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2006 | Vol. 14, nr 1-2 | 104--110
Tytuł artykułu

Radial basis function simulation and metamodelling of surface roughness in centreless grinding

Wybrane pełne teksty z tego czasopisma
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Purpose: The purpose of this study was to investigate the efficiency of artificial neural networks and the related metamodels to simulate and identify complex centreless grinding process. Design/methodology/approach: The modeling is founded on the system approach, which is efficiently dealing with the complexity of the grinding process. The unknown process transfer function is identified via artificial neural network that requires fewer assumptions and less precise information about the process modeled than other conventional modeling techniques. The developed metamodel is a response surface (polynomialfit) of the simulated process that is achieved by the computer model. Findings: The metamodel quality is strongly related to the prediction accuracy of the underlying simulation model. The generalization capability of an artificial neural network is sensitive to the training samples (design of experiments). The predictive ability of a metamodel is comparable to the accuracy of the response surface regression model. Research limitations/implications: Improved simulation model and application of unconventional metamodels (Gaussian process regression) will significantly improve the presented preliminary results. Originality/value: Metamodelling of computer experiments is an expansion of response surface methodology and the classical designs of experiments and represents a new paradigm in empirical modelling of machining operations.
Wydawca

Rocznik
Strony
104--110
Opis fizyczny
Bibliogr. 14 poz., rys., tab., wykr.
Twórcy
autor
  • Faculty of Mechanical Engineering, University of Ljubljana, Askerceva 6, SI-1000 Ljubljana, Slovenia
autor
  • Faculty of Mechanical Engineering, University of Ljubljana, Askerceva 6, SI-1000 Ljubljana, Slovenia
autor
  • Faculty of Mechanical Engineering, University of Ljubljana, Askerceva 6, SI-1000 Ljubljana, Slovenia, janez.kopac@fs.uni-lj.si
Bibliografia
  • [1] F. Klocke, D. Friedrich, B. Linke, Z. Nachmani, Basics for in-process roundness error improvement by a functional workrest blade, Annals of the CIRP, 53/1 (2004) 275-280.
  • [2] U. Zuperl, F. Cus, B. Mursec, T. Ploj, A hybrid analytical neural network approach to the determination of optimal cutting conditions, Journal of Materials Processing Technology, 157-158 (2004) 82-90.
  • [3] M. Perzyk, R. Biernacki, A. Kochanski, Modeling of manufacturing processes by learning systems: The naïve Bayesian classifier versus artificial neural networks, Journal of Materials Processing Technology, 164-165 (2005) 1430-1435.
  • [4] J.P.C. Kleijnen, R.G. Sargent, A methodology for fitting and validating metamodels in simulation, European Journal of Operational Research, 120 (2000) 14-29.
  • [5] M. A. Dabnun, M.S.J. Hashmi, M.A. El-Baradie, Surface roughness prediction model by design of experiments for turning machinable glass–ceramic (Macor), Journal of Materials Processing Technology, 164-165 (2005) 1289-1293.
  • [6] J. Peklenik, Contribution to the correlation theory for the grinding process, ASME Journal of Engineering for Industry, (1964), 85-94.
  • [7] S. Dolinsek, M. Sokovic, Influence of TiN (PVD) coating on the tool on the identification parameters in turning, Journal of Materials Processing Technology, 78 (1998) 67-74.
  • [8] W.T. Liao, L.J. Chen, A neural network approach for grinding processes: modelling and optimization, International Journal of Machine Tools and Manufacture, 34 (1994) 919-937.
  • [9] J. C. Principe, N. R. Euliano, W. C. Lefebvre, Neural and adaptive systems: fundamentals through simulations, John Wiley& Sons, Inc., 2000.
  • [10] J.C. Spall, Introduction to stochastic search and optimization, estimation, simulation and control, John Wiley&Sons, Inc., 2003.
  • [11] Design-Expert 6.0.10., Stat-Ease, Inc., 2003.
  • [12] G. Gantar, K. Kuzman, B. Filipic, Increasing the stability of the deep drawing process by simulation-based optimization, Journal of Materials Processing Technology, 164-165 (2005) 1343-1350.
  • [13] J. Kopac, M. Bahor, Interaction of the workpiece material’s technological past and machining parameters on the desired quality of the product surface roughness, Journal of Materials Processing Technology, 109 (2001) 105-111.
  • [14] P. Krajnik, J. Kopac, A. Sluga, Design of grinding factors based on response surface methodology, Journal of Materials Processing Technology, 162-163 (2005) 629-636.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-022f5b63-f12b-42cd-824b-35e165787e0a
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