Skip to main content

Automatic optimization design of a feeder extrusion die with response surface methodology and mesh deformation technique


During the extrusion process of aluminum alloy profiles, scientific and reasonable design of extrusion dies affects directly on the product qualified rate, production efficiency, and cost. In this work, for a 7N01 aluminum alloy beam profile used in high-speed train, the response surface method (RSM) and mesh deformation technique (MDT) are applied to automatically optimize the feeder chamber of the extrusion die. With implanted material physical properties and constitutive model of this alloy, the numerical simulation of the extrusion process is firstly carried out and verified experimentally. Then, RSM is used to optimize seven different geometric variables of the feeder chamber and to narrow the variables’ ranges. On the basis of above optimization, MDT is finally applied to automatically optimize the feeder chamber. After optimization, the velocity distribution in the cross section of the profile tends to more uniform. The standard deviation of the velocity field decreases from 7.83 to 2.47 mm/s with the reducing rate of 68.46%. More importantly, the automatic optimization process with mesh deformation technique is explored in this work, which could provide an effective and potential means for the automatic optimization design of three-dimensional dies or products in other fields.

This is a preview of subscription content, access via your institution.


  1. 1.

    Peng Z, Sheppard T (2005) Effect of die pockets on multi-hole die extrusion [J]. Mater Sci Eng A 407:89–97

    Article  Google Scholar 

  2. 2.

    Wu XH, Zhao GQ, Luan YG, Ma XW (2006) Numerical simulation and die structure optimization of an aluminum rectangular hollow pipe extrusion process [J]. Mater Sci Eng A 435-436:266–274

    Article  Google Scholar 

  3. 3.

    Fang G, Zhou J, Duszczyk J (2009) FEM simulation of aluminium extrusion through two-hole multi-step pocket dies [J]. J Mater Process Technol 209:1891–1900

    Article  Google Scholar 

  4. 4.

    He YF, Xie SS, Cheng L, Huang GJ, Fu Y (2010) FEM simulation of aluminum extrusion process in porthole die with pockets [J]. Trans Nonferrous Metals Soc China 20:1067–1071

    Article  Google Scholar 

  5. 5.

    Lee SH, Lee JM, Jo HH, Jo H, Kim BM (2008) Process analysis and die design in 12 cells condenser tube extrusion of Al3003 [J]. J Mater Process Technol 201:53–59

    Article  Google Scholar 

  6. 6.

    Liu P, Xie SS, Cheng L (2012) Die structure optimization for a large, multi-cavity aluminum profile using numerical simulation and experiments [J]. Mater Des 36:152–160

    Article  Google Scholar 

  7. 7.

    Chen L, Zhao GQ, Yu JQ, Zhang WD, Wu T (2014) Analysis and porthole die design for a multi-hole extrusion process of a hollow, thin-walled aluminum profile [J]. Int J Adv Manuf Technol 74:383–392

    Article  Google Scholar 

  8. 8.

    Zhao GQ, Chen H, Zhang CS, Guan YJ, Gao AJ, Li P (2014) Die optimization design and experimental study of a large wallboard aluminum alloy profile used for high-speed train [J]. Int J Adv Manuf Technol 74:539–549

    Article  Google Scholar 

  9. 9.

    Zhang CS, Zhao GQ, Guan YJ, Gao AJ, Wang LJ, Li P (2015) Virtual tryout and optimization of the extrusion die for an aluminum profile with complex cross-sections [J]. Int J Adv Manuf Technol 78(5–8):927–937

    Article  Google Scholar 

  10. 10.

    Sun YD, Chen QR, Sun WJ (2015) Numerical simulation of extrusion process and die structure optimization for a complex magnesium doorframe [J]. Int J Adv Manuf Technol 80:495–506

    Article  Google Scholar 

  11. 11.

    Zhou J, Li L, Mo J, Zhou J, Duszczyk J (2009) Prediction of the extrusion load and exit temperature using artificial neural networks based on FEM simulation [J]. Key Eng Mater 424:241–248

    Article  Google Scholar 

  12. 12.

    Gagliardi F, Ambrogio G, Filice L (2012) On the die design in AA6082 porthole extrusion [J]. CIRP Annals-Manufacturing Technology 61(1):231–234

    Article  Google Scholar 

  13. 13.

    Jawwad AKA, Barghash MA (2013) Evaluating the effects of process parameters on maximum extrusion pressure using a new artificial neural network (ANN-based) partial-modeling technique [J]. Int J Adv Manuf Technol 68(9–12):2547–2564

    Article  Google Scholar 

  14. 14.

    Zhao GQ, Chen H, Zhang CS, Guan YJ (2013) Multiobjective optimization design of porthole extrusion die using Pareto-based genetic algorithm [J]. Int J Adv Manuf Technol 69:1547–1556

    Article  Google Scholar 

  15. 15.

    Bingöl S, Ayer Ö, Altinbalik T (2015) Extrusion load prediction of gear-like profile for different die geometries using ANN and FEM with experimental verification [J]. Int J Adv Manuf Technol 76(5):983–992

    Article  Google Scholar 

  16. 16.

    Fang HF, Cai LH, Wang MQ, Wang Q, He DF (2014) Optimization design of I-type extrusion die based on flow balance [J]. The Open Mechanical Engineering Journal 8:694–698

    Article  Google Scholar 

  17. 17.

    Lebaal N, Schmidt F, Puissant S (2009) Design and optimization of three-dimensional extrusion dies, using constraint optimization algorithm [J]. Finite Elem Anal Des 45(5):333–340

    Article  Google Scholar 

  18. 18.

    Ulysse P (2002) Extrusion die design for flow balance using FE and optimization methods [J]. Int J Mech Sci 44(2):319–341

    Article  MATH  Google Scholar 

  19. 19.

    Venketesan R (2010) Extrusion die profile design using simulated annealing algorithm and particle swarm optimization [J]. International Journal of Engineering Science and Technology 2(8):3758–3761

    Google Scholar 

  20. 20.

    Pathak K, Lomash S, Jain N, Jha AK (2009) Tube extrusion design for some selected inner profiles [J]. International Journal of Physical Sciences 4(2):69–75

    Google Scholar 

  21. 21.

    Pathak KK, Lomash S, Jha AK (2010) Die profile design for tube extrusion and its experimental verification [J]. J Braz Soc Mech Sci Eng 32(2):160–164

    Google Scholar 

  22. 22.

    Reddy M, Mayavaram R, Durocher D, Carlsson H, Bergquist O (2004) Analysis and design optimization of aluminum extrusion dies [C]. Proceeding of the Eight International Aluminum Extrusion Technology Seminar 1:231–235

    Google Scholar 

  23. 23.

    Anglani A, Del Prete A, Papadia G (2005) Virtual tryout and optimization of the extrusion process using a shape variables generator integrated in the CAE processing environment [J]. In: Kuljanic E (ed) Advanced manufacturing systems and technology, CISM courses and lectures no. 486. Springer, Wien New York

    Google Scholar 

  24. 24.

    Mayavaram R, Sajja U, Secli C, Niranjan S (2013) Optimization of bearing lengths in aluminum extrusion dies [J]. Procedia CIRP 12:276–281

    Article  Google Scholar 

  25. 25.

    JIS H4100-2006 (2006) Aluminium and aluminium alloy extruded shape [S]. Japanese Industrial Standards Committee

  26. 26.

    Zhang CS, Yang S, Wang CX, Zhao GQ, Gao AJ, Wang LJ (2016) Numerical and experimental investigation on thermo-mechanical behavior during transient extrusion process of high-strength 7××× aluminum alloy profile [J]. Int J Adv Manuf Technol 85:1915–1926

    Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Guoqun Zhao.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhang, C., Yang, S., Zhang, Q. et al. Automatic optimization design of a feeder extrusion die with response surface methodology and mesh deformation technique. Int J Adv Manuf Technol 91, 3181–3193 (2017).

Download citation


  • Automatic optimization
  • Feeder extrusion die
  • Response surface method
  • Mesh deformation technique
  • Design of experiment