Abstract
Reduced machining time and increased accuracy for a sculptured surface are both very important when producing complicated parts, so, the step-size and tool-path interval are essential components in high-speed and high-resolution machining. If they are too small, the machining time will increase, whereas if they are too large, rough surfaces will result. In particular, the machining time, which is a key factor in high-speed machining, is affected by the tool-path interval more than the step size. The present paper introduces a ‘system software’ developed to reduce machining time and increased accuracy for a sculptured surface with Non-Uniform Rational B-Spline (NURBS) patches. The system is mainly based on a new and a powerful artificial intelligence (AI) tool, called artificial immune systems (AIS). It is implemented using C programming language on a PC. It can be used as stand alone system or as the integrated module of a CNC machine tool. With the use of AIS, the impact and power of AI techniques have been reflected on the performance of the tool path optimization system. The methodology of the developed tool path optimization system is illustrated with practical examples in this paper.
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Ada G.L. and Nossal G.J.V. (1987). The clonal selection theory. Scientific American, 257(2): 50–57
Aekambaram R. and Raman S. (1999). Improved tool path generation, error measures and analysis for sculptured surface machining. International Journal of Production Research, 37(2): 413–431
Balic J. and Korosec M. (2002). Intelligent tool path generation for milling of free surfaces using neural networks. International Journal of Machine Tools & Manufacture, 42: 1171–1179
De Castro, L. N., & Von Zuben, F. J. (1999). Artificial immune systems: Part I—Basic theory and applications. DCA-RT 02/00.
De Castro L.N. and Von Zuben F.J. (2001). Immune and neural network models: Theoretical and empirical comparisons. International Journal of Computational Intelligence and Applications, 1(3): 239–257
Dereli T., Filiz I.H. and Baykasoglu A. (2001). Optimizing cutting parameters in process planning of prismatic parts by using genetic algorithms. International Journal of Production Research, 39(15): 3303–3328
Ding S., Mannan M.A., Poo A.N., Yang D.C.H. and Han Z. (2003). Adaptive iso-planar tool path generation for machining of free-form surfaces. Computer-Aided Design, 35: 141–153
Dragomatz D. and Mann S. (1997). A classified bibliography of literature on NC milling path generation. Computer-Aided Design, 29(3): 239–247
Feng H.Y. and Teng Z. (2005). Iso-planar piecewise linear NC tool path generation from discrete measured data points. Computer-Aided Design, 37: 55–64
Forrest, S., Perelson, A. S., Allen, L., & Cherukuri, R. (1994). Self–Nonself Discrimination in a computer. In Proceedings of IEEE Symposium on Research in Security and Privacy (pp. 202–212). CA: IEEE Computer Society Press.
Jee, S., & Koo, T. (2003). Tool-path generation For NURBS surface machining. IEEE Proceedings of the American Control Conference, 2614–2619.
Jimeno A., Sa’nchez J.L., Mora H., Mora J. and Garcý’a-Chamizo J.M. (2006). FPGA-based tool path computation: An application for shoe last machining on CNC lathes. Computers in Industry, 57: 103–111
Lee S.G. and Yang S.-H. (2002). CNC Tool-path planning for high-speed high-resolution machining using a new tool-path calculation algorithm. International Journal of Advanced Manufacturing Technology, 20: 326–333
Lo C-C. (1998). A new approach to CNC tool path generation. Computer-Aided Design, 30(8): 649–655
Lo C-C. (2000). CNC machine tool surface interpolator for ball-end milling of free-form surfaces. International Journal of Machine Tools & Manufacture, 40: 307–326
Loney G.C. and Ozsoy T.M. (1987). NC machining of free form surfaces. Computer-Aided Design, 19(2): 85–90
Peng Y.H. and Yin Z.W. (2005). A new strategy for direct tool path generation from measured point. International Journal of Production Research, 43(5): 933–944
Perelsen A.S. and Oster G.F. (1979). Theoretical studies of clonal selection: Minimal antibody repertuarie size and reliability of self–nonself discrimination. Journal of Theoretical Biology, 81: 645–670
Tandon V., El-Mounayri H. and Kishawy H. (2002). NC end milling optimization using evolutionary computation. International Journal of Machine Tools & Manufacture, 42: 595–605
Tournier C. and Duc E. (2002). A surface based approach for constant scallop height tool-path generation. International Journal Advanced Manufacturing Technology, 19: 318–324
Ülker E. and Arslan A. (2006). The calculation of parametric NURBS surface interval values using neural networks. Lecture Notes in Computer Science, 3992: 247–254
Ülker E. and Isler V. (2007). An artificial immune system approach for B-spline surface approximation problem. Lecture Notes in Computer Science, 4488: 49–56
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Ülker, E., Emin Turanalp, M. & Selçuk Halkaci, H. An artificial immune system approach to CNC tool path generation. J Intell Manuf 20, 67–77 (2009). https://doi.org/10.1007/s10845-008-0104-6
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DOI: https://doi.org/10.1007/s10845-008-0104-6