The Connector Blanking Fracture Surface Quality Prediction in Progressive Stamping Based on Support Vector Machine

Abstract:

The influencing factors of technical parameters on blanking fracture surface quality are quite few and complicated, including clearance, speed, die roughness and punch radius. And determination of these influencing factors and optimization of the process parameters combination are vital to the blanking with stability and duration. The determination of these parameters depending on experience of designer in production leads to a longer development period and higher cost in testing modes. In the current study, a kind of prediction method for fracture surface quality was put forward by using nonlinear mapping properties and learning capacity of the support vector machine theory in which nonlinear input can be mapped into higher dimensional space by selecting kernel function RBF, and compromise parameter with C=0, and expectation error of ε=0. 1. The final production model was obtained by practice with orthogonal experiment of four factors times three levels. Experimental verification was conducted by selecting a number of test data. Results have shown that the predicted values were in good agreement with those from tests.

• Periodical: Key Engineering Materials (Volume 620)
• Pages: 263-268
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.620.263
• Online since: August 2014
• Authors: Li Zhi Gu*, Lei Wang, Tian Qing Zheng
• Keywords: Blanking Section Quality, Fracture Surface Quality Prediction, Support Vector Machine (SVM)

References

[1] S.K. Maiti, A.A. Ambekar, U.P. Singh, et al: Journal of Materials Processing Technology, Vol. 102 (2000), pp.249-256.

[2] S.P. Lo, D.Y. Chang, Y.Y. Lin: Journal of Materials Processing Technology, Vol. 194 (2007), pp.126-133.

[3] L. Zhang: Mould & Die manufacturing, Vol. 3 (2011), pp.23-25. (In Chinese).

[4] W.L. Gu: Research on blanking quality and die wear of progressive die of connector terminal (MS., Jiangshu university, China 2009). (In Chinese).

[5] D.C. Wang: Constructing model and controlling by use of support vector machine (China Meteorological Press, China 2009). (In Chinese).

[6] W.B. Ma, W.J. Zhang: Renmingzhujiang, Vol. 5 (2010), pp.14-15.

[7] H.X. Cui: Concrete, Vol. 5 (2010), pp.49-50.

[8] H.X. Du, y. Zhang: Electrical Technology, Vol. 9 (2009), pp.17-21.

[9] R.J.K. Multi: Journal of Engineering APPlications of Artificial Intenigence, Vol. 13 (2000), pp.249-261.

[10] F. Felix, M.A. S and R. Zamora: Joumal of Materials Processing Technology, Vol. 118 (2001), pp.371-376.

[11] L. Guo and Z.Y. Huang: Punching, Vol. 06 (2006), pp.59-62.

[12] Z.Y. Huang: Forging Technology, Vol. 5 (2005), pp.98-100.

[13] J. Wang: Mechanical Science and Technology, Vol. 25 (2006) NO. 8, pp.891-895.

[14] W.G. Chen: Technology and detection, Vol. 1 (2001), pp.50-52.