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Design of an optimized procedure to predict opposite performances in porthole die extrusion

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Abstract

The main objective of advanced manufacturing control techniques is to provide efficient and accurate tools in order to control the set-up of machines and manufacturing systems. Recent developments and implementations of expert systems and neural networks support this aim. This research explores the combined use of neural networks and Taguchi’s method to enhance the performance of porthole die extrusion process; the energy saving and the quality of the welding line are two conflicting objectives of the process taken into account. The complexity of the analysis, due to the number of the involved variables, does not allow the representation of the specified outputs by means of a simple analytical approach. The implementation of a more accurate and sophisticated tool, such as the neural network, results more efficient and easier to be integrated into a simple “ready to use” procedure for predicting the investigated outputs. The main limit to wider implementation of neural networks is the huge computation resources (times and capacities) required to build the data set; a finite element approach was adopted to overcome the time and money wasting typical of experimental investigations. Satisfactory results in terms of prediction capability of the highlighted outputs were found. Finally, a simple and integrated interface was designed to make easier the application of the proposed procedure and to allow the generalization to other manufacturing processes.

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References

  1. Ulrich KT, Eppinger SD (2008) Product design and development, 4th edn. Mc Graw Hill, NY, USA

    Google Scholar 

  2. Wang H, Chen P (2011) Intelligent diagnosis method for rolling element bearing faults using possibility theory and neural network. Comput Ind Eng 60(4):1457–1471

    Google Scholar 

  3. Sha W, Edwards KL (2007) The use of artificial neural networks in materials science based research. Mater Design 28:1747–1752

    Article  Google Scholar 

  4. Lolas S, Olatunbosun OA (2008) Prediction of vehicle reliability performance using artificial neural networks. Expert Syst Appl 34(4):2360–2369

    Article  Google Scholar 

  5. Zhang Z, Friedrich K (2003) Artificial neural networks applied to polymer composites: a review. Compos Sci Tech 63(14):2029–2044

    Article  Google Scholar 

  6. Peng Y, Liu H, Du R (2008) A neural network-based shape control system for cold rolling operations. J Mater Proc Technol 202:54–60

    Article  Google Scholar 

  7. Saberi S, Yusuff RM (2010) Neural network application in predicting advanced manufacturing technology implementation performance. Neural Comput Appl. doi:10.1007/s00521-010-0507-0

  8. Serhat Y, Armagan AA, Erol F (2011) Surface roughness prediction in machining of cast polyamide using neural network. Neural Comput Appl. doi:10.1007/s00521-011-0557-y

  9. Venkatesan D, Kannan K, Saravanan R (2009) A genetic algorithm-based artificial neural network model for the optimization of machining processes. Neural Comput Applic 18(2):135–140

    Article  Google Scholar 

  10. Dae-Cheol K, Dong-Hwan K, Byung-Min K (1999) Application of artificial neural network and Taguchi method to preform design in metal forming considering workability. Int J Mach Tools Manuf 39:771–785

    Article  Google Scholar 

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

    Article  Google Scholar 

  12. Sukthomya W, Tannock J (2005) The training of neural networks to model manufacturing processes. J Intel Manuf 16:39–51

    Article  Google Scholar 

  13. Ceretti E, Fratini L, Gagliardi F, Giardini C (2009) A new approach to study material bonding in extrusion porthole dies. CIRP Ann Manuf Technol 58:259–262

    Article  Google Scholar 

  14. Kalpakjian S, Schmid SR (2003) Manufacturing processes for engineering materials, 4th edn. Addison-Wesley, Menlo Park, CA

    Google Scholar 

  15. Di Lorenzo R, Ingarao G, Micari F (2006) On the use of artificial intelligence tools for fracture forecast in cold forming operations. J Mater Proc Technol 177:315–318

    Article  Google Scholar 

  16. Khotanzad A, Chung C (1998) Application of multi-layer perceptron neural networks to vision problems. Neural Comput Appl 7(3):249–259

    Article  Google Scholar 

  17. Bahloul R, Mkaddem A, Dal Santo P, Potiron A (2007) Sheet metal bending optimisation using response surface method, numerical simulation and design of experiments. Int J Mech Sci 48(6):997–1003

    Google Scholar 

  18. Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation, 2nd edn. Parallel Distributed Processing MIT Press, Cambridge, MA

    Google Scholar 

  19. Khaw JFC, Lim BS, Lim LE (1995) Optimal design of neural networks using the Taguchi methods. Neurocomputing 7(3):225–245

    Article  MATH  Google Scholar 

  20. Ambrogio G, Filice L, Guerriero F, Guido R, Umbrello D (2011) Prediction of incremental sheet forming process performance by using a neural network approach. Int J Adv Manuf Technol. doi: 10.1007/s00170-010-3011-x

  21. Sukthomya W, Tannock J (2005) The optimization of neural network parameters using Taguchi’s design of experiments approach: an application in manufacturing process modeling. Neural Comput Appl 14:337–344

    Article  Google Scholar 

  22. Phadke MS (1989) Quality engineering using Robust Design. Prentice Hall, New Jersey

    Google Scholar 

  23. Maren AJ, Jones D, Franklin F (1990) Configuring and optimizing the back-propagation network, 2nd edn. Handbook of neural computing applications. Academic Press, Santiago

  24. Udo GJ (1992) Neural networks applications in manufacturing processes. Comput Ind Eng 23(1–4):97–100

    Article  Google Scholar 

  25. Hou TH, Su CH, Chang HZ (2008) An integrated multi-objective immune algorithm for optimizing the wire bonding process of integrated circuits. J Intel Manuf 19:361–374

    Article  Google Scholar 

  26. Marquardt D (1963) An algorithm for least-squares estimation of nonlinear parameters. J Soc Ind Appl Math 11(2):431–441

    Article  MathSciNet  MATH  Google Scholar 

  27. Ding L, Matthews J (2009) A contemporary study into the application of neural network techniques employed to automate CAD/CAM integration for die manufacture. Comput Ind Eng 57:1457–1471

    Article  Google Scholar 

  28. Costa MA, Braga A, De Menezes BR (2007) Improving generalization of MLPs with sliding mode control and Levenberg-Marquardt algorithm. Neurocomputing 70:1342–1347

    Article  Google Scholar 

  29. Mutasem KSA, Khairuddin BO, Shahrul AN (2009) Back propagation algorithm: the best algorithm among the multi-layer perceptron algorithm. Int J Comput Sci Netw Secur 9(4):378–383

    Google Scholar 

  30. Zhenyu J, Lada G, Zhong Z, Klaus F, Alois KS (2008) Neural network based prediction on mechanical and wear properties of short fibers reinforced polyamide composites. Mater Design 29(3):628–637

    Article  Google Scholar 

  31. Jo HH, Jeong CS, Lee SK, Kim BM (2003) Determination of welding pressure in the non-steady-state porthole die extrusion of improved Al7003 hollow section tubes. J Mater Proc Technol 139:428–433

    Article  Google Scholar 

  32. Valberg H (2002) Extrusion welding in aluminium extrusion. Int J Mater Product Tech 17:497–556

    Article  Google Scholar 

  33. Valberg H, Loeken T, Hval M, Nyhus B, Thaulow C (1995) The extrusion of hollow profiles with a gas pocket behind the bridge. Int J Mater Product Technol 10(3–6):222–267

    Google Scholar 

  34. Lee JM, Kim BM, Kang CG (2005) Effects of chamber shapes of porthole die on elastic deformation and extrusion process in condenser tube extrusion. Mater Design 26:327–336

    Article  Google Scholar 

  35. Jo HH, Lee SK, Jung CS, Kim BM (2006) A non-steady state FE analysis of Al tubes hot extrusion by a porthole die. J Mater Proc Technol 173:223–231

    Article  Google Scholar 

  36. Kim YT, Ikeda K, Murakami T (2002) Metal flow in porthole die extrusion of aluminium. J Mater Proc Technol 121:107–115

    Article  Google Scholar 

  37. Bariani PF, Bruschi S, Ghiotti A (2006) Physical simulation of longitudinal welding in Porthole-Die extrusion. CIRP Ann Manuf Technol 55(1):287–290

    Article  Google Scholar 

  38. Donati L, Tomesani L, Minak G (2007) Characterization of seam weld quality in AA6082 extruded profiles. J Mater Proc Technol 191(1–3):127–131

    Article  Google Scholar 

  39. Plata M, Piwnik J (2000) Theoretical and experimental analysis of seam weld formation in hot extrusion of aluminum alloys. In: Proceedings of 7th international aluminum extrusion technology, Chicago USA, pp 205–211

  40. Deform TM (2010) User’s manual (Version 10.1). Columbus, OH

  41. Chanda T, Zhou J, Duszczyk J (2000) 3D FEM simulation of thermal and mechanical events occurring during extrusion trough a channel-shaped die. In: Proceedings of 7th international aluminum extrusion technology, Chicago, USA, pp 125–134

  42. Fitta I, Sheppard T (2000) Material flow and prediction of extrusion pressure when extruding through bridge dies using FEM. In Proceedings of 7th international aluminum extrusion technology, Chicago, USA, pp 141–147

  43. Demuth H, Beale M, Hagan M (2005) Matlab neural network toolbox user’s guide. The MathWorks, Inc, Natick, MA

    Google Scholar 

  44. Arbib MA (1995) Handbook of brain theory and neural networks. The MIT Press, Cambridge, MA. ISBN:0-262-01148-4

  45. Ingarao G, Di Lorenzo R, Micari F (2010) Sustainability issues in sheet metal forming processes: an overview. J Cleaner Product 19(4):337–347

    Article  Google Scholar 

  46. Freeman JA, Skapura DM (1991) Neural networks: algorithms, applications, and programming techniques. Addison-Wesley, Reading MA

    MATH  Google Scholar 

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Authors and Affiliations

  1. Department of Mechanical Engineering, University of Calabria, 87036, Rende, CS, Italy

    G. Ambrogio & F. Gagliardi

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  1. G. Ambrogio
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  2. F. Gagliardi
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Correspondence to G. Ambrogio.

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Ambrogio, G., Gagliardi, F. Design of an optimized procedure to predict opposite performances in porthole die extrusion. Neural Comput & Applic 23, 195–206 (2013). https://doi.org/10.1007/s00521-012-0916-3

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  • DOI: https://doi.org/10.1007/s00521-012-0916-3

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