W. Wang, and R. Wang, "Quantitative Entropy Weight TOPSIS Evaluation of Sustainable Chinese Wind Power Developments," Mathematical Problems in Engineering, vol. 2018, Sep. 2018, Art. no. 6965439. https://doi.org/10.1155/2018/6965439
J. Huang, "Combining entropy weight and TOPSIS method for information system selection," in IEEE Conference on Cybernetics and Intelligent Systems, Chengdu, China, Sep. 2008, pp. 1281-1284.
R. V. Rao, Decision Making in the Manufacturing Environment: Using Graph Theory and Fuzzy Multiple Attribute Decision Making Methods. London, UK: Springer, 2007.
J.-C. Liu, "Stationarity for a Markov-switching Box-Cox transformed threshold GARCH process," Statistics & Probability Letters, vol. 77, no. 13, pp. 1428-1438, Jul. 2007. https://doi.org/10.1016/j.spl.2007.02.009
S. Y. Hwang and I. V. Basawa, "Stationarity and moment structure for Box-Cox transformed threshold GARCH(1,1) processes," Statistics & Probability Letters, vol. 68, no. 3, pp. 209-220, Jul. 2004. https://doi.org/10.1016/j.spl.2003.08.016
B. Singaravel, D. P. Shankar, and L. Prasanna, "Application of MCDM Method for the Selection of Optimum Process Parameters in Turning Process," Materials Today: Proceedings, vol. 5, no. 5, Part 2, pp. 13464-13471, Jan. 2018. https://doi.org/10.1016/j.matpr.2018.02.341
R. V. Rao, Decision Making in the Manufacturing Environment: Using Graph Theory and Fuzzy Multiple Attribute Decision Making Methods. London, UK: Springer, 2007.
G.-H. Tzeng and J.-J. Huang, Fuzzy Multiple Objective Decision Making, 1st edition. Florida, United States: CRC Press, 2013.
J. Xu and X. Zhou, Fuzzy-Like Multiple Objective Decision Making, 2011th edition. Berlin, Germany: Springer, 2011. https://doi.org/10.1007/978-3-642-16895-6
G.-H. Tzeng and J.-J. Huang, Multiple Attribute Decision Making: Methods and Applications. Florida, USA: CRC Press, 2011. https://doi.org/10.1201/b11032
Downloads
How to Cite
License
Copyright (c) 2021 Authors
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.