Skip to main content
SpringerLink
Log in

Extrusion load prediction of gear-like profile for different die geometries using ANN and FEM with experimental verification

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

Abstract

This paper deals with the extrusion of gear-like profiles and uses of finite element method (FEM) and artificial neural network (ANN) to predict the extrusion load. In the study, gear-like components has been manufactured by forward extrusion for the AA1070 aluminum alloy and the process was simulated by using a DEFORM-3D software package to establish a database in order to provide the data for ANN modeling. Serious experiments were performed for only one die set and four teeth gear profile to obtain data for comparing with DEFORM-3D results. After verifying a highly appropriate FEM simulation with the experiment at the same conditions, Results were enhanced for different die lengths, extrusion ratios, and two extra teeth number as three and six using FEM simulations. Subsequently, the data from the performed FEM simulations were submitted for the best obtained ANN model. Finally, a good agreement between FE-simulated and ANN-predicted results was obtained. The proposed ANN model is found to be useful in predicting the forming load of the different die set variations based on the reliable test data.

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. Azad-Noorani M, Bakhshi-Jooybari M, Hosseinipour SJ, Gorji A (2005) Experimental and numerical study of optimal die profile in cold forward rod extrusion of aluminum. J Mater Process Technol 164–165:1572–1577

    Article  Google Scholar 

  2. Bakhshi-Jooybari M, Saboori M, Noorani-Azad M, Hosseinipour SJ (2007) Combined upper bound and slab method, finite element and experimental study of optimal die profile in extrusion. Mater Des 28:1812–1818

    Article  Google Scholar 

  3. Maity KP, Kar PK, Das NS (1996) A class of upper-bound solutions for the extrusion of square shapes from square billets. J Mater Process Technol 62(1–3):185–190

  4. Chen DC, Syu SK, Wu CH, Lin SK (2007) Investigation into cold extrusion of aluminum billets using three-dimensional finite element method. J Mater Process Technol 192–193:188–193

    Article  Google Scholar 

  5. Chen CT, Ling FF (1968) Upper-bound solutions to axisymmetric extrusion problems. Int J Mech Sci 10(11):863–879

    Article  Google Scholar 

  6. Reggiani B, Segatori A, Donati L (2013) Prediction of charge welds in hollow profiles extrusion by FEM simulations and experimental validation. Int J Adv Manuf Technol 69:1855–1872

  7. Fereshteh-Samiee F, Karimi M, Sabzalipor M (2009) An investigation on the effect of process variables on the required load in axisymetric forward rod extrusion. 3rd international conference on integrity, reliability and failure, Porto

  8. Karami P, Abrinia K (2014) An analytical formulation as an alternative to FEM software giving strain and stres distributions for the three-dimensional solution of extrusion problems. Int J Adv Manuf Technol 71:653–665

  9. Onuh SO, Ekoja M, Adeyemi MB (2003) Effects of die geometry and extrusion speed on the cold extrusion of aluminium and lead alloys. J Mater Process Technol 133:274–285

    Article  Google Scholar 

  10. Qamar SZ, Arif AFM, Sheikh AK (2004) A new definition of shape complexity for metal extrusion. J Mater Process Technol 155–156:1734–1739

    Article  Google Scholar 

  11. Zhang C, Zhao G, Chen H, Guan Y, Li H (2012) Optimization of an aluminum profile extrusion process based on Taguchi’s method with S/N. Int J Adv Manuf Technol 60(5-8):589–599

  12. Jung SY, Kang MC, Kim C, Kim CH, Chang YJ, Han SM (2009) A study on the extrusion by a two-step process for manufacturing helical gear. Int J Adv Manuf Technol 41:684–693

    Article  Google Scholar 

  13. Song JH, Im YT (2004) Development of a computer aided-design system of cold forward extrusion of a spur gear. J Mater Process Technol 153–154:821–828

    Article  Google Scholar 

  14. Altinbalik T, Ayer Ö (2013) Effect of die inlet geometry on extrusion of clover sections through curved dies: upper bound analysis and experimental verification. Trans Nonferrous Metals Soc China 23(4):1098–1107

    Article  Google Scholar 

  15. Altinbalik T, Ayer Ö (2008) A theoretical and experimental study for forward extrusion of clover sections. Mater Des 29(6):1182–1189

    Article  Google Scholar 

  16. Toros S, Ozturk F (2011) Flow curve prediction of Al–Mg alloys under warm forming conditions at various strain rates by ANN. Appl Soft Comput 11:1891–1898

    Article  Google Scholar 

  17. Mandal S, Sivaprasad VP, Venugopal S, Murthy NPK (2009) Artificial neural network modeling to evaluate and predict the deformation behavior of stain less steel type AISI 304 L during hot torsion. Appl Soft Comput 9:237–244

    Article  Google Scholar 

  18. Sheikh H, Serajzadeh S (2008) Estimation of flow stress behavior of AA5083 using artificial neural networks with regard to dynamic strain ageing effect. J Mater Process Technol 196:115–119

    Article  Google Scholar 

  19. Hsiang SH, Lin YW (2008) Application of fuzzy theory to predict deformation behaviors of magnesium alloy sheets under hot extrusion. J Mater Process Technol 201(1–3):138–144

    Article  Google Scholar 

  20. Hagan MT, Demuth HB, Beale MH (1996) Neural network design. PWS Publishing, Boston

    Google Scholar 

  21. Patterson DW (1995) Artificial neural network theory and application. Prentice Hall

  22. Patterson JM (1997) Introduction to artificial neural systems, 2nd edn. Jaico Publishing House, New Delhi

    Google Scholar 

  23. Haykin SS (1999) Neural networks: a comprehensive foundation. Upper Saddle River, Prentice Hall

    MATH  Google Scholar 

  24. Li YY, Bridgwater J (2000) Prediction of extrusion pressure using an artificial neural network. Powder Technol 108:65–73

    Article  Google Scholar 

  25. Hsiang SH, Kuo JL, Yang FY (2006) Using artificial neural networks to investigate the influence of temperature on hot extrusion of AZ61 magnesium alloy. J Intell Manuf 17:191–201

    Article  Google Scholar 

  26. Qin YJ, Pan QL, He YB, Li WB, Liu XY, Fan X (2010) Artificial neural network modeling to evaluate and predict the deformation behavior of ZK60 magnesium alloy during hot compression. Mater Manuf Process 25(7):539–545

    Article  Google Scholar 

  27. 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. Key Eng Mater 424:241–248

    Article  Google Scholar 

  28. 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. Int J Adv Manuf Technol 68(9-12):2547–2564

  29. Freeman JA, Skupura DM (1991) Neural networks algorithms, applications and programming techniques. Addison-Wesley, Reading

    MATH  Google Scholar 

  30. Ayer Ö (2012) Düz Dişlilerin İmalat Yöntemi Optimizasyonu ve Analizler. Ph.D. Thesis, Institute of natural sciences, Trakya University

Download references

Author information

Authors and Affiliations

  1. Dicle University, Diyarbakır, Turkey

    Sedat Bingöl

  2. Trakya University, Edirne, Turkey

    Önder Ayer & Tahir Altinbalik

Authors
  1. Sedat Bingöl
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Önder Ayer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tahir Altinbalik
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Önder Ayer.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bingöl, S., Ayer, Ö. & Altinbalik, T. Extrusion load prediction of gear-like profile for different die geometries using ANN and FEM with experimental verification. Int J Adv Manuf Technol 76, 983–992 (2015). https://doi.org/10.1007/s00170-014-6328-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-014-6328-z

Keywords

Search

Navigation