Skip to main content
Log in

Optimization of an aluminum profile extrusion process based on Taguchi’s method with S/N analysis

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

Abstract

Taguchi’s design of experiment and numerical simulation were applied in the optimization of an aluminum profile extrusion process. By means of HyperXtrude, the extrusion process was simulated and the effects of process parameters on the uniformity of metal flow and on the extrusion force were investigated with the signal to noise ratio and the analysis of variance. Through analysis, the optimum combination of process parameters for uniform flow velocity distribution was obtained, with the billet diameter of 170 mm, ram speed of 2.2 mm/s, die temperature of 465°C, billet preheated temperature of 480°C, and container temperature of 425°C. Compared with the initial process parameters, the velocity relative difference in the cross-section of extrudate was decreased from 2.81% to 1.39%. In the same way, the optimum process parameters for minimum required extrusion force were gained, with the billet diameter of 165 mm, ram speed of 0.4 mm/s, die temperature of 475°C, billet preheated temperature of 495°C, and container temperature of 445°C. A 24.7% decrease of required extrusion force with optimum process parameters was realized. Through the optimization analysis in this study, the extrusion performance has been greatly improved. Finally, the numerical results were validated by practical experiments, and the comparison showed that the optimization strategy developed in this work could provide the effective guidance for practical production.

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

Access this article

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. Karayel D (2008) Simulation of direct extrusion process and optimal design of technological parameters using FEM and artificial neural network. Key Eng Mater 367:185–192

    Article  Google Scholar 

  2. Hans Raj K, Swarup Sharma R, Srivastava S, Patvardhan C (2000) Modeling of manufacturing processes with ANNs for intelligent manufacturing. Int J Mach Tools Manuf 40:851–868

    Article  Google Scholar 

  3. Abrinia K, Makaremi M (2009) An analytical solution for the spread extrusion of shaped sections. Int J Adv Manuf Technol 41:670–676

    Article  Google Scholar 

  4. Mihelic A, Stok B (1998) Tool design optimization in extrusion processes. Comput Struct 68:283–293

    Article  MATH  Google Scholar 

  5. Ulysse P (1999) Optimal extrusion die design to achieve flow balance. Int J Mach Tools Manuf 39:1047–1064

    Article  Google Scholar 

  6. Zou L, Xia JC, Wang XY, Hu GA (2003) Optimization of die profile for improving die life in the hot extrusion process. J Mater Process Technol 142:659–664

    Article  Google Scholar 

  7. Byon SM, Hwang SM (2003) Die shape optimal design in cold and hot extrusion. J Mater Process Technol 138:316–324

    Article  Google Scholar 

  8. Jurkovic Z, Jurkovic M, Buljan S (2006) Optimization of extrusion force prediction model using different techniques. J Achieve Mater Manuf Eng 17:353–356

    Google Scholar 

  9. Chen ZZ, Lou ZL, Ruan XY (2007) Finite volume simulation and mould optimization of aluminum profile extrusion. J Mater Process Technol 190:382–386

    Article  Google Scholar 

  10. Lucignano C, Montanari R, Tagliaferri V, Ucciardello N (2010) Artificial neural networks to optimize the extrusion of an aluminium alloy. J Intell Manuf 21:569–574

    Article  Google Scholar 

  11. Taguchi G (1987) The system of experimental design: engineering methods to optimize quality and minimize costs. Quality Resources, White Plains, NY

    Google Scholar 

  12. Fareghi Alamdari R, Hajimirsadeghi SS, Kohsari I (2010) Synthesis of silver chromate nanoparticles: parameter optimization using Taguchi design. Inorg Mater 46:60–64

    Article  Google Scholar 

  13. Kraft FR, Gunasekera S (2005) Conventional hot extrusion. In: Semiatin SL (ed) ASM handbook, volume 14A: metalworking: bulk forming, vol 14. ASM International, Materials Park, Ohio, pp 421–439

    Google Scholar 

  14. Bauser M, Sauer G, Siegert K (2006) Extrusion, 2nd edn. ASM International, Ohio

    Google Scholar 

  15. Lin JL, Wang KS, Yan BH, Tarng YS (2000) Optimization of the electrical discharge machining process based on the Taguchi method with fuzzy logics. J Mater Process Technol 102:48–55

    Article  Google Scholar 

  16. Lin YC, Chen YF, Wang DA, Lee HS (2009) Optimization of machining parameters in magnetic force assisted EDM based on Taguchi method. J Mater Process Technol 209:3374–3383

    Article  Google Scholar 

  17. Tortum A, Yayla N, Celik C, Gokdag M (2007) The investigation of model selection criteria in artificial neural networks by the Taguchi method. Physica A 386:446–468

    Article  Google Scholar 

  18. HyperXtrude 10.0 manual, Altair Engineering, Inc. http://www.tx.altair.com.

  19. Sahoo AK, Rout AK (2009) Investigation of optimal parametric combination for minimum cutting force in turning: response surface methodology approach. J Eng Innovation Res 1:6–13

    Google Scholar 

  20. Adnani A, Basri M, Malek E, Salleh A, Rahman M, Chaibakhsh N, Rahman R (2010) Optimization of lipase-catalyzed synthesis of xylitol ester by Taguchi robust design method. Ind Crop Prod 31:350–356

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to acknowledge financial support from National Natural Science Foundation of China (51105230) and China Postdoctoral Science Foundation funded project (20100481247), 201104586), Shandong Provincial Natural Science Foundation (Z2008F09), State Key Laboratory of Materials Processing and Die & Mould Technology (2011-P09), Program for Changjiang Scholars and Innovative Research Team in University of Ministry of Education of China (IRT0931) and National Science & Technology Pillar Program in the Eleventh Five-year Plan Period of the People’s Republic of China (2009BAG12A07-B01).

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Cunsheng Zhang or Guoqun Zhao.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhang, C., Zhao, G., Chen, H. et al. Optimization of an aluminum profile extrusion process based on Taguchi’s method with S/N analysis. Int J Adv Manuf Technol 60, 589–599 (2012). https://doi.org/10.1007/s00170-011-3622-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-011-3622-x

Keywords

Navigation