Abstract
A software concept based on data mining and knowledge discovery for the configuration and optimisation of a multi-spindle drilling gear machine used in a furniture production process is proposed. The objective is to find the minimum number of supports and the optimised configuration of the multi-spindle drilling gears. Intelligent analysis of input data and an automated system covering the human design procedure are applied to configure multi-drilling gears. The input data in the form of digitised customerengineering drawings together with technology data describing the basic constraints of the machine construction are presented as system inputs. Additionally the transfer of acquired manual design experience from a human expert to a software strategy to solve the multi-criteria optimisation problem is shown.
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References
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Klene, G., Grauel, A., Convey, H.J., Hartley, A.J. (2002). Automated Design of Multi-Drilling Gear Machines. In: Zimmermann, HJ., Tselentis, G., van Someren, M., Dounias, G. (eds) Advances in Computational Intelligence and Learning. International Series in Intelligent Technologies, vol 18. Springer, Dordrecht. https://doi.org/10.1007/978-94-010-0324-7_29
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DOI: https://doi.org/10.1007/978-94-010-0324-7_29
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-010-3872-0
Online ISBN: 978-94-010-0324-7
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