Rapidly Planning Approach Based on CBR for AO of Mechanical Product

Article Preview

Abstract:

During the process of developing product, AO directly affects assembly efficiency and assembly quality for mechanical product. Usually, new mechanical product is developed according to the knowledge and experience. So it is very important how to utilize the knowledge and experience to plan the AO. This paper presents a planning approach based on CBR for AO of mechanical product. Firstly, a method based on hierarchy model represents AO cases. And we store the AO cases into the AO Case Library to accumulate the knowledge and experience of assembly process. Secondly, the Euclidean distance is applied to calculate the similar characteristic between the component of AO of Query (AOQ) and the component of the AO cases in the Library. When some distances are less than the value, the corresponding AO cases are selected to the next calculation. Thirdly, the Manhattan distance is applied to calculate the similar characteristic between AOQ and the selected AO cases. When the distance is minimal, the optimal AO case is obtained. The approach is applied successfully to plan AO for a part of aircraft’s wing.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 102-104)

Pages:

781-785

Citation:

Online since:

March 2010

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2010 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] C. Lu, J.Y.H. Fuh and Y.S. Wong: International Journal of Production Research, Vol. 44 (2006) pp.5037-5063.

Google Scholar

[2] J.L. Leila, D. Abhijit, C. Idelcio and M. Eduardo: IEEE Transactions on Electronics Packaging Manufacturing, Vol. 31 (2008), pp.51-60.

Google Scholar

[3] Y. Zhao, J. Sun and H. Tian: Aircraft Engineering and Aerospace Technology, Vol. 78 (2006) pp.326-330.

Google Scholar

[4] Hamid Ahmadian and Hassan Jalali: Mechanical Systems and Signal Processing, Vol. 21 (2007), pp.1041-1050.

Google Scholar

[5] A.A. Milani and M. Hamedi: Proceedings of the IEEE International Conference on Industrial Technology, (2008).

Google Scholar

[6] B. Wang, D.F. Liu, P. Wang and Q. S Xie: Applied Mechanics and Materials, Vols. 10-12 (2008), pp.435-439.

Google Scholar

[7] T.Y. Dong, R.F. Tong, L. Zhang and J.X. Dong: International Journal of Advanced Manufacturing Technology, Vol. 32 (2007), pp.1232-1244.

Google Scholar

[8] H.J. Qiu, H. Tao, B.T. Yang and X.B. Gao: Materials Science Forum, Vols. 532-533 (2006), pp.640-643.

Google Scholar

[9] Nestor Rychtyckyj: AI Magazine, Vol. 26 (2005), pp.41-50.

Google Scholar

[10] H. Cheng, Y. Li and K.F. Zhang: The International Journal of Advanced Manufacturing Technology, Vol. 42 (2009), pp.1187-1204.

Google Scholar

[11] Q. Su: International Journal of Production Research, Vol. 45 (2007), pp.29-47.

Google Scholar

[12] X.F. Li and J. Li: Data Mining and Knowledge Discovery (Higher Education Press, Beijing 2003) (in Chinese).

Google Scholar