椎弓足螺絲(pedicle screw)是用來矯正脊椎側彎的植入物，然而椎弓足螺絲會因受到彎曲負荷而斷裂或是椎體骨質疏鬆而螺絲鬆脫等併發症問題。彎曲強度與椎弓足螺絲斷裂有關，抗拉強度則與螺絲鬆脫有關，而此兩種強度皆與螺紋設計有很大的關係。本研究使用田口品質法(Taguchi Method)和神經遺傳法(Neuro-Genetic Method)來找出螺紋之最佳化設計。本次研究的目的是比較這兩種方法，哪一個找出的最佳化設計有較好的結果，另一個是計算及分析各個因子的貢獻度。
本次研究用有限元素法(Finite Element Method)模擬出椎弓足螺絲的彎曲強度和抗拉強度，其L25直交表之資料皆代入田口品質法與神經-遺傳法之中。田口品質法運用L25直交表找出最佳設計，並使用變異數分析(Analysis of Variation)統計出每個因子的貢獻度。神經-遺傳法則是將直交表算出的結果匯入類神經網路(Artificial Neural Networks)訓練出六個模型，這六個模型輸入基因演算法(Genetic Algorithms)中找出最佳設計。以變異數分析當標準，計算貢獻度的方法有Garson權重計算法(Garson weight method)、Olden權重計算法(Olden weight method)、因子權重移除法(Weight elimination)、偏微分法(Partial derivative)、敏感度分析(Sensitivity analysis)之全因子配置法(Profile method)與單因子分割法(Factor division)，比較這些類神經網路計算貢獻度的方法並找出何種方法能找出最準確之貢獻度。
在本研究之結果，最佳設計的彎曲強度方面，神經-遺傳法的最佳設計所受到的最大拉應力比田口品質法低3.01％。而在抗拉強度，神經-遺傳法比田口品質法高10.14％。在計算貢獻度方面，因子權重移除法與單因子分割法與變異數分析最為類似。

Pedicle screws used for fixation of hip trochanteric fractures should have higher bending strength to resist breakage and bone holding power to resist migration. The present optimization study is to find screws with the highest bending or bone holding power. Besides, the “black box” problem of ANN was not disclosed. We could get the predictive value from the artificial neural networks conveniently, but the less information about the relative influence of the independent variables was provided. The purpose of the study is to find the optimal solution and illuminate the black box by calculating contribution of each factor with different methods.
The bending and pullout functions of the pedicle screws were first simulated by finite element models (FEM). With L25 orthogonal arrays, optimal screw designs with highest bending or bone holding power were then investigated by Taguchi robust design methods and Neuro-Genetic method hybridizing artificial neural networks (ANN) and genetic algorithm (GA). The mechanical performances of the optimal designs, the predictability of the performances and the contribution of the design factors obtained in these methods were compared. On the aspect of contribution, the result of analysis of variance (ANOVA) is considered as standard. The weight method, weight elimination, partial derivative and sensitivity analysis are compared to find which one’s contribution is the most accurate.
For optimization studies, Neuro-Genetic method could yield better results than Taguchi methods by -3.01% in bending and 10.14% in pullout analyses. For predicting the performances, the errors of NG algorithm were 0.65% and 1.42% for bending and pullout analyses respectively, much lower than -21.26% and 15.95% of Taguchi method. In addition, using weight elimination or factor division could calculate accurate contribution of the design factors.

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