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研究生:丁公輝
研究生(外文):DINH CONG HUY
論文名稱:腰椎前方板型固定器於不同骨螺絲植入角度之拉出強度最佳化研究
論文名稱(外文):An Optimization Study of Screw Orientation on the Pullout Strength of Anterior Lumbar Plate System
指導教授:趙振綱徐慶琪
指導教授(外文):Ching-Kong ChaoChing-Chi Hsu
口試委員:趙振綱徐慶琪
口試委員(外文):Ching-Kong ChaoChing-Chi Hsu
口試日期:2013-06-20
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:機械工程系
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:英文
論文頁數:79
中文關鍵詞:前路椎間融合術腰椎前路板系統拉拔強度田口方法有限元分析人工神經網絡遺傳算法生物力學測試
外文關鍵詞:Anterior Interbody FusionAnterior Lumbar Plate SystemVariable Angle ScrewScrew OrientationPullout StrengthTaguchi MethodFinite Element AnalysisArtificial Neural NetworkGenetic AlgorithmBiomechanical Tests.
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Anterior lumbar plate system has been widely used as an effective interbody fusion device for treating spinal cord compression. In order to reduce the chance of clinical complications including the implant loosening and breakage, manufacturers have provided many kinds of anterior lumbar plate with variable angle screws. However, these devices, which were inserted with a particular inclined screw angle to strengthen whole plate system, were used based on the experience of surgeon without biomechanical evidences. Therefore, the purpose of this study was to investigate the effect of screw orientation for the pullout strength behavior of an anterior lumbar plate system.
This study used two different methods including Taguchi robust design method and Neuro-genetic algorithm to determine the screw angle of an anterior lumbar plate system, which may provide good pullout strength behavior. Advanced three-dimensional finite element models have been developed to simulate the loosening of this plate system. Then a parametric study of Taguchi method was conducted to investigate significant factors and Taguchi robust design. In the other hand, an optimization study of Neuro-genetic algorithm was applied to determine and predict optimum design. The results of these numerical studies were validated using biomechanical tests.
Numerical study and experiment results have indicated that the anterior lumbar plate with divergent screw insertion angle might provide better pullout strength behavior. The optimal result searched by Neuro-genetic algorithm was superior to the optimum combination obtained by Taguchi robust design method. The total reaction force obtained by finite element analyses was closely related to the maximum pullout strength obtained from mechanical tests with a high correlation coefficient.
In conclusion, the Neuro-genetic algorithm could effectively provide optimal design with higher reliability than Taguchi method. The biomechanical experiments could be used to evaluate the results of numerical models. The results of the present study could directly provide suggestion to surgeons and manufacturers in using and developing anterior lumbar plate system with variable angle screw. The powerful methodology using in this study could also be applied to many other situations.
TITLE PAGE i
ABSTRACT ii
ACKNOWLEGEMENT iv
TABLE OF CONTENTS v
LIST OF FIGURES viii
LIST OF TABLES x
CHAPTER 1 INTRODUCTION 1
1.1 Research Background and Study Purpose 1
1.2 Anatomy of the Spine 2
1.2.1 Anatomical directions and plane terms 3
1.2.2 Spinal curves 5
1.2.3 Vertebral Column 6
1.2.4 Each vertebra bone 9
1.3 Literatures Review 11
1.4 Structure of Dissertation 14
CHAPTER 2 MATERIALS AND METHODS 16
2.1 Overview of Study Methodology 16
2.2 Finite Element Analysis 18
2.2.1 Geometry Input 19
2.2.2 Pullout Strength Analysis 21
2.3 Taguchi Method 26
2.3.1 Factors and levels, orthogonal array 26
2.3.2 Signal-to-noise ratio (S/N Ratio) and Additive Model 29
2.3.3 Analysis of Variance Statistic (ANOVA Table) 31
2.4 Neuro-Genetic Method 33
2.4.1 Artificial Neural Network Model 33
2.4.2 Genetic Algorithm 40
2.5 Biomechanical Experiments 44
2.5.1 Structures of bone models, tested screws and anterior plates 44
2.5.2 Biomechanical tests of pullout load 48
CHAPTER 3 RESULTS 51
3.1 Finite Element Analysis 51
3.2 Taguchi Methods 54
3.2.1 Mean S/N Ratio an ANOVA table 54
3.2.2 Optimal Result Obtained by Taguchi Additive Model 57
3.3 Neuro-Genetic Method 59
3.3.1 ANN model 59
3.3.2 Optimum Result Obtained by GA Program 63
3.4 Experiment Results 65
CHAPTER 4 DISCUSSIONS 69
CHAPTER 5 CONCLUSIONS 75
REFERENCES 77
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