Intelligent approach for optimal modeling of manufacturing systems

• Autorzy: Gecevska V. , Cus F. , Lombardi F. , Dukowski V. , Kuzinowski M.
• Języki publikacji: EN

Abstrakty

EN

Purpose: This paper proposes a methodology for analysis and modeling of machining conditions by optimal determination of the cutting parameters in multi-pass NC machining operations. Design/methodology/approach: This paper proposes optimal determination of the cutting parameters by using a deterministic method (DM) and a genetic algorithm (GA). In the research, it is created the complex mathematical model for design of the cutting condition for machining process. In next phase, it is created a numerical algorithm for optimization and its developed software called OPTIMAD (Optimization of Milling and Drilling), by using DM. Also, it is created software, caled GAMO (Genetic Algorithm for Machining Operation), as a GA program modul based of the elementary pseudo-code for GA, with using the MatLAB program language and C++ developed rutines. Findings: Modeling of optimal cutting parameters, as a part of process planning, enables generating of manufacturing data and knowledge representation in machining process plan. Verification of optimized cutting parameters in real machining condition has done confirmation for design of cutting parameters by virtual modelling, using optimization methodologies OPTIMAD and GAMO. Research limitations/implications: The optimization approach is proposed and its uses optimization of mathematical model using a classic and heuristic methods. In this research, GA based optimization method and deterministic optimization method are developed and there implementations into real manufacturing process are analyzed. Practical implications: Use of proposed approach resulted in improved productivity and efficiency of machining process where the cutting conditions are designed by OPTIMAD and GAMO softwares. In the future, this results will be integrated in computer system for process planning. Originality/value: The paper describes a method for eliminating the need for using the extensive user intervention in CAM processes, during determination of cutting parameters.

Słowa kluczowe

EN

analysis; modelling; technological design; CAMS; optimisation; NC machining process

PL

analiza; modelowanie; projekt technologiczny; CAMS; optymalizacja; obróbka skrawaniem

• Wydawca: International OCSCO World Press
• Czasopismo: Journal of Achievements in Materials and Manufacturing Engineering
• Rocznik: 2006
• Tom: Vol. 14, nr 1-2
• Strony: 97--103
• Opis fizyczny: Bibliogr. 9 poz., rys., tab., wykr.

Twórcy

autor

Gecevska V.

• Faculty of Mechanical Engineering, University Ss. Cyril and Methodius, Karpos II bb, PO Box 464, 1000 Skopje, Macedonia

autor

Cus F.

• Faculty of Mechanical Engineering, University of Maribor, Smetanova 17, 1200 Maribor, Slovenia

autor

Lombardi F.

• Politehnico di Torino, Corso Deglli Abruzi 15, Turin, Italy

autor

Dukowski V.

• Faculty of Mechanical Engineering, University Ss. Cyril and Methodius, Karpos II bb, PO Box 464, 1000 Skopje, Macedonia

autor

Kuzinowski M.

• Faculty of Mechanical Engineering, University Ss. Cyril and Methodius, Karpos II bb, PO Box 464, 1000 Skopje, Macedonia

Bibliografia

• [1] N.Alberti, G.Perrone: Multipass machining optimization by using fuzzy possibilistic programming and genetic algorithms, Int. J. of Mechanical Engineering, Vol. 213, 2000, 261-273.
• [2] F. Cus., U. Zuperl: Approach to Optimization if Cutting conditions by using artificial neural networks, Journal of Materials Processing Technology, online Jan.2006, pp.10, http://dx.doi.org/10.1016/j.jmatprotec.2005.04.123.
• [3] T.Chang: Expert Process Planning for Manufacturing, Addison-Wesley, CA., 1990.
• [4] C. Grabowik, R. Knosala: The method of knowledge representation for a CAPP system, Journal of Materials Processing Technology, 2003, vol.133, iss. 1/2, pp. 90-98.
• [5] D. Sormaz: Intelligent Manufacturing Based on Generation of Alternative Process Plans, Proceedings of 9th Int. Conference on Flexible Automation and Intelligent Manufacturing, Tilburg, 1999.
• [6] B. Mursec, F. Cus, J. Balic: Integral model of selection of optimal cutting conditions from different databases of toolmakers, Elsevier Journal of Materials Processing Technology, 2003, vol.133, pp. 158-165.
• [7] J.R. Koza: Genetic Programming II, The MIT Press, Massachusetts, 1994.
• [8] Z. Michalewicz: Genetic Alg. + Data Algorithms + Data Structures = Evolution Programs, Springer-Verlag, 1996.
• [9] V. Gecevska, F. Cus, M. Kuzinovski, U. Zuperl: Evolutionary Computing with Genetic Algorithm in Manufacturing Systems, Journal of Machine Engineering, 2005, vol.5, no 3/4, pp. 188-198.