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Hybrid system for modeling and optimization of production chain in metal forming

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper presents design and implementation of the hybrid system, which is the part of investigation focused on application of multiscale modeling in simulation of real industrial processes. The hybrid system is dedicated to support production processes based on metal forming, by using artificial intelligence and optimization algorithms. The proposed system is based on the multilayer architecture and consists of several functional components responsible for management of production process, modeling and simulations and, finally, optimization. The latter module aims at searching for optimial parameters of selected production processes, which form production chain, i.e. rod rolling and cold forging. Optimization is performed using results obtained from multiscale modeling calculations. Then, the optimized approach is passed directly to the configuration and control centre of the real industrial process as a feedback to obtain better quality of products employing lower costs of manufacturing. Moreover, the hybrid system is designed to exchange information with other external systems implemented inside an enterprise e.g. ERP and its modules. The internal structure of presented system is described in the paper, as well as measurable advantages of hybrid system application to real environment.
Słowa kluczowe
Rocznik
Strony
14--22
Opis fizyczny
Bibliogr. 19 poz., rys.
Twórcy
autor
  • AGH University of Science and Technology, Krakow, Poland
autor
  • AGH University of Science and Technology, Krakow, Poland
autor
  • AGH University of Science and Technology, Krakow, Poland
Bibliografia
  • [1] BEYNON J.H., DAS S., HOWARD I.C., PALMIER E.J., SHTERENLIKHT A., The combination of cellular automata and finite elements for the study of fracture; the CAFE model of fracture., Proc. Conf., ECF14, eds, Neimitz A., Rokach I.V., Kocańda D., Gołoś K., Krakow, 2000, 241-248.
  • [2] CHENG K., HARRISON D.K., PAN P.Y., Implementation of agile manufacturing - Al and Internet based approach, Journal of Material Processing Technology 76, 1998, 96-101.
  • [3] GIACHETTI R.E., A decision support system for material and manufacturing process selection, Journal of Intelligent Manufacturing 9. 1998, 265-276.
  • [4] HALEVI G., WANG K., Knowledge based manufacturing system (KB MS), Journal of Intelligent Manufacturing 18, 2007, 467-474.
  • [5] HIRT G., KOPP R., HOFFMAN O. FRANZKE G., Implementing High Accuracy Multimesh Method for Incremental Bulk Metal Forming, Annals of the CIRP, 57, 2007, 313-316.
  • [6] MADEJ L. HODGSON P.D., PIETRZYK M., Multi scale rheological model for discontinuous phenomena in materials under deformation conditions, Comput. Mater. Sci., 38, 2007, 685-691.
  • [7] MADEJ L., KUZIAK R., PIETRZYK M., Validation of the History Dependent Constitutive Law under Varying Conditions of Hot Deformation, Proc. 6th ESAFORM Conf., ed., Brucato V., Salerno, 2003, 507-511.
  • [8] MADEJ L., MROZEK A., BURCZYŃSKI T., PIETRZYK M., Multi Scale Modelling. Multi-Physics Phenomena and Evolving Discontinuities in Metal Forming, Computer Methods in Materials Science, 7, 2007, (in press)
  • [9] MAHL A., KRIKLER R., Approach for a rule based system for capturing and usage of knowledge in the manufacturing industry, Journal of Intelligent Manufacturing 18, 2007, 519-526.
  • [10] MCKAY K.N., BLACK G.W., The evolution of a production planning system: A J 0-year case study, Computers n Industry 58, 2007, 756-77 L
  • [11] PIETRZYK M., Identification of Parameters in the History Dependent Constitutive Model for Steels, Ann. CIRP, 50,2001.161-164,
  • [12] PIETRZYK M., KUZIAK R., Development of the Constitutive Law for Microalloyed Steels Deformed in the Two- Phase Range of Temperatures, Steel GRIPS, 2, 2004, 465-470.
  • [13] PIETRZYK M., LENARD J.G., DALTON G.M., 4 Study of the Plane Strain Compression Test, Annals CIRP,42, 1993, 331-334.
  • [14] PIETRZYK M., PIDVYSOTSKYY V., PACKO M., Flow Stress Model Accounting for the Strain Localization during: Plastic Deformation of Metals, Ann. CIRP, 53, 2004, 235-238.
  • [15] RAUCH L, KUSIAK J., Data filtering using dynamic particles method, Computer Assisted Mechanics and Engineering Sciences, 14, 2007, 353-360.
  • [16] ROUCOULES C., PIETRZYK M., HODGSON P.D., Analysis of work hardening- and recrystallization during the hot working of steel using a statistically based Internal Variable Method, Mat. Sci. Eng., A339, 2003, 1-9.
  • [17] STANISLAWCZYK A., TALAR J., JAROSZ P., KUSIAK J., Application of dynamic artificial neural networks to modelling of the copper flash smelting process, Computer Methods in Materials Science 6, no. 2, 2006,
  • [18] SZELIGA D., GAWAD J., PIETRZYK M., Inverse Analysis for Identification of Rheological and Friction Models in Metal Forming, Comp. Meth. Appl. Mech. Engrg. 195, 2006, 6778-6798.
  • [19] STANISLAWCZYK A., KUSIAK J., KUZIAK R., Testowy system optymalizacji cyklu produkcji w przeróbce plastycznej, Proc. 15th Conf. KomPlasTech, Korbielow, 2008, (in press).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b5a85fca-c217-4a3f-a498-8ed7043c0132
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