Paper Title:
Modeling and Analysis of Compressive Properties of Porous NiTi Shape Memory Alloy Using Artificial Neural Network
  Abstract

Artificial neural network (ANN) is an intriguing data processing technique. Over the last decade, it was applied widely in the chemistry field, but there were few applications in the porous NiTi shape memory alloy (SMA). In this paper, 32 sets of samples from thermal explosion experiments were used to build a three-layer BP (back propagation) neural network model. According to the registered BP model, the effect of process parameters including heating rate ( ), green density ( ) and particle size of Ti ( d ) on compressive properties of reacted products including ultimate compressive strength ( v D σ ) and ultimate compressive strain (ε ) was analyzed. The predicted results agree with the actual data within reasonable experimental error, which shows that the BP model is a practically very useful tool in the properties analysis and process parameters design of the porous NiTi SMA prepared by thermal explosion method.

  Info
Periodical
Advanced Materials Research (Volumes 41-42)
Edited by
Xiaozhi Hu, Brent Fillery, Tarek Qasim and Kai Duan
Pages
135-140
DOI
10.4028/www.scientific.net/AMR.41-42.135
Citation
Q. Li, X. D. Sun, J. Y. Yu, Z. G. Liu, K. Duan, "Modeling and Analysis of Compressive Properties of Porous NiTi Shape Memory Alloy Using Artificial Neural Network", Advanced Materials Research, Vols. 41-42, pp. 135-140, 2008
Online since
April 2008
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$32.00
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