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Intelligent modelling in manufacturing

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Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Purpose: Modeling of production systems is very important and makes optimization of complicated relation in production system possible. The purpose of this paper is introducing artificial techniques, like Genetic Algorithms in modelling and optimization of job shop scheduling in production environment and in programming of CNC machine tools. Design/methodology/approach: Conventional methods are not suitable for solving such complicated problems. Therefore Artificial Intelligent method was used. We apply Genetic Algorithm method. Genetic Algorithms are computation methods owing their power in particular to autonomous mechanisms in biological evolution, such as selection, "survival of the fittest" (competition), and recombination. Findings: In example solutions are developed for an optimization problem of job shop scheduling by natural selection. Thus no explicit knowledge was required about how to create a good solution: the evolutionary algorithm itself implicitly builds up knowledge about good solutions, and autonomously absorbs knowledge. CNC machining time was significant shorter by using GA method for NC programming. Research limitations/implications: The system was developed for PC and tested in simulation process. It needs to be tested more in detail in the real manufacturing environment. Practical implications: It is suitable for small and medium-sized companies. Human errors are avoid or at lower level. It is important for engineers in job - shops. Originality/value: The present paper is a contribution to more intelligent systems in production environment. It used genetic based methods to solve engineering problem.
Rocznik
Strony
340--349
Opis fizyczny
Bibliogr. 33 poz., fot., rys., tab.
Twórcy
autor
autor
  • University of Maribor, Faculty of Mechanical Engineering, Smetanova 17, SI-2000, Maribor, Slovenia, joze.balic@uni-mb.si
Bibliografia
  • [1] M. Brezocnik, J. Balic, A genetic programming approach for modeling of self-organizing assembly systems, Proceedings of the IAD' 98, IFAC workshop, Bled, Slovenia, 1998.
  • [2] J.N. Holland, Adaptation in natural and artificial systems, University of Michigan, 1975.
  • [3] D.E. Goldberg, Genetic algorithms in search, optimization, and machine learning, Addison-Wesley, Reading, Massachusetts, 1989.
  • [4] M. Brezocnik, J. Balic, Comparison of genetic programming with genetic algorithm, Proceedings of the DMMI’97, Portoroz, Slovenia, 1997.
  • [5] F. Čus, M. Milfelner, U. Župerl, Sodobne metode optimiranja v proizvodnih procesih 21. znanstvena konferenca o razvoju organizacijskih ved, Portorož, Management in Evropska unija 2 (2002) 820-829.
  • [6] A. Levy, Artificial Life, New York: Pantheon, 1992.
  • [7] P. Gray, Alan Turing, Time 153 (1999) 147-150.
  • [8] R. Kurzweil, The paradigms and paradoxes of intelligence, Pt 2: The Church-Turing thesis, Library Journal 117 (1992) 73-74.
  • [9] G. Renner, A. Ekárt, A. Genetic algorithms in computer aided design, Computer-Aided Design 35 (2003) 709-726.
  • [10] R. Rosso, R. Allen, S. Newman, Future issues for CAD/CAM and intelligent CNC manufacture, from www.staff.lboro.ac.uk 2004.
  • [11] T. Matsumura, T. Obikawa, T. Shirakashi, E. Usui, Autonomous turning operation planning with adaptive prediction of tool wear and surface-roughness, Journal of Manufacturing Systems 12 (1993) 253-262.
  • [12] M. Kadono, Tool path data generation apparatus for NC machine tool and numerical controller, provided with: Patent Nr US2001/0000805 A1, 2001.
  • [13] M. Kovacic, J. Balic, Evolutionary programming of a CNC cutting machine, International journal of advanced manufacturing technology 22 (2003) 118-124.
  • [14] B. Abersek, J. Flasker, J. Balic, Expert system for designing and manufacturing of a gear box, Expert systems with applications 11 (1996) 397-405.
  • [15] J. Balic, B. Abersek, Model of an integrated intelligent design and manufacturing system, Journal of intelligent manufacturing 4 (1997) 263-270.
  • [16] J. Balic, M. Korosec, Intelligent tool path generation for milling of free surfaces using neural networks, International journal of machine tool and manufacture 42 (2002) 1171-1179.
  • [17] T. Kamioka, H. Mochizuki, Learning promotion method on tool and learning promotion type machine, Patent JP2001034155, 2001.
  • [18] K. Nemoto, M. Kyoichi, H. Yamaguchi, H. Sugimoto, H.; Hasegawa, NC data generation device and its method, Patent JP11242510, 1999.
  • [19] C.J. Chiou, Y.S. Lee, A machining potential field approach to tool path generation for multi-axis sculptured surface machining, Computer-Aided Design 34 (2002) 357-371.
  • [20] J. Steven, I. Liang, L. Rogelio, L. Heker, R. Landers, Machining process monitoring and control: the state-of-the-arte research, Proceedings of the IMECE 2002, ASME International mechanical Engineering Congress & Exposition, New Orleans, 2002.
  • [21] K. Meissner, Anwendung Genetischer Algorithmen zur Optimierung von Fertigungsprozessen, Proceedings of the 5th International DAAAM Symposium, Maribor, 1994.
  • [22] V. Tandon, H. El-Mounayri, H. Kishawy, NC end milling optimization using evolutionary computation, International journal of machine tool and manufacture 42 (2002) 595-605.
  • [23] K. Kato, T. Momochi, Numerical controller for machining tool with learning function - combines learning program with entered program to produce resulting processing, Patent DE4011591, 1998.
  • [24] J. Balic, CNC control unit with learning ability for machining centers, Patent SI 21200 A, Patent application US 2003/0187624 A1, 2003.
  • [25] Y. Liu, L. Zuo, T. Cheng, C. Wang, Development of an open parallel intelligent CNC milling system: Part 1, System structure, International Journal of Advanced Manufacturing Technology 16 (2000) 537-541.
  • [26] Y. Liu, C. Wang, Neural network adaptive control and optimization in the milling process, International Journal of Advanced Manufacturing Technology 15 (1999) 791-795.
  • [27] I. Chang, J. Deng, S. Chan, A next generation machining system based on NC feature unit and real-time tool path generation, International Journal of Advanced Manufacturing Technology 16 (2000) 889-901.
  • [28] I. Drstvensek, M. Brezocnik, J. Balic, GA work operation determination based on feature recognition, Annals of DAAAM for 1999, Proceedings of the l0th International DAAAM Symposium, Vienna University of Technology, (1999) 129-130.
  • [29] I. Drstvensek, I. Pahole, J. Balic, A model of data flow in lower CIM levels, Journal of Materials Processing Technology 157-158 (2004) 123-130.
  • [30] I. Drstvensek, M. Brezocnik, CAP integration interface based on GA work determination operation, Proceedings of the 8th International Scientific Conference "Achievements in Mechanical and Materials Engineering" AMME'99, Gliwice-Rydzyna-Pawłowice-Rokosowo, Poland, (1999) 189-192.
  • [31] J. Balic, M. Kovacic, B. Vaupotic, Intelligent programming of CNC turning operations using genetic algorithm, Journal of Intelligent Manufacturing 17/3 (2006) 331-340.
  • [32] J. Balic, Model of automated computer aided NC machine tools programming. Journal of Achievements in Materials and Manufacturing Engineering 17 (2006) 309-312.
  • [33] J. Balic, Intelligent CAD/CAM systems for CNC programming - an overview. Advanced Product Engineering Management 1/1 (2006) 13-22.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BOS5-0021-0015
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