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EDM process optimization via predicting a controller model

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Purpose: Electro-discharge machining is an important manufacture technology in machining difficult-to-cut materials and to shape complicated contours and profiles with high material removal rate, low tool wear and good tolerances. Design/methodology/approach: In machining of carbon-based materials such as WC-Co and non-oxide ceramics which are growingly used, the complexity and non-linear nature of EDM is a serious problem. EDM is the best and nearly the only non-conventional method for machining of these kind of materials, but it shows high instability and tendency to arcing, compared with machining of steel. Occurrence of instability phenomenon due to the different input setting up parameters make the modeling of EDM process impossible with conventional methods. To achieve instantaneous data from machining condition, the new method of fuzzy analysis of single machining pulses and computing the magnitude of system condition in the form of a real number between 0 and 1, has been used. Findings: Some tests with WC-Co material are carried out and finally, the results of implementation of control system on a sinking ED machine and an EDM system that has been set with an expert user, has been compared. Practical implications: The optimization and control of EDM process using the neural model predictive control method. A genetic algorithm has also been employed to optimize the input parameters and to create the optimized setting collection of process. Originality/value: The testing results from ED machining of WC-Co confirms the capability of the system of predictive controller model based on neural network with 32.8% efficiency increasing in stock removal rate.
Rocznik
Strony
161--167
Opis fizyczny
Bibliogr. 12 poz., tab., rys., wykr.
Twórcy
  • School of Mechanical Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran, mahdavin@ut.ac.ir
Bibliografia
  • [1] A. Buchacz, Investigation of piezoelectric in influence on characteristics of mechatronic system, Journal of Achievements in Materials and Manufacturing Engineering 26/1 (2008) 41-48.
  • [2] A. Buchacz, Dynamical flexibility of torsionally vibrating mechatronic system, Journal of Achievements in Materials and Manufacturing Engineering 26/1 (2008) 33-40.
  • [3] N. Constantin, Adaptive Neural Predictive Techniques for Nonlinear Control, Studies in Informatics and Control 12 (2003) 285-291.
  • [4] N. Constantin, I. Dumitrache, Advanced Adaptive Techniques for Multivariable Nonlinear Processes, Proceedings of the 14th IFAC World Congress, 1 (1999) 174-180.
  • [5] J.S. Donat, N. Bhat, T.J. McAvoy, Neural Net Based Model Predictive Control, International Journal of Control 54 (1991) 1453-1468.
  • [6] T. Dzitkowski, A. Dymarek, Design and examining sensitivity of machine driving systems with required frequency spectrum, Journal of Achievements in Materials and Manufacturing Engineering 26/1 (2008) 49-56.
  • [7] J.Y. Kao, Y.S. Tarng, A Neural Network Approach for the On-Line Monitoring of the Electrical Discharge Machining Processes, Journal of Material Processing Technology 69 (1997) 112-119.
  • [8] J. Kopac, High precision machining on high speed machines, Journal of Achievements in Materials and Manufacturing Engineering 24/1 (2007) 405-412.
  • [9] D. Reclik, G. Kost, J. Świder, The signal connections in robot integrated manufacturing systems, Journal of Achievements in Materials and Manufacturing Engineering 26/1 (2008) 89-96.
  • [10] T.A. Spedding, Z.Q. Wang, Study on Modeling of Wire - EDM Process, Journal of Material Processing Technology 69 (1997) 18-28.
  • [11] Y.S. Tarng, S.C. Ma, L.K. Chung, Determination of Optimal Cutting Parameters in Wire- Eletrical Discharge Machining, International Journal of Machine Tools and Manufacture 35 (1995) 1435-1443.
  • [12] Y.S. Tarng, S.C. Ma, P.J. Wang, K.M. Tsai, Semi-Empirical model of Work Removal and Tool Wear in Electrical Discharge machining, Journal of Material Processing Technology 114 (2001) 89-96.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-PWA9-0042-0021
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