Rapid prediction method for nonlinear expansion process of medical vascular stent |
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Authors: | ZhongHua Ni XingZhong Gu YueXuan Wang |
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Affiliation: | (1) School of Mechanical Engineering, Southeast University, Nanjing, 210096, China |
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Abstract: | A neural network model with high nonlinear recognition capability was constructed to describe the relationship between the
deformation impact factors and the deformation results of vascular stent. Then, using the weighted correction method with
the attached momentum term, the network training algorithm was optimized by introducing learning factor η and momentum factor ψ, so the speed of the network training and the system robustness were enhanced. The network was trained by some practical
cases, and the statistical hypothesis validation was made for the predictive errors. It was shown that the average difference
between the intelligent predictive result of vascular stent deformation neural network and the nonlinear finite element analysis
result was less than 0.03%, and the trained network could perfectly predict the vascular stent deformation. Further more,
the rapid evaluation tool for the vascular stent mechanics performance was established using the Pro/Toolkit and the intelligent
neural network predictive model of vascular stent expansion. The proposed tool system with strong practicality and high efficiency
can significantly shorten the product development cycle of vascular stent.
Supported by the National Basic Research Program of China (Grant Nos. 2006CB708610, 2006AA04Z351), the National Natural Science
Foundation of China (Grant No. 50675033), and partly by the Natural Science Foundation of Jiangsu Province (Grant Nos. BK2006709
and BK2005072) |
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Keywords: | vascular stent nonlinear finite element analysis artificial neural network |
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