Skip to main content
Log in

Modeling and optimization of cold extrusion process by using response surface methodology and metaheuristic approaches

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Obtaining the optimal extrusion process parameters by integration of optimization techniques was crucial and continuous engineering task in which it attempted to minimize the tool load. The tool load should be minimized as higher extrusion forces required greater capacity and energy. It may lead to increase the chance of part defects, die wear and die breakage. Besides, optimization may help to save the time and cost of producing the final product, in addition to produce better formability of work material and better quality of the finishing product. In this regard, this study aimed to determine the optimal extrusion process parameters. The minimization of punch load was the main concern, in such a way that the structurally sound product at minimum load can be achieved. Minimization of punch load during the extrusion process was first formulated as a nonlinear programming model using response surface methodology in this study. The established extrusion force model was then taken as the fitness function. Subsequently, the analytical approach and metaheuristic algorithms, specifically the particle swarm optimization, cuckoo search algorithm (CSA) and flower pollination algorithm, were applied to optimize the extrusion process parameters. Performance assessment demonstrated the promising results of all presented techniques in minimizing the tool loading. The CSA, however, gave more persistent optimization results, which was validated through statistical analysis.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

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

    Article  Google Scholar 

  2. Kuzman K (2001) Problems of accuracy control in cold forming. J Mater Process Technol 113:10–15

    Article  Google Scholar 

  3. Narasimha M, Rejikumar R (2013) Plastic pipe defects minimization. Int J Innov Res Dev 2:1337–1351

    Google Scholar 

  4. Ashhab MS, Breitsprecher T, Wartzack S (2014) Neural network based modeling and optimization of deep drawing—extrusion combined process. J Intell Manuf 25:77–84

    Article  Google Scholar 

  5. Bakhtiari H, Karimi M, Rezazadeh S (2014) Modeling, analysis and multi-objective optimization of twist extrusion process using predictive models and meta-heuristic approaches, based on finite element results. J Intell Manuf 27:1–11

    Google Scholar 

  6. Alam MS, Pathania S, Sharma A (2016) Optimization of the extrusion process for development of high fibre soybean-rice ready-to-eat snacks using carrot pomace and cauliflower trimmings. LWT Food Sci Technol 74:135–144

    Article  Google Scholar 

  7. Lin Z, Juchen X, Xinyun W, Guoan H (2003) Optimization of die profile for improving die life in the hot extrusion process. J Mater Process Technol 142:659–664

    Article  Google Scholar 

  8. Zhou J, Lin L, Luo Y (2014) The multi-objective optimization design of a new closed extrusion forging technology for a steering knuckle with long rod and fork. Int J Adv Manuf Technol 72:1219–1225

    Article  Google Scholar 

  9. Ghassemali E, Tan M-J, Jarfors AEW, Lim SCV (2013) Optimization of axisymmetric open-die micro-forging/extrusion processes: an upper bound approach. Int J Mech Sci 71:58–67

    Article  Google Scholar 

  10. Sharififar M, Akbari Mousavi SAA (2015) Simulation and optimization of hot extrusion process to produce rectangular waveguides. Int J Adv Manuf Technol 79:1961–1973

    Article  Google Scholar 

  11. Zhao G, Chen H, Zhang C, Guan Y (2013) Multiobjective optimization design of porthole extrusion die using Pareto-based genetic algorithm. Int J Adv Manuf Technol 69:1547–1556

    Article  Google Scholar 

  12. Chen WJ, Su WC, Nian FL, Lin JR, Chen DC (2013) Application of ANOVA and Taguchi-based mutation particle swarm algorithm for parameters design of multi-hole extrusion process. Res J Appl Sci Eng Technol 6:2316–2325

    Google Scholar 

  13. Yaghoobi A, Bakhshi-Jooybari M, Gorji A, Baseri H (2016) Application of adaptive neuro fuzzy inference system and genetic algorithm for pressure path optimization in sheet hydroforming process. Int J Adv Manuf Technol 86:1–11

    Article  Google Scholar 

  14. Huang H-X, Li J-C, Xiao C-L (2015) A proposed iteration optimization approach integrating backpropagation neural network with genetic algorithm. Expert Syst Appl 42:146–155

    Article  Google Scholar 

  15. Fereshteh-Saniee F, Sepahi-Boroujeni A, Sepahi-Boroujeni S (2016) Optimized tool design for expansion equal channel angular extrusion (Exp-ECAE) process using FE-based neural network and genetic algorithm. Int J Adv Manuf Technol 86:1–12

    Article  Google Scholar 

  16. Sun X, Zhao G, Zhang C, Guan Y, Gao A (2013) Optimal design of second-step welding chamber for a condenser tube extrusion die based on the response surface method and the genetic algorithm. Mater Manuf Process 28:823–834

    Article  Google Scholar 

  17. Sharififar M, Akbari Mousavi SAA (2015) Simulation and optimization of hot extrusion process to produce rectangular waveguides. Int J Adv Manuf Technol 79:1961–1973

    Article  Google Scholar 

  18. Chen W-J, Chen D-C, Su W-C, Nian F-L (2011) Optimization design of parameters with hybrid particle swarm optimization algorithm in multi-hole extrusion process. In: Jin D, Lin S (eds) Advances in electronic engineering, communication and management vol 2: proceedings of 2011 international conference on electronic engineering, communication and management (EECM 2011), held on December 24–25, 2011, Beijing, China, 2012. Springer, pp 279–284

  19. Li L, Tang H, Guo S, Huang L, Xu Y (2016) Design and implementation of an integral design CAD system for plastic profile extrusion die. Int J Adv Manuf Technol 1–17. doi:10.1007/s00170-016-9099-x

  20. Farzad H, Ebrahimi R (2017) Die profile optimization of rectangular cross section extrusion in plane strain condition using upper bound analysis method and simulated annealing algorithm. J Manuf Sci Eng 139(2). doi:10.1115/1.4034336

  21. Yilmaz O, Gunes H, Kirkkopru K (2014) Optimization of a profile extrusion die for flow balance. Fibers Polym 15:753–761

    Article  Google Scholar 

  22. Jurković Z, Brezočnik M, Grizelj B, Mandić V (2009) Optimization of extrusion process by genetic algorithms and conventional techniques. Tech Gaz 16:27–33

    Google Scholar 

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

    Google Scholar 

  24. MathWorks, Matlab (2010a) The MathWorks Inc. Massachusetts

  25. Bakhshi-Jooybari M (2002) A theoretical and experimental study of friction in metal forming by the use of the forward extrusion process. J Mater Process Technol 125:369–374

    Article  Google Scholar 

  26. Jurković M, Jurković Z, Buljan S (2006) The tribological state test in metal forming processes using experiment and modelling. J Achiev Mater Manuf Eng 18:383–386

    Google Scholar 

  27. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks. 1995. IEEE Publisher

  28. Hrelja M, Klancnik S, Irgolic T, Paulic M, Jurkovic Z, Balic J, Brezocnik M (2014) Particle swarm optimization approach for modelling a turning process. Adv Prod Eng Manag 9:21

    Google Scholar 

  29. Yang X-S (2012) Flower pollination algorithm for global optimization. In: Durand-Lose J, Jonoska N (eds) Unconventional computation and natural computation. Springer, New York, pp 240–249

  30. Yang XS (2012) Flower pollination algorithm for global optimization. In: Lecture notes in computer science (including subseries Lecture Notes in artificial intelligence and lecture notes in bioinformatics), 2012, pp 240–249

  31. Yang XS, Deb S (2009) Cuckoo Search via Levy flights. In: World congress on nature & biologically inspired computing (NaBIC 2009), 2009

  32. Ong P, Zainuddin Z (2013) An efficient cuckoo search algorithm for numerical function optimization. AIP Conf Proc 1522:1378–1384

    Article  Google Scholar 

  33. Chandramouli R, Pandey K, Kandavel T, Ashokkumar T, Shanmugasundaram D (2007) Influence of material flow constraints during cold forming on the deformation and densification behaviour of hypoeutectoid P/M steel ring preforms. Int J Adv Manuf Technol 31:926–932

    Article  Google Scholar 

  34. Ghaemi F, Ebrahimi R, Hosseinifar R (2013) Optimization of die profile for cold forward extrusion using an improved slab method analysis. Iran J Sci Technol Trans Mech Eng 37:189

    Google Scholar 

  35. Wang G, Deb S, Coelho L (2015) Earthworm optimization algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Int J Bio Inspir Comput (in press)

  36. Wang G-G, Deb S, Cui Z (2015) Monarch butterfly optimization. Neural Comput Appl 1–20. doi:10.1007/s00521-015-1923-y

  37. Wang G-G, Gandomi AH, Alavi AH (2014) Stud krill herd algorithm. Neurocomputing 128:363–370

    Article  Google Scholar 

  38. Meng Z, Pan J-S (2016) Monkey king evolution: a new memetic evolutionary algorithm and its application in vehicle fuel consumption optimization. Knowl Based Syst 97:144–157

    Article  MathSciNet  Google Scholar 

  39. Wang G-G (2016) Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Memet Comput 1–14. doi:10.1007/s12293-016-0212-3

  40. Wang G, Deb S, Gao X, Coelho L (2016) A new metaheuristic optimization algorithm motivated by elephant herding behavior. Int J Bio Inspir Comput (in press)

Download references

Acknowledgments

Financial supports from the Malaysian Government with the cooperation of Universiti Tun Hussein Onn Malaysia (UTHM) in the form of FRGS Grant Vot 1490 are gratefully acknowledged. Authors would like to highlight that the data used in this study were obtained from published work in [22, 23].

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Pauline Ong.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ong, P., Chin, D.D.V.S., Ho, C.S. et al. Modeling and optimization of cold extrusion process by using response surface methodology and metaheuristic approaches. Neural Comput & Applic 29, 1077–1087 (2018). https://doi.org/10.1007/s00521-016-2626-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-016-2626-8

Keywords

Navigation