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Design of Multi-drilling Gear Machines by Knowledge Processing and Machine Simulation

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Intelligent Data Engineering and Automated Learning — IDEAL 2002 (IDEAL 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2412))

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Abstract

A software concept for automated design of a multi-spindle drilling gear machine used in furniture production process is proposed. To find an optimised design of the target-machine, this means to find the minimum number of supports and gears as well as the optimised configuration of the multi-spindle drilling gears, an automated system based on pattern identification, knowledge discovery and automated decision process is explained. The transfer of acquired manual design experience from the human expert to a software strategy to solve the multi-criteria optimisation problem will achieve cost reductions during the machine design.

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Author information

Authors and Affiliations

  1. Soest Campus, Unit 16, Department Mathematics, University of Applied Sciences, Steingraben 21, D-59494, Soest, Germany

    G. Klene & A. Grauel

  2. Faculty of Technology, Technology Development Unit, Bolton Institute of Higher Education, Deane Campus, Bolton, BL3 5AB, England

    H. J. Convey & A. J. Hartley

Authors
  1. G. Klene
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  2. A. Grauel
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  3. H. J. Convey
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  4. A. J. Hartley
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Editor information

Editors and Affiliations

  1. Department of Electrical Engineering and Electronics, UMIST, Manchester, M60 1QD, UK

    Hujun Yin , Nigel Allinson  & Richard Freeman ,  & 

  2. Department of Computation, UMIST, Manchester, M60 1QD, UK

    John Keane

  3. Department of Biomolecular Science, UMIST, Manchester, M60 1QD, UK

    Simon Hubbard

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© 2002 Springer-Verlag Berlin Heidelberg

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Klene, G., Grauel, A., Convey, H.J., Hartley, A.J. (2002). Design of Multi-drilling Gear Machines by Knowledge Processing and Machine Simulation. In: Yin, H., Allinson, N., Freeman, R., Keane, J., Hubbard, S. (eds) Intelligent Data Engineering and Automated Learning — IDEAL 2002. IDEAL 2002. Lecture Notes in Computer Science, vol 2412. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45675-9_16

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  • DOI: https://doi.org/10.1007/3-540-45675-9_16

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44025-3

  • Online ISBN: 978-3-540-45675-9

  • eBook Packages: Springer Book Archive

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