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Determination of machining parameters in HSM through TSK-FLC

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Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Purpose: The optimal setting of machining parameters that may be realized via a suitable model/controller is an important concern to fulfill the overall objectives in machining. Design/methodology/approach: The present paper proposes an approach for determination of optimal setting of machining parameters in high speed climb milling operation through an TSK-type fuzzy logic controller (TSK-FLC). A novel approach is proposed here which combines the techniques of linear regression (LR) and genetic algorithm (GA) to utilize the advantages of each other, in order to develop an efficient FLC for high-speed milling. Findings: Modeling of manufacturing process enables generating of manufacturing data and knowledge representation in machining process. Comparisons of results with real experimental data as well as those obtained by other common methods of modeling show the effectiveness of the FLC. Research limitations/implications: The design approach of fuzzy logic controller uses experimental data for learning. The shape fuzzy subsets as well as the structure(s) of rule consequent functions are the important concern for optimal knowledgebase (KB) of a FLC. Use of the advantages of both LR and GA makes it possible to achieve optimal KB of FLC. Practical implications: Use of developed FLC results in improved productivity and efficiency of machining process via the setting of optimal values of cutting parameters and the possibility to develop automatic manufacturing system by online determination of machining parameters. Originality/value: The paper describes a method for designing a FLC for manufacturing process by a combination of LR and GA, which leads to eliminate a long regression function as required in standard linear regression method.
Rocznik
Strony
57--60
Opis fizyczny
Bibliogr. 60 poz., rys., tab.
Twórcy
autor
  • Central Mechanical Engineering Research Institute, M.G. Avenue, Durgapur-713209, WB, India, nandiarup@yahoo.com
Bibliografia
  • [1] H. Juan, S.F. Yu, B.Y. Lee, The optimal cutting-parameter selection of production cost in HSM for SKD61 tool steels, International Journal of Machine Tools and Manufacture 43 (7) (2003) 679-686.
  • [2] A. Ber, J. Rotberg, S. Zombach, A method for cutting force evaluation of end mills, CIRP Annals 37 (1) (1988) 37-40.
  • [3] E.J.A. Armarego, N.P. Deshpande, Computerized predictive cutting models for forces in end-milling including eccentricity effects, CIRP Annals 38 (1) (1989) 45-50.
  • [4] Y. Altintas, S. Engin, Generalized Modeling of Mechanics and Dynamics of Milling Cutters, CIRP Annals 50 (1) (2001) 25-30.
  • [5] H. Leea, D.W. Cho, Development of a reference cutting force model for rough milling feedrate scheduling using FEM analysis, International Journal of Machine Tools and Manufacture 47 (1) (2007).
  • [6] B. Ozcelika, M. Bayramoglu, The statistical modeling of surface roughness in high-speed flat end milling, International Journal of Machine Tools and Manufacture 46 (12-13) (2006) 1395-1402.
  • [7] F. Cus, M. Milfelner, J. Balic, An intelligent system for monitoring and optimization of ball-end milling process, Journal of Materials Processing Technology 175 (2006) 90-97.
  • [8] U. Zuperl, F. Cus and M. Milfelner, Fuzzy control strategy for an adaptive force control in end-milling, Journal of Materials Processing Technology 164-165 (2005), 1472-1478.
  • [9] L.N. Lopez De Lacalle, A. Lamikiz, J.A. Sanchez, J.L. Arana, Improving the surface finish in high speed milling of stamping dies, Journal of Materials Processing Technology 123 (2002), 292-302.
  • [10] A.K. Nandi, TSK-Type FLC using a combined LR and GA: surface roughness prediction in ultraprecision turning. Journal of Material Processing Technology 178 (1-3) (2006) 200-210.
  • [11] M. Sugeno, G. T. Kang, Structure identification of fuzzy model, Journal of Fuzzy Sets and Systems 28 (1) (1988) 15-33.
  • [12] T. Takagi, M. Sugeno, Fuzzy identification of systems and its application to modelling and control, IEEE Transactions on Systems, Man and Cybernetics 15 (1) (1985) 116-132.
  • [13] A. Nandi, F. Klawonn, Detecting ambiguities in regression problems using TSK models, IEEE International conference on Fuzzy Systems (FUZZ-IEEE 2004), Budapest, Hungary, 2004, 221-226.
  • [14] D.E. Goldberg, Genetic algorithms in search, optimization, and machine learning, Addison-Wesley, Reading, MA, USA, 1989.
  • [15] J. Vivancos, C.J. Luis, L. Costa, J.A. Ortiz, Optimal machining parameters selection in high speed milling of hardened steel for injection moulds, Proceedings of the International Conference on Advances in Materials and Processing Technologies. DCU, Ireland, 2003, 815-818.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BOS3-0016-0087
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