Skip to main content
SpringerLink
Log in

Intelligent parameter identification of machining Ti64 alloy

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

Abstract

In this paper, a methodology approach based on analysis of multidimensional Pareto front is proposed. A new optimization approach helps the user to set the optimal parameters of a machining process. Four neural networks are used to model desire output responses, and they are used as objective functions. Particle swarm optimization (PSO) is used to find the best parameters that improve process. As application of approached proposed, an analysis of a multidimensional Pareto front is made considering a minimization of time, temperature, vibration, and surface roughness in a milling process of Ti64 alloy. Physical parameters for experimental approach are tool diameter, number of cutting edge of the tool, cutting speed, feed, and depth of cut. Analysing the 2D and 3D multidimensional Pareto front is generated a user table of machining parameters.

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

Similar content being viewed by others

References

  1. Abaqus/Explicit, Users manual. Abaqus

  2. ASM Handbook (2010) Metals process simulation. ASM International

  3. Bharathi Raja S, Baskar N (2011) Particle swarm optimization technique for determining optimal machining parameters of different work piece materials in turning operation. Int J Adv Manuf Technol 54(5–8):577

    Google Scholar 

  4. Arrazola PJ, Garay A, Iriarte LM, Armendia M, Marya S, Maitre FL (2009) Machinability of titanium alloys (ti6al4v and ti555.3). J Mater Process Technol 209(5):2223–2230. doi:10.1016/j.jmatprotec.2008.06.020

    Article  Google Scholar 

  5. Gonzalez Gonzales C, Zeleny R (2013) Metrologia dimensional. Mc-Graw Hill

  6. Chelladurai H, Jain V, Vyas N (2008) Development of a cutting tool condition monitoring system for high speed turning operation by vibration and strain analysis. Int J Adv Manuf Technol 37:471–485

    Article  Google Scholar 

  7. Chen G, Ren C, Yang X, Jin X, Guo T (2011) Finite element simulation of high-speed machining of titanium alloy (ti 6al 4v) based on ductile failure model. Int J Adv Manuf Technol 56(9–12). doi:10.1007/s00170-011-3233-6

  8. Childs THC (1998) Material property need in modeling metal machining. In: Proceedings of the CIRP international workshop on modeling of machining operations, pp 193–202

  9. Ciurana J, Arias G, Ozel T (2009) Neural network modeling and particle swarm optimization (pso) of process parameters in pulsed laser micromachining of hardened aisi h13 steel. Mater Manuf Process 24(3):358–368 . doi:10.1080/10426910802679568

    Article  Google Scholar 

  10. Darabi A, Alfi A, Kiumarsi B, Modares H (2011) Employing adaptive particle swarm optimization algorithm for parameter estimation of an exciter machine. J Dyn Syst Meas Control 134(1). doi:10.1115/1.4005371

  11. Ezugwu E, Wang Z (1997) Titanium alloys and their machinability: a review. J Mater Process Technol 68(3):262–274. doi:10.1016/S0924-0136(96)00030-1. Superplasticity and Superplastic Technology in Japan

    Article  Google Scholar 

  12. Haykin S. (2008) Neural networks and learning machines. Prentice Hall, Pearson

    Google Scholar 

  13. Shi K, Zhang D, Ren J, Yao C, Yuan Y (2014) Multiobjective optimization of surface integrity in milling TB6 alloy based on taguchi-grey relational analysis. Adv Mech Eng 2014(ID280313). doi:10.1155/2014/280313

  14. Kennedy J, Eberhart R et al (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, vol 4. Perth, Australia, pp 1942–1948

  15. Khan M, Kumar A, Poomari A (2012) A hybrid algorithm to optimize cutting parameter for machining gfrp composite using alumina cutting tools. Int J Adv Manuf Technol 59(9–12). doi:10.1007/s00170-011-3553-6

  16. Komanduri R, Hou ZB (2002) On thermoplastic shear instability in the machining of a titanium alloy (ti-6al-4v). Metall Mater Trans A 33(9):2995–3010. doi:10.1007/s11661-002-0284-1

    Article  Google Scholar 

  17. Lazoglu I, Altintas Y (2002) Prediction of tool and chip temperature in continuous and interrupted machining. Int J Mach Tools Manuf 42(9):1011–1022. doi:10.1016/S0890-6955(02)00039-1

    Article  Google Scholar 

  18. Maass P, Kuhfu B, Riemer O (2008) Mathematical models for surface characterization of machining processes. Microsyst Technol 14:1989–1993. doi:10.1007/s00542-008-0687-z

    Article  Google Scholar 

  19. Majumder A (2013) Process parameter optimization during EDM of AISI 316 LN stainless steel by using fuzzy based multi-objective PSO. J Mech Sci Technol 27(7). doi:10.1007/s12206-013-0524-x

  20. Orhan S, Er A, cu, NC, Aslan E (2007) Tool wear evaluation by vibration analysis during end milling of AI d3 cold work tool steel with 35 hrc hardness. Elsevier NDT&E International 40:121–126

  21. Palanisamy P, Rajendran I, Shanmugasundaram S, Saravanan R (2008) Prediction of cutting forcer and temperature rise in endmilling operation. Proc Inst Mech Eng B J Eng Manuf 220(10)

  22. Parapar J, Vidal MM, Santos J (2012) Finding the best parameter setting particle swarm optimisation

  23. Rashid RR, Sun S, Wang G, Dargusch M (2012) An investigation of cutting forces and cutting temperatures during laser-assisted machining of the ti-6cr-5mo-5v-4al beta titanium alloy. Int J Mach Tools Manuf 63:58–69. doi:10.1016/j.ijmachtools.2012.06.004

    Article  Google Scholar 

  24. Sun J, Guo Y (2009) Material flow stress and failure in multiscale machining titanium alloy ti-6al-4v. Int J Adv Manuf Technol 41(7–8). doi:10.1007/s00170-008-1521-6

  25. Ulutan D, Özel T (2013) Multiobjective optimization of experimental and simulated residual stresses in turning of nickel-alloy in100. Mater Manuf Process 28(7):835–841. doi:10.1080/10426914.2012.718474

    Article  Google Scholar 

  26. Váquez E, Ciurana J, Rodríguez CA, Thepsonthi T, Özel T (2011) Swarm intelligent selection and optimization of machining system parameters for microchannel fabrication in medical devices. Mater Manuf Process 26(3):403–414. doi:10.1080/10426914.2010.520792

    Article  Google Scholar 

  27. Vázquez E, Ciurana J, Rodríguez CA, Thepsonthi T, Ozel T (2011) Swarm intelligent selection and optimization of machining system parameters for microchannel fabrication in medical devices. Mater Manuf Process 26(3):403–414. doi:10.1080/10426914.2010.520792

    Article  Google Scholar 

  28. Weiss I, Semiatin S (1998) Thermomechanical processing of beta titanium alloys: an overview. Mater Sci Eng A 243(1,2):46–65. doi:10.1016/S0921-5093(97)00783-1

    Article  Google Scholar 

  29. Yusup N, Zain AM, Hashim SZM (2012) Evolutionary techniques in optimizing machining parameters: review and recent applications (2007-2011). Expert Syst Appl 39(10):9909–9927. doi:10.1016/j.eswa.2012.02.109

    Article  Google Scholar 

  30. Zatarain M, Bediaga I, Munoa J, Insperger T (2009) Analysis of directional factors in milling: importance of multi-frequency calculation and of the inclusion of the effect of the helix angle. Int J Adv Manuf Technol 47:535–542. doi:10.1007/s00170-009-2230-5

    Article  Google Scholar 

  31. Zhang Q, Mahfouf M, Yates JR, Pinna C, Panoutsos G, Boumaiza S, Greene RJ, de Leon L (2011) Modeling and optimal design of machining-induced residual stresses in aluminium alloys using a fast hierarchical multiobjective optimization algorithm. Mater Manuf Process 26(3):508–520. doi:10.1080/10426914.2010.537421

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Facultad de Ingeniería Mecánica y Eléctrica, Universidad Autónoma de Nuevo León, Ave. Universidad s/n, Cd. Universitaria, San Nicolas de los Garza, NL, CP 66450, Mexico

    Indira G. Escamilla-Salazar, Luis Torres-Trevi no & Bernardo Gonzalez-Ortiz

Authors
  1. Indira G. Escamilla-Salazar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luis Torres-Trevi no
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bernardo Gonzalez-Ortiz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Indira G. Escamilla-Salazar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Escamilla-Salazar, I.G., Torres-Trevi no, L. & Gonzalez-Ortiz, B. Intelligent parameter identification of machining Ti64 alloy. Int J Adv Manuf Technol 86, 1997–2009 (2016). https://doi.org/10.1007/s00170-015-7967-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-015-7967-4

Keywords

Search

Navigation