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Application of neural network and fuzzy model to grinding process control

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Abstract

Grinding is a machining process for removing material from a workpiece by using an expendable grinding wheel. The paper presents, a control system for grinding process using neural network and fuzzy technique. It was discovered that the maximum grinding temperature was very important since too high temperature would lead to surface burns and thermal damage to the grinding wheel as well as the workpiece material. A neuro-fuzzy model was used to analyze the grinding wheel performance index as it affects the general grinding operations of the grinding process. The research work can be applied to any other grinding process, whether wet or dry grinding process.

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References

  • Angelov P, Buswell R (2002) Identification of evolving fuzzy rule-based models. IEEE Trans Fuzzy Syst 10(5):667–677

    Article  Google Scholar 

  • Angelov P, Filev DP (2004) An approach to online identification of Takagi-Sugeno fuzzy models. IEEE Trans Syst Man Cybern B Cybern 34(1):484–498

    Article  Google Scholar 

  • Baruch IS, Mariaca CR (2009) A levenberg-marquardt learning applied for recurrent neural identification and control of a wastewater treatment bioprocess. Int J Intell Syst 24(11):1094–1114

    Article  MATH  Google Scholar 

  • Canuto A, Howells G, Fairhurst MA (2006) Comparative evaluation of the RePART neuro-fuzzy network. In: 6th workshop on fuzzy systems, New York, pp 225–229

  • Cox E (1994) The fuzzy system handbook. AP Professional, New York

  • Crawford S (1979) Basic engineering processes. Rolls-Royce Technical College, Bristol, Printed by W.J. Arrowsmith. Ltd, Bristol

  • Curry AC, Shih AJ, Scattergood RO, Kong J, McSpadden SB (2003) Grinding temperature measurements in MgO, PSZ, using infrared spectrometry. J Am Ceramic Soc 86(7):333–341

    Article  Google Scholar 

  • Dhar NR, Siddiqui AT, Rashid MH (2006) Effect of high-pressure coolant jet on grinding temperature, chip and surface roughness in grinding Aisi-1040 steel. J Eng Appl Sci 1(4):22–28

    Google Scholar 

  • Diniz AE, Marcondes FC, Coppini NL (2000) Technology of the machining of materials, 2nd edn. Artiliber Publisher Limited, Campinas, Brazil

  • Haykin S (1998) Neural networks: a comprehensive foundation, 2nd edn. Prentice Hall, Engelwood Cliffs

    Google Scholar 

  • Iglesias JA, Angelov P, Ledezma A, Sanchis A (2010) An evolving classification of agents behaviors: a general approach. Evol Syst 1(3):161–172

    Article  Google Scholar 

  • Leite D, Ballini R, Costa P, Gomide F (2012) Evolving fuzzy granular modeling from nonstationary fuzzy data streams. Evolv Syst 3(2):65–79

    Article  Google Scholar 

  • Lemos A, Caminhas W, Gomide F (2011) Multivariable Gaussian evolving fuzzy modeling system. IEEE Trans Fuzzy Syst 19(1):91–104

    Article  Google Scholar 

  • Li B, Ye B, Jiang Y, Wang T (2005) A control scheme for grinding based on combination of fuzzy and adaptive control techniques. Int J Information Technol 11(11):70–77

    Google Scholar 

  • Malkin S, Ritter PE (1989) Grinding technology: theory and applications of machining with abrasives. Ellis Horwood Limited Publishers, Chichester, Halsted Press: a division of Wiley

  • Mehrotra K, Mohan CK, Ranka S (1997) Elements of artificial neural networks. The MIT Press, Cambridge

    Google Scholar 

  • Odior AO (2002) Development of a grinding wheel from locally available materials. J Nigerian Inst Product Eng 7(2):62–68

    Google Scholar 

  • Odior AO, Oyawale FA, Charles-Owaba OE, Akpobi JA (2010) Manufacture of abrasive grinding wheel using silicon carbide abrasive materials. J Eng Dev 9:54–64

    Google Scholar 

  • Radford JD, Richardson DB (1978) Production technology, 2nd edn. Edward Arnold, London

    Google Scholar 

  • Rubio JJ, Ortiz JJ, Mariaca FCR, Tovar JC (2011) A method for online pattern recognition of abnormal eye movements. Neural Comput Appl. doi:10.1007/s00521-011-0705-4. http://link.springer.com/journal/521

  • Rubio JJ, Angelov P, Pacheco J (2011) Uniformly stable backpropagation algorithm to train a feedforward neural network. IEEE Trans Neural Netw 22(3):356–366, ISSN: 1045-9227

    Google Scholar 

  • Ruspini E, Bonissone P, Pedryez W (1998) Hand book of Fuzzy Computation. Ed. Iop Pub/Institute of Physics, Williston

    Book  Google Scholar 

  • Shih AJ, Curry AC, Scattergood RO (2003) Grinding of zirconia using the dense vitreous bond silicon carbide wheel. J Manuf Sci Eng 125(4):297–303

    Article  Google Scholar 

  • Stephenson DJ, Sun X, Zervos C (2006) A study on Elid ultra precision grinding of optical glass with acoustic emission. Int J Mach Tools Manuf 46:1053–1063

    Article  Google Scholar 

  • Wasserman PD (1989) Neural computing. Van nostrand Reinhold, New York

    Google Scholar 

  • Zadeh LA (1988) Fuzzy logic. IEEE Computer 21:83–92

    Article  Google Scholar 

  • Yu W, Moreno MA, Ortiz F (2007) System identification using hierarchical fuzzy neural networks with stable learning algorithm. J Intell Fuzzy Syst 18(2):171–183

    MATH  Google Scholar 

  • Zadeh L (1965) Fuzzy sets. Inf Cont 8:338–353

    Article  MathSciNet  MATH  Google Scholar 

Download references

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Correspondence to A. O. Odior.

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Odior, A.O. Application of neural network and fuzzy model to grinding process control. Evolving Systems 4, 195–201 (2013). https://doi.org/10.1007/s12530-013-9073-x

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  • DOI: https://doi.org/10.1007/s12530-013-9073-x

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