Hybrid genetic algorithms and artificial neural networks for complex design optimization in CFD

2004 ◽  
Vol 44 (11) ◽  
pp. 1257-1278 ◽  
Author(s):  
R. Duvigneau ◽  
M. Visonneau
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Design Optimization ◽  
Hybrid Genetic Algorithms ◽  
Complex Design ◽  
Artificial Neural

A Neurogenetic Approach to a Multiobjective Design Optimization of Spinal Pedicle Screws

2010 ◽  
Vol 132 (9) ◽  
Author(s):  
Ching-Kong Chao ◽  
Jinn Lin ◽  
Sandy Tri Putra ◽  
Ching-Chi Hsu
Keyword(s):  
Neural Networks ◽  
Finite Element ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Design Optimization ◽  
Pedicle Screw ◽  
Pedicle Screws ◽  
Screw Breakage ◽  
Artificial Neural ◽  
Multiobjective Design

A pedicle screw fixation has been widely used to treat spinal diseases. Clinical reports have shown that the weakest part of the spinal fixator is the pedicle screw. However, previous studies have only focused on either screw breakage or screw loosening. There have been no studies that have addressed the multiobjective design optimization of the pedicle screws. The multiobjective optimization methodology was applied and it consisted of finite element method, Taguchi method, artificial neural networks, and genetic algorithms. Three-dimensional finite element models for both the bending strength and the pullout strength of the pedicle screw were first developed and arranged on an L25 orthogonal array. Then, artificial neural networks were used to create two objective functions. Finally, the optimum solutions of the pedicle screws were obtained by genetic algorithms. The results showed that the optimum designs had higher bending and pullout strengths compared with commercially available screws. The optimum designs of pedicle screw revealed excellent biomechanical performances. The neurogenetic approach has effectively decreased the time and effort required for searching for the optimal designs of pedicle screws and has directly provided the selection information to surgeons.

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