Knowledge Discovery and Management from Numerical Simulation and its Application to Robust Optimization of Extrusion-Forging Processing

Abstract:

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Numerical simulation technology has been used widely in plastic forming area. However, the simulation of increasingly complex forming process leads to the generation of vast quantities of data, which implies much useful knowledge. Consequently domain knowledge is very significant to product design and process development in metal plastic forming area. The paper presented a new robust optimization method based on knowledge discovery from numerical simulation. Firstly, the knowledge discovery model from numerical simulation is established. In this model, interval-based rule presentation is adopted to describe the uncertainty of design parameters quantitatively to enhance the design robustness. Secondly, the optimization process based on knowledge discovery and management is presented, and genetic arithmetic is used to obtain the robust optimization parameter. Finally, the application to robust optimization of extrusion-forging processing is analyzed to show the scheme to be effective. The proposed method can overcome the pathologies in simulation optimization and improve the efficiency & robustness in design optimization.

Info:

Periodical:

Key Engineering Materials (Volumes 340-341)

Edited by:

N. Ohno and T. Uehara

Pages:

659-664

DOI:

10.4028/www.scientific.net/KEM.340-341.659

Citation:

H. Jie and J. L. Yin, "Knowledge Discovery and Management from Numerical Simulation and its Application to Robust Optimization of Extrusion-Forging Processing", Key Engineering Materials, Vols. 340-341, pp. 659-664, 2007

Online since:

June 2007

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$35.00

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