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Tytuł artykułu

Intelligent control system for HSM

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Next-generation of High-Speed Machining (HSM) systems demand advanced features such as intelligent control under uncertainty. This requires, in turn, an efficient administration and optimization of all system's resources towards a previously identified objective. This work presents an optimization system based on Markov Decision Process (MDP), where an intelligent control guides the actions of the operator in peripheral milling processes. Early results suggest that MDP framework can cope with this application, yielding several benefits, which are discussed in detail. Future work will address the full integration of the developed optimization scheme within a commercial machining center.
Twórcy
autor
  • Associate Director of Research, Tecnológico de Monterrey. Tecnológico de Monterrey, Campus Monterrey, Av. E Garza Sada # 2501 Sur; 64,849 Monterrey; NL México, avallejo@itesm.mx
Bibliografia
  • [1] Balic J., Intelligent CAD/CAM Systems for CNC Programming - An Overview. Advances in Production Eng & Management, no. 1, 2006, pp. 13-22.
  • [2] Carpenter I., Maropoulos P., “Automatic Tool Selection for Milling Operations. Part 1: Cutting Data Generation”. In: Proc. Instn. Mech. Engrs. 214, 2000 pp. 271-282.
  • [3] Dereli T., Filiz I., Baykasoglu A., “Optimizing Cutting Parameters in Process Planning of Prismatic Parts by using Genetic Algorithms”,Int. J. Prod. Research , vol. 39, no. 15, 2001, pp. 3303-3328.
  • [4] Feldman R.M., Valdez-Flores C.,Applied Probability and Stochastic Processes, Thomson Brooks/Cole, 511 Forest Lodge Road, Pacific Grove, CA 93950, 1 Edition, 2004.
  • [5] Jawahir I., Wang X., “Development of Hybrid Predictive Models and Optimization Techniques forMachining Operations”,Journal of Materials Processing Technology , no. 185, 2007, pp. 46-59.
  • [6] Monostori L., “Intelligent Machines”. In:Proc. of 2 Conf. on Mechanical Eng., Hungary, 2000, pp. 24-36.
  • [7] Mukherjee I., Ray P.K., “A Review of Optimization Techniques in Metal Cutting Processes”,Computers and Industrial Engineering, no. 50, 2006, pp. 15-34.
  • [8] Mursec B., Cus F., “Integral Model of Selection of Optimal Cutting Conditions from Different Databases of Tool Makers”, J. of Materials Processing Technology, no. 133, 2003, pp. 158-165.
  • [9] Palanisamy P., Rajendran I., Shanmugasundaram S.,“Optimization of Machining Parameters Using Genetic Algorithm and Experimental Validation for End-Milling Operations”, Int. J. Adv. Manuf. Technol., no. 32, 2007, pp. 644-655.
  • [10] Suresh P.V.S., Venkateswara P, Deshmukh S.G., “A Genetic Algorithm Approach for Optimization of Surface Roughness Prediction Model”,Int. J. of Machine Tools and Manufacture, no. 42, 2002, pp. 675-680.
  • [11] Tansel I.N., Ozcelik B., Bao W.Y., Chen P., Rincon D., Yang S.Y., Yenilmez A., “Selection of Optimal Cutting Conditions by Using GONNS”.Int. J. of Machine Tools and Manufacture, no. 46, 2006, pp. 26-35.
  • [12] Tzeng Y., Chen, F., “Optimization of High Speed CNC Milling Process Using Two-Phase Parameter Design Strategy by the Taguchi Methods”, JSME Int. J. Series C, vol. 48, no. 4, 2005, pp. 775-783.
  • [13] Vallejo A., Nolazco-Flores J., Morales-Menendez R., Sucar L., Rodríguez C., “Tool-Wear Monitoring Based on Continuous Hidden Markov Models”. In:10 Iberoamerican Congress on Pattern Recognition, 2005, pp: 880-890.
  • [14] Vallejo A., Morales-Menendez R., and Alique J.R.,Designing a Cost-effective Supervisory Control System for Machining Processes, In: IFAC-CEA Monterrey México, IFACPapersOnline.net. Available: October 2007.
  • [15] Vallejo A., Morales-Menendez R., Alique J.R., “Intelligent Monitoring and Decision Control System for Peripheral Milling Process”. In:IEEE Int. Conf. on Systems, Man, and Cybernetics, Singapore, 2008, pp. 1620-1625.
  • [16] Vallejo A., Morales-Menendez R.,Decision Control System for HSM. In: IFAC-IMS, Szczecin, Poland, IFACPapersOnLine.net. Available: October 2008.
  • [17] Vallejo A., Morales-Menendez R., Elizalde-Siller H.,Surface Roughness Modeling in Peripheral Milling Processes, to appear in NAMRC 37 USA, May 2009.
  • [18] Wong E., Sridharan S., “Comparison of Linear Prediction Cepstrum Coefficients and Mel-Frequency Cepstrum Coefficients for Language Identification”. In:Proc. of Int. Symp. on Intelligent Multimedia, Video and Speech Pro-cessing , pp. 95-98.
  • [19] Yih-Fong T., “A Hybrid Approach to Optimize Multiple Performance Characteristics of High-Speed Computerized Numerical Control Milling Tool Steels”,Materials and Design , 2005, p. 110.
  • [20] Zuperl U., Cus F., Mursec B., Ploj T., “A Hybrid Analytical-Neural Network Approach to the Determination of Optimal Conditions”,J. of Materials Processing Technology, no. 157-158, 2004, pp. 82-90.
  • [21] Zuperl U., Cus F., and Kiker E., “Intelligent Adaptive Cutting Force Control in End-Milling”, Technical Gazette, vol. 13, no.1-2, 2006, pp. 15-22.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUJ5-0025-0007
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