迦瑪骨釘(Gamma nail)和動態髖骨螺絲(Dynamic hip screw)已廣泛的應用於治療股骨近端骨折，然而，從臨床觀察中發現，在骨折治療過程中植入物會發生的失效模式有破斷和鬆脫等情形，且近端股骨可能會因植入物的影響而發生骨切效應風險(Cut-out)，為了減少迦瑪骨釘和動態髖骨螺絲之許多缺點，設計製造出雙螺絲骨釘。雙螺絲骨釘包含一支骨髓內釘、兩支遲滯螺絲以及一或兩支遠端鎖定螺絲，雖然雙螺絲骨釘相較於迦瑪骨釘和動態髖骨螺絲有較佳的臨床性能，但手術後遲滯螺絲亦可能在短期內發生破壞，因此，最佳化設計方法將應用於遲滯螺絲。本研究之目的為以田口品質工程法分析遲滯螺絲之重要設計參數，以及使用類神經網路與遺傳演算法獲得遲滯螺絲之最佳化設計。
根據田口直交表，採用SolidWorks 2005繪圖軟體來建立三維實體模型，並將實體模型利用parasolid格式轉入為ANSYS 10 Workbench分析軟體中，求解模擬每一個模型的彎曲強度與骨咬合強度。在後處理，遲滯螺絲之最大張應力和總反作用力被用來作為目標值，也取代了最佳化設計之彎曲強度與骨咬合強度。使用變異數分析（ANOVA）來研究遲滯螺絲之彎曲強度和骨咬合強度之設計參數的貢獻度。在最佳化設計中，將遲滯螺絲之最大張應力和總反作用力兩個目標函數產生類神經網路模型，此類神經網路模型可以簡化成加權總集方法，最後以遺傳演算法找出最佳設計。
本研究之參數化分析中，圓錐角起始位置、內徑是決定彎曲強度的重要設計參數，而內徑、近端根部弧角半徑與節距是決定骨咬合強度的重要設計參數。在本研究中，類神經網絡模型與有限元素分析之結果相近，類神經網路模型相較於傳統的線性迴歸模型有較佳的預測性。根據本研究所提供的有限元素模型，可反應出臨床上的觀察結果。最佳設計點(Patero front)的最佳設計區域(Knee region)同時擁有高的彎曲強度與骨咬合強度，本研究所得到的最佳化設計範圍是權重值在0.42至0.68的區間內，設計參數的相關範圍包括：圓錐角起始位置8.129 ~ 9.032 mm，內徑4.068 ~ 4.342 mm，近端根部弧角半徑為0.4 mm，節距為2.6 mm，近端螺牙傾角為5°，螺牙寬為0.1 mm。本研究可以幫助工程師設計出新的骨科植入物及幫助骨科醫生選擇合適的骨科植入物。

The gamma nail and dynamic hip screw have been applied for the treatment of proximal femoral fracture. However, in the clinical point of view, the implants may fail and cause loss of fracture fixation and impairment of fracture healing. In addition, the proximal femur may be cut out by those implants. In order to reduce many disadvantages of the gamma nail and dynamic hip screw, the double screw nail was designed and manufactured. The double screw nail which consisted of an interlocking nail, two proximal lag screws, and one or two distal locking screws. Although the double screw nail had better clinical performance as compared with the gamma nail and dynamic hip screw, the lag screws used in the double screw nail might also occur damage in a short period. Therefore, the multiobjective design optimization method would be applied to the lag screws. The purposes of this study were to analyze the important design parameters of the lag screws using the Taguchi method and acquire the optimal design of the lag screws using the artificial neural networks and genetic algorithms.
Based on the arrangement of the Taguchi orthogonal array, the three-dimensional solid models were created by SolidWorks 2005. Then, the solid models were transformed into ANSYS 10 Workbench by using parasolid format to simulate the bending and pullout strength of the lag screws. In the postprocessing, the maximal tensile stress and the total reaction force of the lag screw was used as the objective value for subsequent design optimizations to represent the bending strength and pullout strength. Analysis of the variance (ANOVA) was used to investigate the contribution of design parameters. In the optimal design, the maximal tensile stress and the total reaction force of the lag screws, two objective functions were created by artificial neural networks, and they could be simplified by a weighted-sum aggregating approach. Finally, the optimal design of the lag screws would be obtained by using genetic algorithms.
In the parametric analysis of this study, initial position of conical angle and inner diameter are the important factors for bending strength, and inner diameter, proximal root radius, and pitch are the important parameters for the pullout strength. The ANN models could accurately reflect the finite element results and also had the better predictability as compared with the traditional linear regression model. Developing the finite element models in this study could reflect the clinical observation. The solutions at the knee region of the Pareto fronts had higher bending strength and pullout strength. In the knee region, the weight ranged from 0.42-0.68. The corresponding range of the design parameters was 8.129 ~ 9.032 mm for initial position of conical angle and 4.068 ~ 4.342 mm for inner diameter. The fixed parameters were 2.6 mm for pitch, 0.4 mm for proximal root radius, 5 degrees for proximal half angle, and 0.1 mm for thread width. This study could assist the engineers to design new orthopedics implants and help the orthopedics surgeons to select suitable orthopedics implants.

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