脊椎是人體中最重要的構造之一，當人體脊椎因為一些意外事件而造成脊椎錯位、脊椎椎體骨折或因脊椎病變產生腫瘤或感染時，將病變椎體置換成脊椎椎體椎籠為骨科醫師常用於治療上述類型之脊椎椎體疾病的方法。人工椎籠置換手術(Vertebral Body Replacement Cage)可以解除脊椎疼痛並能保留脊椎活動範圍，而在治療的過程中植入物會有下陷和滑脫等不穩定的情形，這些情況會造成骨折固定失效的問題，影響患者康復的情形。本研究是使用電腦數值模擬法來進行椎體椎籠介面鬆脫的研究，在這方面的問題，一般會施加一個固定方向(常見的是前後方向)並與脊柱相互垂直的負荷或位移，來探討椎體椎籠的介面咬合強度，但是椎體椎籠在植入體內後，會承受不同平面的負載，這些負載可能會造成椎體椎籠產生不同方向的滑脫，因此本研究希望能找一個在每個方向都能提供良好介面強度的設計。
本研究的方法是依照田口直交表(Orthogonal Array)規律的分配每一個模型的參數水準，然後，利用SolidWorks 2008建立立體模型，將其利用parasolid檔案轉進ANSYS Workbench 11中進行有限元素分析模擬，之後，將數據輸入進類神經網路程式中得到輸入參數與輸出介面強度的關係，並利用遺傳演算法找出人工椎籠在各種方向滑脫時的最佳化的範圍。
經遺傳演算法的最佳化演算後，單方向介面強度最佳化的端面齒型高度為2㎜、齒型寬度為1.866㎜、端面齒型的斜度則是1，而齒排數為15、內徑為10mm；而考慮不同方向介面強度最佳化的端面齒型高度同樣為2㎜、齒型寬度則為1㎜、端面齒型的斜度為0.16，而齒排數為16、內徑為10mm；本研究結果顯示，單獨考慮單一方向的介面強度而言，較寬的的端面齒型配上較斜的傾斜程度能提供較好的介面強度，若針對多方向的介面強度而言，較窄的端面齒型搭配上沒有傾斜的齒型反而是比較好的設計。

關鍵字： 人工椎體椎籠、介面強度、有限元素分析、田口品質工程法、 類神經網路、遺傳演算法

Spine is one of the most important structures of the human body. Orthopedists usually use cage to treat the vertebral body illness, such as the slipped disk and the collapsed vertebral caused by unexpected accident or some tumor and infection caused by vertebral lesions. Vertebral Body Replacement Cage could ease the pain of the spine and maintain the spine act as usual. However, there are some unstable conditions like the subsidence and the loosening of the implant would happen after we implant the cage into the body. These conditions might invalidate the fracture fixation then react on sufferer’s recovery. In this study, we adopt FEM to analyze the loosening of the vertebral body cage. Generally, we would discuss the pullout strength of vertebral body cage by exerting a single direction (normally forward and backward) loading or displacement which is vertical to the spine. However, the vertebral body cage would take the loading from different directions after its implantation into body. These loading would result the vertebral body cage loosening from different directions. By this research, we hope we could find out a design which could provide the well pullout strength in every direction.
This study adopted Orthogonal Array to regularly allocate various levels to each model. After that, we used SolidWorks 2008 to build solid models then transferred the parasolid file into ANSYS Workbench 11 to finite element analyzed. Next step, we inputted the data to artificial neural network (ANN) program to get the relation between the input parameters and the output pullout strength. Moreover, we analyzed the relation by genetic algorithm (GA) to find out the optional range of the pullout strength of the vertebral body cage by every direction. After the optional calculation by GA, we got the optional parametric combination of the pullout strength in single direction, that the spike height is 2 mm, the spike width is 1.866 mm, the spike oblique is 1, spike row is 15 and the inner diameter is 10 mm. For the optional parametric combination of the pullout strength in every direction, the spike height is same as with single direction that is 2 mm, spike width is 1 mm, the spike oblique is 0.16, the spike row is 16, and the inner diameter is 10 mm. The outcome of this study is that wider and more gradient spike could offer better pullout strength when we only consider single direction. On the contrary, narrower and less gradient spike is a better design for pullout strength when we consider multi-direction.
Key Works： Vertebral Body Cage、Pullout Strength、Finite Element Analysis、Taguchi method、Artificial Neural Network、Genetic Algorithm

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