Optimization of Non-Cutting Tool Paths

Article Preview

Abstract:

The focus of CAM systems is on effectively creating cutting tool paths. However, collision risk is very high on multi axes machines when performing non-cutting traverse moves. If available, CAM systems offer limited setting options for non-cutting tool moves. In this paper an approach is presented that allows to automatically generating non-cutting tool paths. Process planners will not only be released from developing and simulating time-consuming multi axes traverse moves. The automatically calculated traverse moves will also machine-specifically optimized with respect to various optimization criteria.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

911-917

Citation:

Online since:

April 2011

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2011 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] B. Denkena and C. Ammermann: CA-Technologien in der Fertigungs- und Prozessplanung, in: ZWF, 104, 4 (2009), pp.300-305.

DOI: 10.3139/104.110050

Google Scholar

[2] B. Denkena and C. Ammermann: Modular Numerical Control Model Including Realistic Motion Planning, in: The IFIP WG5.1 7th International Conference on Product Lifecycle Management (2010)

Google Scholar

[3] Z. Yazar, K.-F. Koch, T. Merrick and T. Altan: Feed rate optimization based on cutting force calculations in 3-axis milling of dies and molds with sculptured surfaces, in: International Journal of Machine Tools and Manufacture (34) (1994), pp.365-377.

DOI: 10.1016/0890-6955(94)90006-x

Google Scholar

[4] Y. Altintas and S.D. Merdol: Virtual High Performance Milling, in: CIRP Annals – Manufacturing Technology, 56, 1, (2007), pp.81-84.

DOI: 10.1016/j.cirp.2007.05.022

Google Scholar

[5] P. Damm: Rechnergestützte Optimierung des 5-Achsen-Simulationsfräsens von Freiformflächen, Dissertation, Technische Universität Dortmund (2005).

Google Scholar

[6] B. Sencer, Y. Altintas and E. Croft: Feed optimization for five-axis CNC machine tools with drive constraints, in: International Journal of Machine Tools and Manufacture, 48, 7-8 (2008), pp.733-745.

DOI: 10.1016/j.ijmachtools.2008.01.002

Google Scholar

[7] J. Aurich, D. Biermann, H. Blum, C. Brecher, C. Carstensen, B. Denkena et al.: Modeling and simulation of process: machine interaction in grinding, in: Production Engineering, 3, (2009), p.111–120.

DOI: 10.1007/s11740-008-0137-x

Google Scholar

[8] G. Luger and W.A. Stubblefield: Artificial intelligence – Structures and strategies for complex problem solving, Addison Wesley Longman, Harlow (1998).

Google Scholar

[9] B. Denkena, H. Henning and L.-E. Lorenzen: Genetics and intelligence: new approaches in production engineering, in: Production Engineering, 4 (2010), pp.65-73.

DOI: 10.1007/s11740-009-0191-z

Google Scholar

[10] G. Schuh and S. Gottschalk: Production engineering for self-organizing complex systems, in: Production Engineering, 2 (2008), pp.431-435.

DOI: 10.1007/s11740-008-0120-6

Google Scholar

[11] K. Weinert, A. Zabel, H. Müller and P. Kersting: Optimizing of NC tool paths for five-axis milling using evolutionary algorithms on wavelets, in: GECCO '06, Proceedings of the 8th annual conference on Genetic and evolutionary computation. New York, NY, USA: ACM (2006), pp.1809-1816.

DOI: 10.1145/1143997.1144289

Google Scholar

[12] C.-H. Chu, C.-T. Lee, K.-W. Tien and C.-J. Ting: Efficient tool path planning for 5-axis flank milling of ruled surfaces using ant colony system algorithms, in: International Journal of Production Research, (2010), pp.1-18.

DOI: 10.1080/00207540903501720

Google Scholar

[13] K. Erkorkmaz and M. Heng: A heuristic feedrate optimization strategy for NURBS tool paths, in: CIRP Annals - Manufacturing Technology, 57, 1 (2008), pp.407-410.

DOI: 10.1016/j.cirp.2008.03.039

Google Scholar