Advertisement

Modeling and optimization of the turning parameters of cobalt alloy (Stellite 6) based on RSM and desirability function

  • Riadh Saidi
  • Brahim Ben Fathallah
  • Tarek MabroukiEmail author
  • Salim Belhadi
  • Mohamed Athmane Yallese
ORIGINAL ARTICLE
  • 105 Downloads

Abstract

The present study consists in identifying the significant effects of various cutting conditions characterizing the machining of the cobalt-based alloy (Stellite 6). For that, an experimental approach based on experimental design was adopted. Predictive models, concerning the evolutions of arithmetic mean roughness, tangential force, material removal rate, and cutting power, were established. Their R2 coefficients are, respectively, 97.15, 99.60, 99.71, and 98.11%. The obtained results demonstrate that both feed rate and insert nose radius are high; the arithmetic mean roughness is getting high. Also, it can be underlined that both depth of cut and feed rate have an important effect on tangential force evolution. Moreover, results demonstrate that depth of cut has the main effect on the evolution of material removal rate and it is followed by cutting speed and feed rate effects. Analysis of variance was applied to find significant cutting parameters affecting surface roughness, tangential force, material removal rate, and cutting power evolutions. A Pareto approach confirms the results obtained by ANOVA. Moreover, a multi-objective optimization based on the desirability function was adopted. The optimization was conducted according to three approaches, which are “quality optimization,” “productivity optimization,” and “quality-productivity combination.”

Keywords

Cobalt alloy (Stellite 6) ANOVA RSM Turning Optimization Desirability function 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

This project was realized in MAI laboratory (University of Tunis El Manar, ENIT, Tunis, Tunisia) and LMS laboratory (Guelma University, Algeria). The authors express their thanks to the industrial “Paradigm Precision, Tunisia” specialized in complicated manufacturing and high precision for the customers in commercial and military aviation, power generation, and marine industry.

References

  1. 1.
    Sato J, Omori T, Oikawa K, Ohnuma I, Kainuma R, Ishida K (2006) Cobalt-base high-temperature alloys. Science 312(5770):90–91CrossRefGoogle Scholar
  2. 2.
    Davis JR (2000) Nickel, cobalt, and their alloys. ASM internationalGoogle Scholar
  3. 3.
    Kuzucu V, Ceylan M, Celik H, Aksoy I (1998) An investigation of stellite-6 alloy containing 5.0 wt% silicon. J Mater Process Technol 79(1–3):47–51CrossRefGoogle Scholar
  4. 4.
    Murray JL (1982) The Al−Mg (aluminum−magnesium) system. J Phase Equilib 3(1):60CrossRefGoogle Scholar
  5. 5.
    Lee S-H, Takahashi E, Nomura N, Chiba A (2005) Effect of heat treatment on microstructure and mechanical properties of Ni-and C-free Co–Cr–Mo alloys for medical applications. Mater Trans 46(8):1790–1793CrossRefGoogle Scholar
  6. 6.
    Zaman HA, Sharif S, Kim D-W, Idris MH, Suhaimi MA, Tumurkhuyag Z (2017) Machinability of cobalt-based and cobalt chromium molybdenum alloys—a review. Proc Manuf 11:563–570Google Scholar
  7. 7.
    Bordin A, Bruschi S, Ghiotti A (2014) The effect of cutting speed and feed rate on the surface integrity in dry turning of CoCrMo alloy. Proc CIRP 13:219–224CrossRefGoogle Scholar
  8. 8.
    Yingfei G, de Escalona PM, Galloway A (2017) Influence of cutting parameters and tool wear on the surface integrity of cobalt-based stellite 6 alloy when machined under a dry cutting environment. J Mater Eng Perform 26(1):312–326CrossRefGoogle Scholar
  9. 9.
    Bruschi S, Ghiotti A, Bordin A (2013) Effect of the process parameters on the machinability characteristics of a CoCrMo alloy. In: Key Eng Mater. Trans Tech Publ, p 1976–1983Google Scholar
  10. 10.
    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 91:347–357CrossRefGoogle Scholar
  11. 11.
    Bagci E, Aykut Ş (2006) A study of Taguchi optimization method for identifying optimum surface roughness in CNC face milling of cobalt-based alloy (Stellite 6). Int J Adv Manuf Technol 29(9–10):940–947CrossRefGoogle Scholar
  12. 12.
    Aykut S, Kentli A, Gulmez S, Yazicioglu O (2012) Robust multiobjective optimization of cutting parameters in face milling. Acta Poly Hung 9(4):85–100Google Scholar
  13. 13.
    Aykut Ş, Bagci E, Kentli A, Yazıcıoğlu O (2007) Experimental observation of tool wear, cutting forces and chip morphology in face milling of cobalt based super-alloy with physical vapour deposition coated and uncoated tool. Mater Des 28(6):1880–1888CrossRefGoogle Scholar
  14. 14.
    Aykut Ş, Gölcü M, Semiz S, Ergür H (2007) Modeling of cutting forces as function of cutting parameters for face milling of satellite 6 using an artificial neural network. J Mater Process Technol 190(1–3):199–203CrossRefGoogle Scholar
  15. 15.
    Sarikaya M, Güllü A (2014) The analysis of process parameters for turning cobalt-based super alloy Haynes 25/L 605 using design of experiment. Solid State Phenom 220-221:749–753 5pCrossRefGoogle Scholar
  16. 16.
    Folea M, Schlegel D, Lupulescu N, Parv L (2009) Modeling surface roughness in high speed milling: cobalt based superalloy case study. In: Proceedings of 1st International Conference on Manufacturing Engineering Quality Production System, p 353–357Google Scholar
  17. 17.
    Bağcı E, Aykut Ş (2014) The effects of tool position, coating and cutting parameters on forces, power, MRR and wear in face milling of Stellite 6. Arab J Sci Eng 39(11):8135–8146CrossRefGoogle Scholar
  18. 18.
    Davim JP, Aveiro P (2016) Design of experiments in production engineering. SpringerGoogle Scholar
  19. 19.
    Aouici H, Elbah M, Yallese M, Fnides B, Meddour I, Benlahmidi S (2016) Performance comparison of wiper and conventional ceramic inserts in hard turning of AISI 4140 steel: analysis of machining forces and flank wear. Int J Adv Manuf Technol 87(5–8):2221–2244CrossRefGoogle Scholar
  20. 20.
    Mian A, Driver N, Mativenga P (2011) Identification of factors that dominate size effect in micro-machining. Int J Mach Tools Manuf 51(5):383–394CrossRefGoogle Scholar
  21. 21.
    Bhattacharya A, Das S, Majumder P, Batish A (2009) Estimating the effect of cutting parameters on surface finish and power consumption during high speed machining of AISI 1045 steel using Taguchi design and ANOVA. Prod Eng 3(1):31–40CrossRefGoogle Scholar
  22. 22.
    Camposeco-Negrete C (2013) Optimization of cutting parameters for minimizing energy consumption in turning of AISI 6061 T6 using Taguchi methodology and ANOVA. J Clean Prod 53:195–203CrossRefGoogle Scholar
  23. 23.
    Jaffery SHI, Khan M, Ali L, Mativenga PT (2016) Statistical analysis of process parameters in micromachining of Ti-6Al-4V alloy. Proc Inst Mech Eng B J Eng Manuf 230(6):1017–1034CrossRefGoogle Scholar
  24. 24.
    Jaffery S, Driver N, Mativenga P (2010) Analysis of process parameters in the micromachining of Ti-6Al-4V alloy. In: Proceedings of the 36th International MATADOR Conference. Springer, p 239–242Google Scholar
  25. 25.
    Sarıkaya M, Güllü A (2014) Taguchi design and response surface methodology based analysis of machining parameters in CNC turning under MQL. J Clean Prod 65:604–616CrossRefGoogle Scholar
  26. 26.
    Sarıkaya M, Güllü A (2015) Examining of tool wear in cryogenic machining of cobalt-based Haynes 25 superalloy. World Academy of Science, Engineering and Technology, International Journal of Chemical, Molecular, Nuclear, Materials and Metallurgical Engineering. 9(8):984–988Google Scholar
  27. 27.
    CANNON-MUSKEGON Corporation, (2017) Certification chemical analysis (WT%/PPM) and DICKSON TESTING Company INC, product manufactured by Cannon Muskegon in the USA, Muskegon, Michigan, USA, p 49443–0506Google Scholar
  28. 28.
    Chabbi A, Yallese M, Nouioua M, Meddour I, Mabrouki T, Girardin F (2017) Modeling and optimization of turning process parameters during the cutting of polymer (POM C) based on RSM, ANN, and DF methods. Int J Adv Manuf Technol 91(5–8):2267–2290CrossRefGoogle Scholar
  29. 29.
    Khellaf A, Aouici H, Smaiah S, Boutabba S, Yallese M, Elbah M (2017) Comparative assessment of two ceramic cutting tools on surface roughness in hard turning of AISI H11 steel: including 2D and 3D surface topography. Int J Adv Manuf Technol 89(1–4):333–354CrossRefGoogle Scholar
  30. 30.
    Zerti O, Yallese MA, Khettabi R, Chaoui K, Mabrouki T (2017) Design optimization for minimum technological parameters when dry turning of AISI D3 steel using Taguchi method. Int J Adv Manuf Technol 89(5–8):1915–1934CrossRefGoogle Scholar
  31. 31.
    Suresh R, Basavarajappa S, Gaitonde V, Samuel G (2012) Machinability investigations on hardened AISI 4340 steel using coated carbide insert. Int J Refract Met Hard Mater 33:75–86CrossRefGoogle Scholar
  32. 32.
    Kıvak T, Samtaş G, Cicek A (2012) Taguchi method based optimisation of drilling parameters in drilling of AISI 316 steel with PVD monolayer and multilayer coated HSS drills. Measurement 45(6):1547–1557CrossRefGoogle Scholar
  33. 33.
    Bouzid L, Berkani S, Yallese M, Girardin F, Mabrouki T (2018) Estimation and optimization of flank wear and tool lifespan in finish turning of AISI 304 stainless steel using desirability function approach. Int J Ind Eng Comput 9(3):349–368Google Scholar
  34. 34.
    Roughness ISO 4287 (1997) Geometrical product specifications (GPS)—surface texture: profile method—terms, definitions and surface texture parametersGoogle Scholar
  35. 35.
    Nouioua M, Yallese MA, Khettabi R, Belhadi S, Mabrouki T (2017) Comparative assessment of cooling conditions, including MQL technology on machining factors in an environmentally friendly approach. Int J Adv Manuf Technol 91(9–12):3079–3094CrossRefGoogle Scholar
  36. 36.
    Harrington EC (1965) The desirability function. Indus Quality Cont 21(10):494–498Google Scholar
  37. 37.
    Hessainia Z, Belbah A, Yallese MA, Mabrouki T, Rigal J-F (2013) On the prediction of surface roughness in the hard turning based on cutting parameters and tool vibrations. Measurement 46(5):1671–1681CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Riadh Saidi
    • 1
  • Brahim Ben Fathallah
    • 1
    • 2
  • Tarek Mabrouki
    • 1
    Email author
  • Salim Belhadi
    • 3
  • Mohamed Athmane Yallese
    • 3
  1. 1.Applied Mechanics and Engineering Laboratory (LR-11-ES19)University of Tunis El Manar, ENITTunisTunisia
  2. 2.Mechanical, Material and Process Laboratory (LR99ES05) ENSITUniversity of TunisTunisTunisia
  3. 3.Mechanics and Structures Research Laboratory (LMS)May 8th 1945 UniversityGuelmaAlgeria

Personalised recommendations