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Introduction

  • Ramón Quiza
  • Omar López-Armas
  • J. Paulo Davim
Chapter
  • 969 Downloads
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)

Abstract

This chapter begins with an explanation about the importance of modeling and optimization of manufacturing processes not only from the scientific and researching point of view but also for practical industrial applications. Then it introduces the hybrid approach which combines artificial intelligence tools and finite element method for these modeling and optimization tasks. The advantages and shortcomings of each of these techniques are exposed, highlighting the convenience of combining both methods, increasing the robustness and flexibility. Furthermore, the different approaches for combining artificial intelligence and finite element method in modeling and optimization of manufactured processes are outlined and preliminarily evaluated.

Keywords

Finite Element Method Artificial Intelligence Technique Punch Radius Extrusion Load Cellular Automaton Finite Element 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. A. Anca, A. Cardona, J. Risso, V.D. Fachinotti, Finite element modeling of welding processes. Appl. Math. Model. 35, 688–707 (2011). doi: 10.1016/j.apm.2010.07.026 MathSciNetzbMATHCrossRefGoogle Scholar
  2. P.J. Arrazola, T. Özel, Investigations on the effects of friction modeling in finite element simulation of machining. Int. J. Mech. Sci. 52, 31–42 (2010). doi: 10.1016/j.ijmecsci.2009.10.001 CrossRefGoogle Scholar
  3. Y.T. Azene, R. Roy, D. Farrugia, C. Onisa, J. Mehnen, H. Trautmann, Work roll cooling system design optimisation in presence of uncertainty and constrains. CIRP J. Manuf. Sci. Tech. 2, 290–298 (2010). doi: 10.1016/j.cirpj.2010.06.001 CrossRefGoogle Scholar
  4. E. Boillat, S. Kosolov, R. Glardon, M. Loher, D. Saladin, G. Levy, Finite element and neural network models for process optimization in selective laser sintering. P I Mech. Eng. B.-J. Eng. 218, 607–614 (2004). doi: 10.1243/0954405041167121 CrossRefGoogle Scholar
  5. M. Chandrasekaran, M. Muralidhar, C. Murali Krishna, U.S. Dixit, Application of soft computing techniques in machining performance prediction and optimization: a literature review. Int. J. Adv. Manuf. Tech. 46, 445–464 (2010). doi: 10.1007/s00170-009-2104-x CrossRefGoogle Scholar
  6. W.L. Chan, M.W. Fu, J. Lu, An integrated FEM and ANN methodology for metal-formed product design. Eng. Appl. Artif. Intell. 21, 1170–1181 (2008). doi: 10.1016/j.engappai.2008.04.001 CrossRefGoogle Scholar
  7. T. Childs, K. Mekawa, T. Obiwaka, Y. Yamano, Metal Machining Theory and Applications (Wiley, New York, 2000)Google Scholar
  8. C. Ching-Kao, H.S. Lu, The optimal cutting-parameter selection of heavy cutting process in side milling for SUS304 stainless steel. Int. J. Adv. Manuf. Tech. 34, 440–447 (2007). doi: 10.1007/s00170-006-0630-3 CrossRefGoogle Scholar
  9. R. Coutinho, I. Marinescu, Methodology to compare 3-D and 2-D parameters for the optimization of hard turned surfaces. Mach. Sci. Technol. 9, 383–409 (2005). doi: 10.1080/10910340500196330 CrossRefGoogle Scholar
  10. S. Das, M.F. Abbod, Q. Zhu, E.J. Palmiere, I.C. Howard, D.A. Linkens, A combined neuro fuzzy-cellular automata based material model for finite element simulation of plane strain compression. Comp. Mater. Sci. 40, 366–375 (2007). doi: 10.1016/j.commatsci.2007.01.010 CrossRefGoogle Scholar
  11. P.M. Dixit, U.S. Dixit, Modeling of Metal Forming and Machining Processes by Finite Element and Soft Computing Methods (Springer, London, 2008)Google Scholar
  12. S. Dolinšek, B. Šuštaršic, J. Kopac, Wear mechanisms of cutting tools in high-speed cutting processes. Wear 250, 349–356 (2001). doi: 10.1016/S0043-1648(01)00620-2 CrossRefGoogle Scholar
  13. R.K. Dutta, S. Paul, A.B. Chattopadhyay, The efficacy of back propagation neural net-work with delta bar delta learning in predicting the wear of carbide inserts in face milling. Int. J. Adv. Manuf. Tech. 31, 434–442 (2006). doi: 10.1007/s00170-005-0230-7 CrossRefGoogle Scholar
  14. P.P. Gudur, U.S. Dixit, A neural network-assisted finite element analysis of cold flat rolling. Eng. Appl. Artif. Intell. 21, 43–52 (2008). doi: 10.1016/j.engappai.2006.10.001 CrossRefGoogle Scholar
  15. Z. Fu, J. Mo, L. Chen, W. Chen, Using genetic algorithm-back propagation neural network prediction and finite-element model simulation to optimize the process of multiple-step incremental air-bending forming of sheet metal. Mater. Des. 31, 267–277 (2010). doi: 10.1016/j.matdes.2009.06.019 CrossRefGoogle Scholar
  16. Y.M.A. Hashash, S. Jung, J. Ghaboussi, Numerical implementation of a neural network based material model in finite element analysis. Int. J. Numer. Method Eng. 59, 989–1005 (2004). doi: 10.1002/nme.905 zbMATHCrossRefGoogle Scholar
  17. A.R. Khoei, S. Keshavarz, S.O. Biabanaki, Optimal design of powder compaction processes via genetic algorithm technique. Finite Elem. Anal. Des. 46, 843–861 (2010). doi: 10.1016/j.finel.2010.05.004 CrossRefGoogle Scholar
  18. D.J. Kim, B.M. Kim, Application of neural network and FEM for metal forming processes. Int. J. Mach. Tool Manuf. 40, 911–925 (2000). doi: 10.1016/S0890-6955(99)00090-5 CrossRefGoogle Scholar
  19. T. Kolodziejczyk, R. Toscano, S. Fouvry, G. Morales-Espejel, Artificial intelligence as efficient technique for ball bearing fretting wear damage prediction. Wear 268, 309–315 (2010). doi: 10.1016/j.wear.2009.08.016 CrossRefGoogle Scholar
  20. S. Kumar Singha, A. Kumar Gupta, Application of support vector regression in predicting thickness strains in hydro-mechanical deep drawing and comparison with ANN and FEM. CIRP J. Manuf. Sci. Technol. 3, 66–72 (2010). doi: 10.1016/j.cirpj.2010.07.005 CrossRefGoogle Scholar
  21. A.A. Javadi, T.P. Tan, M. Zhang, Neural network for constitutive modelling in finite element analysis. J. Comput. Assist. Mech. Eng. Sci. 10, 523–529 (2003)zbMATHGoogle Scholar
  22. R.W. Lewis, A.S. Usmani, J.T. Cross, Efficient mould filling simulation in castings by an explicit finite element method. Int. J. Numer. Method Fl 20, 493–506 (2005). doi: 10.1002/fld.1650200606 CrossRefGoogle Scholar
  23. Y.-Y. Lin, S.-P. Lo, Modeling of chemical mechanical polishing process using FEM and abductive network. Eng. Appl. Artif. Intell. 18, 373–381 (2005). doi: 10.1016/j.engappai.2004.09.008 CrossRefGoogle Scholar
  24. A. Mamalis, J. Kundrák, A. Markopoulos, D. Manolakos, On the finite element modelling of high speed hard turning. Int. J. Avd. Manuf. Tech. 38, 441–446 (2008). doi: 10.1007/s00170-007-1114-9 CrossRefGoogle Scholar
  25. K. Manabe, M. Suetake, H. Koyama, M. Yang, Hydroforming process optimization of aluminum alloy tube using intelligent control technique. Int. J. Mach. Tool Manuf. 46, 1207–1211 (2006). doi: 10.1016/j.ijmachtools.2006.01.028 CrossRefGoogle Scholar
  26. D. Moens, D. Vandepitte, A fuzzy finite element procedure for the calculation of uncertain frequency-response functions of damped structures: Part 1-Procedure. J. Sound Vib. 288, 431–462 (2005). doi: 10.1016/j.jsv.2005.07.001 CrossRefGoogle Scholar
  27. R. Muhanna, V. Kreinovich, P. Solín, J. Chessa, R. Araiza, G. Xiang, Interval finite element methods: New directions, in NSF Workshop o Modeling Errors and Uncertainty in Engineering Computations, Savannah, 2006Google Scholar
  28. I. Mukherjee, P.K. Ray, A review of optimization techniques in metal cutting processes. Comput. Ind. Eng. 50, 15–34 (2006). doi: 10.1016/j.cie.2005.10.001 CrossRefGoogle Scholar
  29. P. Palanisamy, I. Rajendran, S. Shanmugasundaram, Prediction of tool wear using regression and ANN models in end-milling operation. Int. J. Adv. Manuf. Tech. 37, 29–41 (2007). doi: 10.1007/s00170-007-0948-5 CrossRefGoogle Scholar
  30. R. Quiza, J.P. Davim, Computational methods and optimization, in Machining of hard materials, ed. by J.P. Davim (Springer, London, 2011)Google Scholar
  31. R. Quiza, J.E. Albelo, J.P. Davim, Multi-objective optimisation of multipass turning by using a genetic algorithm. Int. J. Mater. Prod. Tech. 35, 134–144 (2009). doi: 10.1504/IJMPT.2009.025223 CrossRefGoogle Scholar
  32. P. Ray, B.J. MacDonald, Determination of the optimal load path for tube hydroforming processes using a fuzzy load control algorithm and finite element analysis. Finite Elem. Anal. Des. 41, 173–192 (2004). doi: 10.1016/j.finel.2004.03.005 CrossRefGoogle Scholar
  33. Y. Sahin, A.R. Motorcu, Surface roughness model in machining hardened steel with cubic boron nitride cutting tool. Int. J. Refract. Met. H26, 84–90 (2008). doi: 10.1016/j.ijrmhm.2007.02.005 CrossRefGoogle Scholar
  34. W. Sha, K.L. Edwards, The use of artificial neural networks in materials science based research. Mater. Des. 28, 1747–1752 (2007). doi: 10.1016/j.matdes.2007.02.009 CrossRefGoogle Scholar
  35. A.R. Shahani, S. Setayeshi, S.A. Nodamaie, M.A. Asadi, S. Rezaie, Prediction of influence parameters on the hot rolling process using finite element method and neural network. J. Mater. Process. Tech. 209, 1920–1935 (2009). doi: 10.1016/j.jmatprotec.2008.04.055 CrossRefGoogle Scholar
  36. R.S. Sharma, V. Upadhyay, K.H. Raj, Neuro-fuzzy modeling of hot extrusion process. Indian J. Eng. Mater. Sci. 16, 86–92 (2009)Google Scholar
  37. Y. Sun, W.D. Zeng, Y.Q. Zhao, Y.L. Qi, X. Ma, Y.F. Han, Development of constitutive relationship model of Ti600 alloy using artificial neural network. Comp. Mater. Sci. 48, 686–691 (2010). doi: 10.1016/j.commatsci.2010.03.007 CrossRefGoogle Scholar
  38. F.W. Taylor, On the art of cutting metals. Trans. ASME 28, 310–350 (1907)Google Scholar
  39. D. Umbrello, G. Ambrogio, L. Filice, R. Shivpuri, A hybrid finite element method-artificial neural network approach for predicting residual stresses and the optimal cutting conditions during hard turning of AISI 52100 bearing steel. Mater. Des. 29, 873–883 (2008). doi: 10.1016/j.matdes.2007.03.004 CrossRefGoogle Scholar
  40. D. Umbrello, G. Ambrogio, L. Filice, R. Shivpuri, An ANN approach for predicting subsurface residual stresses and the desired cutting conditions during hard turning. J. Mater. Process. Tech. 189, 143–152 (2007). doi: 10.1016/j.jmatprotec.2007.01.016 CrossRefGoogle Scholar
  41. W. Verhaeghe, M. De Munck, W. Desmet, D. Vandepitte, D. Moens, A fuzzy finite element analysis technique for structural static analysis based on interval fields, in 4th International Workshop on Reliable Engineering Computing, Singapore, pp. 117–128, 2010Google Scholar
  42. X. Wang, W. Wang, Y. Huang, N. Nguyen, K. Krishnakumar, Design of neural net-work-based estimator for tool wear modeling in hard turning. J. Intell. Manuf. 19, 383–396 (2008). doi: 10.1007/s10845-008-0090-8 CrossRefGoogle Scholar
  43. Y. Zhang, S. Zhao, Z. Zhang, Optimization for the forming process parameters of thin-walled valve shell. Thin Wall Struct. 46, 371–379 (2008). doi: 10.1016/j.tws.2007.10.007 CrossRefGoogle Scholar

Copyright information

© The Author(s) 2012

Authors and Affiliations

  • Ramón Quiza
    • 1
  • Omar López-Armas
    • 2
  • J. Paulo Davim
    • 3
  1. 1.Department of Mechanical EngineeringUniversity of MatanzasMatanzasCuba
  2. 2.Department of Mechanical EngineeringUniversity of MatanzasMatanzasCuba
  3. 3.Department of Mechanical EngineeringUniversity of AveiroAveiroPortugal

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