Wear mechanism and tribological characteristics of porous NiTi shape memory alloy for bone scaffold

2013 ◽  
Vol 101A (9) ◽  
pp. 2586-2601 ◽  
Author(s):  
Shuilin Wu ◽  
Xiangmei Liu ◽  
Guosong Wu ◽  
Kelvin W.K. Yeung ◽  
Dong Zheng ◽  
...  
2008 ◽  
Vol 41-42 ◽  
pp. 135-140 ◽  
Author(s):  
Qiang Li ◽  
Xu Dong Sun ◽  
Jing Yuan Yu ◽  
Zhi Gang Liu ◽  
Kai Duan

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.


2011 ◽  
Vol 528 (18) ◽  
pp. 6006-6011 ◽  
Author(s):  
C.Y. Tang ◽  
L.N. Zhang ◽  
C.T. Wong ◽  
K.C. Chan ◽  
T.M. Yue

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