Skip to main content

Advertisement

SpringerLink
Log in

Multi-objective optimization of turning titanium-based alloy Ti-6Al-4V under dry, wet, and cryogenic conditions using gray relational analysis (GRA)

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

In modern manufacturing industries, the importance of multi-objective optimization cannot be overemphasized particularly when the desired responses are differing in nature towards each other. With the emergence of new technologies, the need to achieve overall efficiency in terms of energy, output, and tooling is on the rise. Resultantly, endeavor is to make the machining process sustainable, productive, and efficient simultaneously. In this research, the effects of machining parameters (feed, cutting speed, depth of cut, and cutting condition including dry, wet, and cryogenic) were analyzed. Since sustainable production demands a balance between production quality and energy consumption, therefore, response parameters including specific cutting energy, tool wear, surface roughness, and material removal rate were considered. Taguchi-gray integrated approach was adopted in this study. Multi-objective function was developed using gray relational methodology, and its regression analysis was conducted. Response surface optimization was carried out to optimize the formulated multi-objective function and derive the optimum machining parameters. Concurrent responses were optimized with best-suited values of input parameters to make the most out of the machining process. Analysis of variance results showed that feed is the most effective parameter followed by cutting condition in terms of overall contribution in multi-objective function. The proposed optimum parameters resulted in improvement of tool wear and surface roughness by 30% and 22%, respectively, whereas specific cutting energy was reduced by 4%.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Abbreviations

AHP :

Analytic hierarchy process

MRR :

Material removal rate (cm3/s)

ANOVA :

Analysis of Variance

R :

Wear rate

D :

Workpiece diameter (mm)

Ra :

Surface roughness (μm)

d :

Depth of cut (mm)

RSM :

Response surface methodology

f :

Feed (mm/rev)

SCE :

Specific cutting energy

GRA :

Gray relational analysis

t :

Cutting time

GRC :

Gray relational coefficient

TOPSIS :

Technique for order of preference by similarity to ideal solution

GRG :

Gray relational grade

V :

Cutting speed (m/min)

l s :

Spiral length of cut (mm)

VB :

Flank wear (mm)

MOO :

Multi-objective optimization

References

  1. Yi S, Jang Y-C, An AK (2018) Potential for energy recovery and greenhouse gas reduction through waste-to-energy technologies. J Clean Prod 176:503–511

    Google Scholar 

  2. Zhou L, Li J, Li F, Meng Q, Li J, Xu X (2016) Energy consumption model and energy efficiency of machine tools: a comprehensive literature review. J Clean Prod 112:3721–3734

    Google Scholar 

  3. Kara S, Li W (2011) Unit process energy consumption models for material removal processes. CIRP Ann 60:37–40

    Google Scholar 

  4. Birol F (2017) Key world energy statistics. International Energy Agency, Paris

    Google Scholar 

  5. Zhao G, Hou C, Qiao J, Cheng X (2016) Energy consumption characteristics evaluation method in turning. Adv Mech Eng 8:1–8

    Google Scholar 

  6. Tonshoff H, Wobker H, Brandt D (1996) Tool wear and surface integrity in hard turning. Prod Eng 3:19–24

    Google Scholar 

  7. Hughes J, Sharman A, Ridgway K (2006) "the effect of cutting tool material and edge geometry on tool life and workpiece surface integrity," Proceedings of the Institution of Mechanical Engineers. Part B: J Eng Manuf 220:93–107

    Google Scholar 

  8. López de lacalle LN, Pérez J, Llorente JI, Sánchez JA (2000) Advanced cutting conditions for the milling of aeronautical alloys. J Mater Process Technol 100:1–11

    Google Scholar 

  9. Barry J, Byrne G, Lennon D (2001) Observations on chip formation and acoustic emission in machining Ti–6Al–4V alloy. Int J Mach Tools Manuf 41:1055–1070

    Google Scholar 

  10. Hong SY, Markus I, Jeong W-c (2001) New cooling approach and tool life improvement in cryogenic machining of titanium alloy Ti-6Al-4V. Int J Mach Tools Manuf 41:2245–2260

    Google Scholar 

  11. Sivaiah P, Chakradhar D (2018) Effect of cryogenic coolant on turning performance characteristics during machining of 17-4 PH stainless steel: a comparison with MQL, wet, dry machining. CIRP J Manuf Sci Technol 21:86–96

    Google Scholar 

  12. Sun Y, Huang B, Puleo DA, Jawahir IS (2015) Enhanced machinability of Ti-5553 alloy from cryogenic machining: comparison with MQL and flood-cooled machining and modeling. Procedia CIRP 31:477–482

    Google Scholar 

  13. Kaynak Y, Lu T, Jawahir IS (2014) Cryogenic machining-induced surface integrity: a review and comparison with dry, MQL, and flood-cooled machining. Mach Sci Technol 18:149–198

    Google Scholar 

  14. Senevirathne SWMAI, Punchihewa HKG (2017) Comparison of tool life and surface roughness with MQL, flood cooling, and dry cutting conditions with P20 and D2 steel. IOP Conference Series: Materials Science and Engineering 244:012006

    Google Scholar 

  15. Khatri A, Jahan MP (2018) Investigating tool wear mechanisms in machining of Ti-6Al-4V in flood coolant, dry and MQL conditions. Procedia Manufacturing 26:434–445

    Google Scholar 

  16. Aramcharoen A (2016) Influence of cryogenic cooling on tool wear and chip formation in turning of titanium alloy. Procedia CIRP 46:83–86

    Google Scholar 

  17. Bagherzadeh A, Budak E (2018) Investigation of machinability in turning of difficult-to-cut materials using a new cryogenic cooling approach. Tribol Int 119:510–520

    Google Scholar 

  18. Bermingham MJ, Kirsch J, Sun S, Palanisamy S, Dargusch MS (2011) New observations on tool life, cutting forces and chip morphology in cryogenic machining Ti-6Al-4V. Int J Mach Tools Manuf 51:500–511

    Google Scholar 

  19. Bordin A, Bruschi S, Ghiotti A, Bariani PF (2015) Analysis of tool wear in cryogenic machining of additive manufactured Ti6Al4V alloy. Wear 328-329:89–99

    Google Scholar 

  20. Hong SY, Ding Y, Ekkens RG (1999) Improving low carbon steel chip breakability by cryogenic chip cooling. Int J Mach Tools Manuf 39:1065–1085

    Google Scholar 

  21. Lu T, Kudaravalli R, Georgiou G (2018) Cryogenic machining through the spindle and tool for improved machining process performance and sustainability: Pt. I, system design. Procedia Manuf 21:266–272

    Google Scholar 

  22. Zhao Z, Hong S (1992) Cooling strategies for cryogenic machining from a materials viewpoint. J Mater Eng Perform 1:669–678

    Google Scholar 

  23. Shokrani A, Dhokia V, Newman ST (2012) Environmentally conscious machining of difficult-to-machine materials with regard to cutting fluids. Int J Mach Tools Manuf 57:83–101

    Google Scholar 

  24. Sreejith P, Ngoi B (2000) Dry machining: machining of the future. J Mater Process Technol 101:287–291

    Google Scholar 

  25. Bhushan RK (2013) Optimization of cutting parameters for minimizing power consumption and maximizing tool life during machining of Al alloy SiC particle composites. J Clean Prod 39:242–254

    Google Scholar 

  26. Camposeco-Negrete C (2015) Optimization of cutting parameters using response surface method for minimizing energy consumption and maximizing cutting quality in turning of AISI 6061 T6 aluminum. J Clean Prod 91:109–117

    Google Scholar 

  27. Kumar R, Bilga PS, Singh S (2017) 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 164:45–57

    Google Scholar 

  28. Warsi SS, Agha MH, Ahmad R, Jaffery SHI, Khan M (2018) Sustainable turning using multi-objective optimization: a study of Al 6061 T6 at high cutting speeds. Int J Adv Manuf Technol 100:843–855

    Google Scholar 

  29. Gupta MK, Sood PK, Singh G, Sharma VS (2017) Sustainable machining of aerospace material–Ti (grade-2) alloy: modeling and optimization. J Clean Prod 147:614–627

    Google Scholar 

  30. Mia M, Khan MA, Rahman SS, Dhar NR (April 01 2017) Mono-objective and multi-objective optimization of performance parameters in high pressure coolant assisted turning of Ti-6Al-4V. Int J Adv Manuf Technol 90:109–118

    Google Scholar 

  31. Venkata Ramana M, Rao GKM, Rao DH (2018) Multi Objective optimization of Process Parameters in Turning of Ti-6Al-4V alloy. Materials Today: Proceedings 5:18966–18974

    Google Scholar 

  32. Uçak N, Çiçek A (2018) The effects of cutting conditions on cutting temperature and hole quality in drilling of Inconel 718 using solid carbide drills. J Manuf Process 31:662–673

    Google Scholar 

  33. Sarıkaya M, Güllü A (2015) Multi-response optimization of minimum quantity lubrication parameters using Taguchi-based grey relational analysis in turning of difficult-to-cut alloy Haynes 25. J Clean Prod 91:347–357

    Google Scholar 

  34. Behera BC, Alemayehu H, Ghosh S, Rao PV (2017) A comparative study of recent lubri-coolant strategies for turning of Ni-based superalloy. J Manuf Process 30:541–552

    Google Scholar 

  35. Mia M, Gupta MK, Lozano JA, Carou D, Pimenov DY, Królczyk G et al (2019) Multi-objective optimization and life cycle assessment of eco-friendly cryogenic N2 assisted turning of Ti-6Al-4V. J Clean Prod 210:121–133

    Google Scholar 

  36. Younas M, Jaffery SHI, Khan M, Ali Khan M, Ahmad R, Mubashar A et al (2019) Multi-objective optimization for sustainable turning Ti6Al4V alloy using grey relational analysis (GRA) based on analytic hierarchy process (AHP). In: The International Journal of Advanced Manufacturing Technology

    Google Scholar 

  37. ASTM (ed) (2015) B265–15, "Standard specification for titanium and titanium alloy strip, sheet and plate," ed. ASTM International, West Conshohocken, PA

    Google Scholar 

  38. Zhao Z, Hong S (1992) Cryogenic properties of some cutting tool materials. J Mater Eng Perform 1:705–714

    Google Scholar 

  39. Jawahir I, Attia H, Biermann D, Duflou J, Klocke F, Meyer D et al (2016) Cryogenic manufacturing processes. CIRP Ann 65:713–736

    Google Scholar 

  40. Hong SY (2006) Lubrication mechanisms of LN2 in ecological cryogenic machining. Mach Sci Technol 10:133–155

    Google Scholar 

  41. Bermingham MJ, Palanisamy S, Kent D, Dargusch MS (2012) A comparison of cryogenic and high pressure emulsion cooling technologies on tool life and chip morphology in Ti–6Al–4V cutting. J Mater Process Technol 212:752–765

    Google Scholar 

  42. Khan MA, Jaffery SHI, Khan M, Younas M, Butt SI, Ahmad R et al (2019) 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 10:561–573

    Google Scholar 

  43. Younas M, Jaffery SHI, Khan M, Ahmad R, Ali L, Khan Z et al (2019) Tool Wear progression and its effect on energy consumption in turning of titanium alloy (Ti-6Al-4V). Mech Sci 10:373–382

    Google Scholar 

  44. ISO Standard (1993) "ISO-3685," Tool-life Testing with Single Point Turning Tools

  45. Sandvik (2015) Sandvik launches CBN tool for hard-part turning. Metal Powder Report 70:312–313

    Google Scholar 

  46. Li W, Kara S (2011) "An empirical model for predicting energy consumption of manufacturing processes: a case of turning process," Proceedings of the Institution of Mechanical Engineers. Part B: J Eng Manuf 225:1636–1646

    Google Scholar 

  47. Warsi SS, Jaffery SHI, Ahmad R, Khan M, Ali L, Agha MH et al (2017) "development of energy consumption map for orthogonal machining of Al 6061-T6 alloy," Proceedings of the Institution of Mechanical Engineers. Part B: J Eng Manuf 232:2510–2522

    Google Scholar 

  48. Taguchi G, Yokoyama Y (1993) Taguchi methods: design of experiments. Amer Supplier Inst 4

  49. Ross PJ, Ross PJ (1988) Taguchi techniques for quality engineering: loss function, orthogonal experiments, parameter and tolerance design. McGraw-hill, New York

    Google Scholar 

  50. Ziegel ER (1997) Taguchi techniques for quality engineering. Taylor & Francis Group

  51. Black J, Kohser R (2008) Materials & Processes in Manufacturing. John Wiley&Sons," ed: Inc

  52. Ravi Kumar SM, Kulkarni SK (2017) Analysis of hard machining of titanium alloy by Taguchi method. Materials Today: Proceedings 4:10729–10738

    Google Scholar 

  53. Davis R, Singh V, Priyanka S (2014) Optimization of process parameters of turning operation of EN 24 steel using Taguchi Design of Experiment Method. In: Proceedings of the World Congress on Engineering

    Google Scholar 

  54. Sarwar M, Persson M, Hellbergh H, Haider J (2009) Measurement of specific cutting energy for evaluating the efficiency of bandsawing different workpiece materials. Int J Mach Tools Manuf 49:958–965

    Google Scholar 

  55. Mia M, Dhar NR (2016) Prediction of surface roughness in hard turning under high pressure coolant using artificial neural network. Measurement 92:464–474

    Google Scholar 

  56. Yan J, Li L (2013) Multi-objective optimization of milling parameters–the trade-offs between energy, production rate and cutting quality. J Clean Prod 52:462–471

    Google Scholar 

  57. Groover MP (2007) Fundamentals of modern manufacturing: materials processes, and systems. John Wiley & Sons, New york

    Google Scholar 

  58. Schulz H, Moriwaki T (1992) High-speed machining. CIRP Ann 41:637–643

    Google Scholar 

  59. Fan Y, Hao Z, Zheng M, Yang S (2016) Wear characteristics of cemented carbide tool in dry-machining Ti-6Al-4V. Mach Sci Technol 20:249–261

    Google Scholar 

  60. Shaw MC (2005) Metal cutting principles. Oxford university press, New York

    Google Scholar 

  61. Mia M, Dhar NR (2017) Optimization of surface roughness and cutting temperature in high-pressure coolant-assisted hard turning using Taguchi method. Int J Adv Manuf Technol 88:739–753

    Google Scholar 

  62. Jaffery SI, Mativenga PT (2008) Assessment of the machinability of Ti-6Al-4V alloy using the wear map approach. Int J Adv Manuf Technol 40:687–696

    Google Scholar 

  63. Schulz H, Arnold W, Scherer J (1981) High speed machining: new technology or slogan? Werkstatt und Betrieb 114:527–531

    Google Scholar 

  64. Komanduri R, Subramanian K, von Turkovich B (1984) High speed machining: presented at the winter annual meeting of the American Society of Mechanical Engineers. New Orleans, Louisiana

    Google Scholar 

  65. Ali Khan M, Husain Imran S (2019) Jaffery, M. Khan, and S. Ikramullah butt, "Wear and surface roughness analysis of machining of Ti-6Al-4V under dry, wet and cryogenic conditions," IOP Conference Series. Mater Sci Eng 689:012006

    Google Scholar 

  66. Dhar N, Kamruzzaman M (2007) Cutting temperature, tool wear, surface roughness and dimensional deviation in turning AISI-4037 steel under cryogenic condition. Int J Mach Tools Manuf 47:754–759

    Google Scholar 

  67. Venugopal KA, Paul S, Chattopadhyay AB (2007) Growth of tool wear in turning of Ti-6Al-4V alloy under cryogenic cooling. Wear 262:1071–1078

    Google Scholar 

  68. Strano M, Chiappini E, Tirelli S, Albertelli P, Monno M (2013) "comparison of Ti6Al4V machining forces and tool life for cryogenic versus conventional cooling," Proceedings of the Institution of Mechanical Engineers. Part B: J Eng Manuf 227:1403–1408

    Google Scholar 

  69. Chichili D, Ramesh K, Hemker K (1998) The high-strain-rate response of alpha-titanium: experiments, deformation mechanisms and modeling. Acta Mater 46:1025–1043

    Google Scholar 

  70. Julong D (1989) Introduction to grey system theory. The Journal of grey system 1:1–24

    MathSciNet  MATH  Google Scholar 

  71. Julong D (1982) Control problems of grey systems. Sys & Contr Lett 1:288–294

    MathSciNet  Google Scholar 

  72. Wang T (1985) Grey functions and relational grade. Fuzzy Mathematics (Special Issue of Grey Systems) 2:59–66

    Google Scholar 

  73. Wojciechowski S, Maruda RW, Krolczyk GM, Niesłony P (2018) Application of signal to noise ratio and grey relational analysis to minimize forces and vibrations during precise ball end milling. Precis Eng 51:582–596

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology (NUST), Sector H-12, Islamabad, 44000, Pakistan

    Muhammad Ali Khan, Syed Husain Imran Jaffery, Mushtaq Khan, Muhammad Younas, Shahid Ikramullah Butt & Riaz Ahmad

  2. Capital University of Science and Technology (CUST), Islamabad, Pakistan

    Salman Sagheer Warsi

Authors
  1. Muhammad Ali Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Syed Husain Imran Jaffery
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mushtaq Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Muhammad Younas
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shahid Ikramullah Butt
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Riaz Ahmad
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Salman Sagheer Warsi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Syed Husain Imran Jaffery.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khan, M.A., Jaffery, S.H.I., Khan, M. et al. 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 106, 3897–3911 (2020). https://doi.org/10.1007/s00170-019-04913-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-019-04913-6

Keywords

Search

Navigation