An Integrated RS and ANN Design Method for Product Agile Customization

Abstract:

Product agile customization design is an effective technological measure to win the customers and improve development efficiency. It needs designer to determine product structure quickly according to customer’s customized requirements. In this paper, a novel design method of product agile customization is presented by integrating rough set (RS) theory and artificial neural network (ANN) in the design process. In the method, design demands are reduced so as to form effective decision conditions by applying RS, and on that basis ANN models between design demands of different design stages and corresponding product structures are established so as to determine product structural styles quickly by applying ANN. Finally, this method is applied to the general schematic design process of a roll plate machine’s customization, and its validity is verified.

• Periodical: Key Engineering Materials (Volume 458)
• Pages: 212-217
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.458.212
• Online since: December 2010
• Authors: Chang Feng Yuan, Wan Lei Wang, Yan Chen
• Keywords: Agile Customization Design, Artificial Neural Network (ANN), Rough Set (RS)

References

[1] T. Y. Gao, W. Sun and Q. Y. Ma: Computer Engineering and Application Vol. 43 (2007), pp.20-21, 44.

[2] J. Jelonek, K. Krawiec and R. Slowiński: Computational Intelligence Vol. 11 (2007), pp.339-347.

[3] D. Alisantoso, L. P. Khoo: International Journal of Systems Science Vol. 40 (2009), pp.121-130.

[4] W. L Chan, M. W. Fu: Engineering Application of Artificial Intelligence Vol. 21 (2008), pp.1170-1181.

[5] W.W. Chen, Q. Zhang, X. P. Wei, T. T. Zhao and X. Y. Zhao: Computer Engineering and Design Vol. 30 (2009), pp.4290-4292, 4302.

[6] Y. Li, L. Y. Wang: International Journal of Computer Applications in Technology Vol. 33 (2008), pp.247-254.

[7] W. Deng, H. M. Zhao, X. H. Yang and X. Z. Mi: Journal of Machine Design Vol. 26 (2009), pp.67-70.

[8] Z. B. Gong, D. B. Li and Y. Y. Pan: Computer Integrated Manufacturing Systems Vol. 12 (2006), pp.1198-1202.

[9] Y.C. Lin, C. H. Yeh and C. H. Hung: NCM 2008: 4th International Conference on Networked Computing and Advanced Information Management Vol. 1 (2008), pp.53-57.

[10] J. J. Ding, S. L. Tan, X. F. Song, and Y. J. Sun: Journal of Engineering Design Vol. 14 (2007), pp.199-203.

[11] Q. S. Xie, J. Ying and L. K. Luo: China Machine Press, (2003).

[12] Application of Artificial Neural Network in ARM Platform: http: /www. eda121. com/arm/jcarm/200709/693. html, (2007).