Skip to main content
SpringerLink
Log in

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

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

Abstract

In light of its exceptional physical and mechanical properties, Ti-6Al-4V may be considered one of the more difficult titanium alloys to machine and is known for damaging cutting tools due to its aggressiveness. The objective of this present research is to assess the impact of the cutting conditions (CS, Doc, and FR), coated (TiN and TiAlN), and uncoated cutting carbides on the response parameters during the dry machining of Ti-6Al-4V alloy. First, a series of parametric tests were carried out in order to compare the performances of the three used cutting tools in terms of wear, roughness, cutting forces, and power consumption. Subsequently, a second test campaign was conducted following a Taguchi L9 design (4*3) to measure the impact of the input parameters on the output technological ones (Ra, Fz, Vb, MRR, and Pc) using the ANOVA technique. The results obtained through the application of the statistical treatment based on the RSM methodology led to the obtaining of predictive mathematical models for each of the cutting tools for different outputs. A single-objective optimization of the input parameters was carried out by applying Taguchi’s approach based on signal-to-noise ratio. Finally, a comparative multi-objective optimization was conducted and discussed among the four MCDM methods (CoCoSo, ARAS, MABAC, and CODAS) based on the S/N ratio. The numerous well-founded results obtained are of interest to mechanical manufacturing companies and academic researchers working on the performance of optimization methods.

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
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Data availability

This is not available.

Abbreviations

F:

Resultant cutting force

TiN:

Titanium nitride

TiAlN:

Titanium aluminium nitride

RE:

Roundness error

T°:

Cutting temperature

tc:

Cutting time

MCDM:

Multi-criteria decision-making

ARAS:

Additive ratio assessment

CoCoSo:

Combined compromise solution

MABAC:

Multi-attributive border approximation area comparison

CODAS:

Combinative distance-based assessment

TL:

Tool life (min)

References

  1. Patil S, Joshi S, Tewari A, Joshi SS (2014) Modelling and simulation of effect of ultrasonic vibrations on machining of Ti6Al4V. Ultrasonics 54(2):694–705

    Article  Google Scholar 

  2. Xin S, Zhang J, Mao X, Zhao Y, Hong Q Research and development of low-cost titanium alloys. In : Journal of Physics: Conference Series. IOP Publishing, 2019. 012022.

  3. Velásquez JP, Tidu A, Bolle B, Chevrier P, Fundenberger JJ (2010) Sub-surface and surface analysis of high speed machined Ti–6Al–4V alloy. Mater Sci Eng: A 527(10-11):2572–2578

    Article  Google Scholar 

  4. Menezes J, Rubeo MA, Kiran K, Honeycutt A, Schmitz TL (2016) Productivity progression with tool wear in titanium milling. Procedia Manuf 5:427–441

    Article  Google Scholar 

  5. Ulutan D, Ozel T (2011) Machining induced surface integrity in titanium and nickel alloys: a review. Int J Mach Tools Manuf 51(3):250–280

    Article  Google Scholar 

  6. Barelli, Floran. Développement d’une méthodologie d'optimisation des conditions d’usinage: application au fraisage de l'alliage de titane TA6V. 2016. Thèse de doctorat. 2016

    Google Scholar 

  7. Umasekar VG, Gopal M, Rahul K, Saikiran S, Mowli GS (2006) Investigation of surface roughness in finish turning of titanium alloy Ti-6Al-4V. Carbon 100:0–29

    Google Scholar 

  8. Patil S, Jadhav S, Kekade S, Supare A, Powar A, Singh RK (2016) The influence of cutting heat on the surface integrity during machining of titanium alloy Ti6Al4V. Procedia Manuf 5:857–869

    Article  Google Scholar 

  9. Vijay S, Krishnaraj V (2013) Machining parameters optimization in end milling of Ti-6Al-4 V. Procedia Eng 64:1079–1088

    Article  Google Scholar 

  10. Kumar R, Sahoo A, Satyanarayana K, Rao G (2013) Some studies on cutting force and temperature in machining Ti-6Al-4V alloy using regression analysis and ANOVA. Int J Ind Eng Comput 4(3):427–436

    Google Scholar 

  11. Muthuswamy P, Murugesan VGV (2021) Machinability analysis in high speed turning of Ti–6Al–4V alloy and investigation of wear mechanism in AlTiN PVD coated tungsten carbide tool. Eng Res Express 3(4):045011

    Article  Google Scholar 

  12. Liang X, Liu Z (2018) Tool wear behaviors and corresponding machined surface topography during high-speed machining of Ti-6Al-4V with fine grain tools. Tribol Int 121:321–332

    Article  Google Scholar 

  13. Guan XL, Melchers RE (2001) Effect of response surface parameter variation on structural reliability estimates. Struct Saf 23(4):429–444

    Article  Google Scholar 

  14. Youn BD, Choi KK (2004) A new response surface methodology for reliability-based design optimization. Comput Struct 82(2-3):241–256

    Article  Google Scholar 

  15. Aouici H, Yallese MA, Fnides B, Chaoui K, Mabrouki T (2011) Modeling and optimization of hard turning of X38CrMoV5-1 steel with CBN tool: machining parameters effects on flank wear and surface roughness. J Mech Sci Technol 25:2843–2851

    Article  Google Scholar 

  16. Campatelli G, Lorenzini L, Scippa A (2014) Optimization of process parameters using a response surface method for minimizing power consumption in the milling of carbon steel. J Clean Prod 66:309–316

    Article  Google Scholar 

  17. Neşeli S, Yaldız S, Türkeş E (2011) Optimization of tool geometry parameters for turning operations based on the response surface methodology. Measurement 44(3):580–587

    Article  Google Scholar 

  18. Rahman AM, Rob SMA, Srivastava AK (2021) Modeling and optimization of process parameters in face milling of Ti6Al4V alloy using Taguchi and grey relational analysis. Procedia Manuf 53:204–212

    Article  Google Scholar 

  19. Saini A, Chauhan P, Pabla BS, Dhami SS (2018) Multi-process parameter optimization in face milling of Ti6Al4V alloy using response surface methodology. Proc Inst Mech Eng, Part B: Journal of Engineering Manufacture 232(9):1590–1602

    Article  Google Scholar 

  20. Tunacan T, Torkul O (2021) The impact of information sharing on different performance indicators in a multi-level supply chain. Tehnički vjesnik 28(6):1960–1974

    Google Scholar 

  21. Yazdani M, Zarate P, Kazimieras Zavadskas E, Turskis Z (2019) A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems. Manag Decis 57(9):2501–2519

    Article  Google Scholar 

  22. Ulutaş A, Popovic G, Radanov P, Stanujkic D, Karabasevic D (2021) A new hybrid fuzzy PSI-PIPRECIA-CoCoSo MCDM based approach to solving the transportation company selection problem. Technol Econ Dev Econ 27(5):1227–1249

    Article  Google Scholar 

  23. Jahan F, Soni M, Parveen A, Waseem M (2022) Application of combined compromise solution method for material selection. In: In : Advancement in materials, manufacturing and energy engineering, Vol. I: Select Proceedings of ICAMME 2021. Springer, Singapore, pp 379–387

    Google Scholar 

  24. Do DT, Nguyen NT (2022) Applying Cocoso, Mabac, Mairca, Eamr, Topsis and weight determination methods for multi-criteria decision making in hole turning process. Strojnícky časopis-J Mech Eng 72(2):15–40

    Article  Google Scholar 

  25. Sapkota G, Das S, Sharma A, Ghadai RK (2022) Comparison of various multi-criteria decision methods for the selection of quality hole produced by ultrasonic machining process. Mater Today: Proceedings 58:702–708

    Article  Google Scholar 

  26. Paramasivam SS, Kumaran D, Natarajan H, Krishnan GS, Sairaghav SE (2021) Process parameter optimization of key machining parameters of mg alloy with cryogenic treated tools by MABAC approach. Mater Today: Proceedings 47:7149–7154

    Article  Google Scholar 

  27. Singaravel B, Shankar DP, Prasanna L (2018) Application of MCDM method for the selection of optimum process parameters in turning process. Mater Today: Proceedings 5(5):13464–13471

    Article  Google Scholar 

  28. Goswami SS, Behera DK (2021) Solving material handling equipment selection problems in an industry with the help of entropy integrated COPRAS and ARAS MCDM techniques. Process Integr Optim Sustain 5(4):947–973

    Article  Google Scholar 

  29. Khanna R, Sharma N, Kumar N, Gupta RD, Sharma A (2022) WEDM of Al/SiC/Ti composite: a hybrid approach of RSM-ARAS-TLBO algorithm. Int J Lightweight mater Manuf 5(3):315–325

    Google Scholar 

  30. Motorcu AR, Ekici E (2022) Evaluation and multi-criteria optimization of surface roughness, deviation from dimensional accuracy and roundness error in drilling CFRP/Ti6Al4 stacks. FME Trans 50(3):441–460

    Article  Google Scholar 

  31. Sivalingam V, Poogavanam G, Natarajan Y, Sun J (2022) Optimization of atomized spray cutting fluid eco-friendly turning of Inconel 718 alloy using ARAS and CODAS methods. Int J Adv Manuf Technol 120(7-8):4551–4564

    Article  Google Scholar 

  32. Chakraborty SEZ, Kazimieras E (2014) Applications of WASPAS method in manufacturing decision making. Informatica 25(1):1–20

    Article  Google Scholar 

  33. Keshavarz Ghorabaee M, Zavadskas EK, Turskis Z, Antucheviciene J (2016) A new combinative distance-based assessment (CODAS) method for multi-criteria decision-making. Econ Comput Econ Cybern 50:3

    Google Scholar 

  34. Hagag AM, Yousef LS, Abdelmaguid TF (2023) Multi-criteria decision-making for machine selection in manufacturing and construction: recent trends. Mathematics 11(3):631

    Article  Google Scholar 

  35. Ghorabaee MK, Amiri M, Zavadskas EK, Hooshmand R, Antuchevičienė J (2017) Fuzzy extension of the CODAS method for multi-criteria market segment evaluation. J Bus Econ Manag 18(1):1–19

    Article  Google Scholar 

  36. Goswami SS, Jena S, Behera DK (2021) Implementation of CODAS MCDM method for the selection of suitable cutting fluid. In: In : 2021 International Conference on Simulation, Automation & Smart Manufacturing (SASM). IEEE, pp 1–6

    Google Scholar 

  37. Fellah M, Labaïz M, Assala O, Dekhil L, Taleb A, Rezag H, Iost A (2014) Tribological behavior of Ti-6Al-4V and Ti-6Al-7Nb alloys for total hip prosthesis. Adv Tribol 21:2014

    Google Scholar 

  38. Andriya N, Venkateswara Rao P, Ghosh S, Engineering M, Delhi T, Delhi N (2012) Machining Study of TI-6AL-4V Using PVD Coated TiAlN Inserts. Asian Rev Mech Eng 1(2):34

    Google Scholar 

  39. Semenova IP, Saitova LR, Raab GI, Korshunov A, Zhu YT, Lowe TC, Valiev R. Microstructural features and mechanical properties of the Ti-6Al-4V ELI alloy processed by severe plastic deformation. In : Materials Science Forum. Trans Tech Publications Ltd, 2006. 757-762.

  40. Boyer RR, Briggs RD (2005) The use of β titanium alloys in the aerospace industry. J Mater Eng Perform 14:681–685

    Article  Google Scholar 

  41. Sandvik Coromant - outils et solutions pour l’usinage. Sandvik Coromant [online]. [vid. 2022-11-28]. Dostupné z: https://www.sandvik.coromant.com/fr-fr

  42. Facchini L, Magalini E, Robotti P, Molinari A (2009) Microstructure and mechanical properties of Ti-6Al-4V produced by electron beam melting of pre-alloyed powders. Rapid Prototyping Journal

    Book  Google Scholar 

  43. Nouari M, Calamaz M, Girot F (2008) Mécanismes d’usure des outils coupants en usinage à sec de l’alliage de titane aéronautique Ti–6Al–4V. Comptes Rendus Mécanique 336(10):772–781

    Article  Google Scholar 

  44. Cellier A (2013) Etude du fraisage de l’alliage de titane Ti-6AI-4V: influence des angles de coupe et des rayons de bec sur l’intégrité de surface et la limite d’endurance des pièces. Thèse de doctorat. Tours

    Google Scholar 

  45. Yallese MA, Bouchelaghem H, Belhadi S, Kribes N (2007) Investigation expérimentale sur l’usure des outils de coupe en CBN lors du tournage des pièces dures. Sciences & Technologie. B, Sciences de l'ingénieur, pp 15–22

    Google Scholar 

  46. Selvaraj DP, Chandramohan P, Mohanraj M (2014) Optimization of surface roughness, cutting force and tool wear of nitrogen alloyed duplex stainless steel in a dry turning process using Taguchi method. Measurement 49:205–215

    Article  Google Scholar 

  47. de Oca-Valero AJM (2002. Thèse de doctorat) Elaboration du carbure et du nitrure de titane par ds procédés chimiques et physiques en phase vapeur : caractérisation de la microstructure. Université Sciences et Technologies-Bordeaux I

    Google Scholar 

  48. Younas M, Jaffery SHI, Khan A, Khan M (2021) Development and analysis of tool wear and energy consumption maps for turning of titanium alloy (Ti6Al4V). J Manuf Process 62:613–622

    Article  Google Scholar 

  49. Yallese M, Boulanouar L, Belhadi S (2003) Etude de l’endommagement des outils de coupe en céramique noire et en CBN lors du tournage d’un acier durci. Revue de 1(5):323–339

    Google Scholar 

  50. Bouzid L (2015) Optimisation des conditions de coupe et analyse de leur effet sur les paramètres technologiques d’usinage-Application à l’usinage de l’acier inoxydable X20Cr13. Thèse de doctorat

    Google Scholar 

  51. Uddin GM, Joyia FM, Ghufran M, Khan SA, Raza MA, Faisal M, Arafat SM, Zubair SW, Jawad M, Zafar MQ, Irfan M (2021) Comparative performance analysis of cemented carbide, TiN, TiAlN, and PCD coated inserts in dry machining of Al 2024 alloy. Int J Adv Manuf Technol 112:1461–1481

    Article  Google Scholar 

  52. Badisch E, Mitterer C, Mayrhofer PH, Mori G, Bakker RJ, Brenner J, Störi H (2004) Characterization of tribo-layers on self-lubricating plasma-assisted chemical-vapor-deposited TiN coatings. Thin Solid Films 460(1-2):125–132

    Article  Google Scholar 

  53. Guleryuz CG, Krzanowski JE (2010) Mechanisms of self-lubrication in patterned TiN coatings containing solid lubricant microreservoirs. Surf Coat Technol 204(15):2392–2399

    Article  Google Scholar 

  54. Rech J, Kusiak A, Battaglia JL (2004) Tribological and thermal functions of cutting tool coatings. Surf Coat Technol 186(3):364–371

    Article  Google Scholar 

  55. Grzesik W (1998) The role of coatings in controlling the cutting process when turning with coated indexable inserts. J Mater Process Technol 79(1-3):133–143

    Article  Google Scholar 

  56. Zitouni M (2018) Optimisation des efforts et de la Puissance de coupe en utilisant la Méthode de Taguchi, lors du tournage de l’acier AISI D3 avec des outils en carbure et en céramique. Universite Oum El Bouaghi

    Google Scholar 

  57. Rmili W, Serra R, Ouahabi A (2006) Suivi d’usure des outils de coupe en tournage par analyse vibratoire. ResearchGate

    Google Scholar 

  58. Rathod NJ, Chopra MK, Chaurasiya PK, Vidhate US (2022) Optimization of tool life, surface roughness and production time in CNC turning process using Taguchi method and ANOVA. Annals of Data Science, pp 1–19

    Google Scholar 

  59. Nouioua M, Laouissi A, Brahami R, Blaoui MM, Hammoudi A, Yallese MA (2022) Evaluation of: MOSSA, MOALO, MOVO and MOGWO algorithms in green machining to enhance the turning performances of X210Cr12 steel. Int J Adv Manuf Technol 120(3-4):2135–2150

    Article  Google Scholar 

  60. Gupta MK, Sood PK, Sharma VS (2016) Investigations on surface roughness measurement in minimum quantity lubrication turning of titanium alloys using response surface methodology and Box–Cox transformation. Jour Manu Sci Prod 16(2):75–88

    Google Scholar 

  61. Sargade V, Nipanikar S, Meshram S (2016) Analysis of surface roughness and cutting force during turning of Ti6Al4V ELI in dry environment. Int J Ind Eng Comput 7(2):257–266

    Google Scholar 

  62. Salem, Sahbi Ben. Développement d’un nouveau modèle de la durée de vie de l’outil en fonction des conditions de coupe, de la géométrie de l’outil et de la précision dimensionnelle.

  63. Swain S, Panigrahi I, Sahoo AK, Panda A, Kumar R (2022) An experimental investigation to augment the machinability characteristics during dry turning of Ti-6Al-4V alloy. Arab J Sci Eng 47(7):8105–8127

    Article  Google Scholar 

  64. Kwak JS (2005) Application of Taguchi and response surface methodologies for geometric error in surface grinding process. Int J Mach Tools Manuf 45(3):327–334

    Article  Google Scholar 

  65. Ramesh S, Karunamoorthy L, Palanikumar K (2012) Measurement and analysis of surface roughness in turning of aerospace titanium alloy (gr5). Measurement 45(5):1266–1276

    Article  Google Scholar 

  66. Kosaraju S, Anne VG (2013) Optimal machining conditions for turning Ti-6Al-4V using response surface methodology. Adv Manu 1:329–339

    Article  Google Scholar 

  67. Gariani S, El-Sayed MA, Shyha I (2021) Optimisation of cutting fluid concentration and operating parameters based on RSM for turning Ti–6Al–4V. Int J Adv Manuf Technol 117:539–553

    Article  Google Scholar 

  68. Sahu NK, Andhare AB (2019) Multiobjective optimization for improving machinability of Ti-6Al-4V using RSM and advanced algorithms. J Comput Des Eng 6(1):1–12

    Google Scholar 

  69. Shetty R, Kumar CRS, Ravindra MR (2021) RSM based expert system development for cutting force prediction during machining of Ti–6Al–4V under minimum quantity lubrication. Int J Syst Assur Eng Manag 2021:1–8

    Google Scholar 

  70. Haoues S, Yallese MA, Belhadi S, Chihaoui S, Uysal A (2022) Modeling and optimization in turning of PA66-GF30% and PA66 using multi-criteria decision-making (PSI, MABAC, and MAIRCA) methods: a comparative study. Int J Adv Manuf Technol 124:2401–2421

    Article  Google Scholar 

  71. L’usinage, Du Polyamide. Optimisation mono et multi-objectif de L’usinage du polyamide (PA66-GF30%) en utilisant la methode Taguchi-dear basee sur le rapport signal/bruit.

  72. Mia M, Khan MA, Rahman SS, Dhar NR (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

    Article  Google Scholar 

  73. Yang WH, Tarng YS (1998) Design optimization of cutting parameters for turning operations based on the Taguchi method. J Mater Process Technol 84(1-3):122–129

    Article  Google Scholar 

  74. Karabasevic D, Zavadskas EK, Turskis Z, Stanujkic D (2016) The framework for the selection of personnel based on the SWARA and ARAS methods under uncertainties. Informatica 27(1):49–65

    Article  Google Scholar 

  75. Pamučar D, Ćirović G (2015) The selection of transport and handling resources in logistics centers using multi-attributive border approximation area comparison (MABAC). Expert Syst Appl 42(6):3016–3028

    Article  Google Scholar 

  76. Liang X, Guo J, Sun Y, Liu X (2021) A method of product selection based on online reviews. Mob Inf Syst 2021:1–16

    Google Scholar 

  77. Gupta K, Roy S, Poonia RC, Kumar R, Nayak SR, Altameem A, Saudagar AK (2022) Multi-criteria usability evaluation of mHealth applications on type 2 diabetes mellitus using two hybrid MCDM models: CODAS-FAHP and MOORA-FAHP. Appl Sci 12(9):4156

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank all members of the two laboratories LAMNM and LMS (University of Guelma, Algeria) for their valuable support.

Code availability

This is not available

Funding

The present research was undertaken by the Applied Mechanics of New Materials Laboratory-LMANM, university’ 8 Mai 1945, P.O. Box 401, 24000 Guelma, Algeria.

Author information

Authors and Affiliations

  1. Applied Mechanics of New Materials Laboratory-LMANM, University´ 8 Mai 1945, P.O. Box 401, 24000, Guelma, Algeria

    Khaoula ABBED, Nabil Kribes & Smail Boutabba

  2. Mechanics and Structures Research Laboratory (LMS), University´ 8 Mai 1945, P.O. Box 401, 24000, Guelma, Algeria

    Mohamed Athman Yallese & Salim Chihaoui

Authors
  1. Khaoula ABBED
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nabil Kribes
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohamed Athman Yallese
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Salim Chihaoui
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Smail Boutabba
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Khaoula ABBED: roughness tests and cutting force measurement. Nabil Kribes: test parametric, result analysis, and supervision. Mohamed A. Yallese, wear, modeling, and statistical analysis. Salim Chihaoui: optimization methods. Smail Boutabba, writing of original draft, review, and editing.

Corresponding author

Correspondence to Khaoula ABBED.

Ethics declarations

Ethics approval

I certify that the paper follows the guidelines stated in the journal’s “Ethical Responsibilities of Authors.”

Consent to participate

This is not applicable

Consent for publication

Khaoula ABBED authorizes the publication of the manuscript.

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher’s note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

ABBED, K., Kribes, N., Yallese, M.A. et al. 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 128, 1535–1557 (2023). https://doi.org/10.1007/s00170-023-11775-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-023-11775-6

Keywords

Search

Navigation