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

Abstract:

Article Preview

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

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

Authors:

Export:

Price:

$38.00

[1] U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy: Advances in Knowledge Discovery in Databases (MIT Press, Cambridge, Mass. 1996).

[2] D. Braha: Data Mining for Design and Manufacturing: Methods and Applications (Kluwer academic publishers 2001).

[3] L.G. Robert: Data Mining for Scientific and Engineering Applications (Kluwer Academic Publishers 2001).

[4] J. Yu, and K. Ishii. Robust design by matching the design with manufacturing variation patterns. In: ASME Design Automation Conference. September, Minneapolis, MN,. DE-Vol. 69-2 (1994), pp.7-14.

[5] B.R. Cho, Y.J. Kim, D.L. Kimbler, and M.D. Phillips. An integrated joint optimization procedure for robust and tolerance design. International Journal of Production Research, Vol. 38, No. 10 (2000), pp.2309-2325.

DOI: https://doi.org/10.1080/00207540050028115

[6] K.L. Tsui. Robust design optimization for multiple characteristic problems. International Journal of Production Research, Vol. 37, No. 2 (1999), pp.433-445.

[7] F.C. Wu, and C.C. Chyu. Optimization of robust design for multiple quality characteristics. International Journal of Production Research, Vol. 42, No. 2 (2004), pp.337-354.

[8] A. Jiju: Design of Experiments for Engineers and Scientists (Butterworth Publishers 2003).

Fetching data from Crossref.
This may take some time to load.