臺灣博碩士論文加值系統

A numerous approaches in recognition of intersecting and isolated features have been proposed in the last several decades. However, they are limited to features with topologically fixed shapes since they are dependent on the pre-defined patterns or rules. On the other hand, some works have been addressed to increase the flexibility in accommodating different features with variable topology, but they are often restricted to isolated machining features.
In the present thesis work, a rule based approach is developed to accommodate intersecting features with variable topology shapes. The proposed approach classifies the features according to the multi-level feature taxonomy. In the first level, features are categorized into three groups of primitive features. Loops and edges are used as fundamental entities to determine the primitive feature types. In the second level, pockets and holes are identified from their primitive feature attributes while visibility maps are adopted to recognize slots and steps features. Intersecting features are identified based on adjacency relationships among face members of the features. On the other hand, pre-defined rules are still utilized in restricted application to identify special machining features such as t-slot, v slot and dovetail slot.
In addition to that, the proposed approach has been implemented to recognize machining features in the industrial parts model in the b-rep format. The result shows that both of isolated and intersecting features with different topology shapes are well extracted from the 3D model.

CONTENT

ABSTRACT 1
CONTENT 2
LIST OF FIGURES 4
LIST OF TABLE 6
CHAPTER 1 INTRODUCTION 7
1.1 Research Background 7
1.2 Research Objective 13
1.3 Thesis Organization 13
CHAPTER 2 THEORETICAL BACKGROUND 15
2.1 Feature 15
2.1.1 Feature classification 16
2.2 Features based design VS features recognition 18
2.3 Solid modelling 19
2.3.1 Boundary representative 20
2.3.2 Constructive solid geometry 22
CHAPTER 3 MULTI-STRUCTURE METHODOLOGY FOR MACHINING FEATURES RECOGNITION 24
3.1 Machining features taxonomy. 24
3.1.1 First level classification 24
3.1.2 Second level classification 26
3.2 Edge and loop classification. 31
3.2.1 Edge 31
3.2.2 Loop 34
3.3 Algorithm for machining features recognition. 36
3.3.1 Face collection. 37
3.3.2 Features concatenation 40
3.3.3 Features recognition 44
CHAPTER 4 SYSTEM IMPLEMENTATION 57
4.1 Systems implementation 57
4.2 Implementation result 59
4.2.1 Solid model 1 59
4.2.2 Solid model 2 61
4.2.3 Solid model 3 63
4.2.4 Solid model 4 65
4.2.5 Solid model 5 66
4.3 Contribution 68
CHAPTER 5 CONCLUSION 71
5.1 Conclusion 71
5.2 Future consideration 72
REFRENCES 73

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