簡易檢索 / 詳目顯示

回結果列表
研究生： Riona Ihsan Media
Riona - Ihsan Media
論文名稱： A Rule-Based Two-Level Classification Approach for Recognition of Machining Features from 3D Solid Models
A Rule-Based Two-Level Classification Approach for Recognition of Machining Features from 3D Solid Models
指導教授： 林清安
Alan C. Lin
口試委員: 鍾俊輝
Chun-Hui Chung
巫木誠
Muh-Cherng Wu
學位類別： 碩士
Master
系所名稱： 工程學院 - 機械工程系
Department of Mechanical Engineering
論文出版年： 2016
畢業學年度： 104
語文別： 英文
論文頁數： 80
中文關鍵詞： 3DCAD 、Featurerecognition 、SiemensNX
外文關鍵詞： 3D CAD, Feature recognition, Siemens NX
相關次數： 點閱：166  下載：0 
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  •  上一筆

    • A wide range approaches for recognizing intersecting and isolated features have been suggested in the past few decades. Most of them are dependent on a pre-defined library. A large number of rules are needed to accommodate features with form variation. Therefore, several approaches are proposed in order to increase the flexibility in recognizing different features. Although they show promising results, most of them are limited to recognizing only isolated features. In this thesis work, a
    rule-based approach is presented to enhance the capability of recognizing both intersecting and isolated features with form variation. Features are recognized and classified into a two-level classification. In the first level, edge and loop types are utilized to categorize the features into two different groups, namely single-entry feature (SEF) and multiple-entry feature (MEF). In the second level classification, hole and pocket features can be recognized by either convex internal loops or concave external loops. Meanwhile, step and slot features can be recognized by hybrid loops and the total number of tool accessible directions. Besides, the transitional features are categorized into simple and rounded features according to surface types and the number of face sets. Non-planar surfaces including cylinder, sphere and cone are also examined. Finally, special machining features such as T-slot and dovetail slot are also evaluated by means of pre-defined rules. The proposed approach has been implemented using NX Open in Siemens NX as the platform for system development. Five real industrial parts are used as test examples. The result shows that eighteen feature types are successfully recognized to accommodate intersecting and isolated machining features with variable topology.
    Keywords: 3D CAD, Feature recognition, Siemens NX.

    ABSTRACT ............................................................ i ACKNOWLEDGEMENTS .................................................... ii TABLE OF CONTENTS .................................................... iii LIST OF TABLES ...................................................... v LIST OF FIGURES ..................................................... vi CHAPTER 1 INTRODUCTION ............................................. 1 1.1 Background and Motivation ...................................... 1 1.2 Related works .................................................. 2 1.3 Objectives ..................................................... 5 1.4 Thesis organization ............................................ 5 CHAPTER 2 FEATURE PRINCIPLES ........................................ 6 2.1 Feature Technology ............................................. 6 2.1.1 Feature definition ........................................... 6 2.1.2 Feature taxonomy ............................................. 9 2.1.3 Feature representation schemes ............................... 11 2.1.4 Feature-based methodologies .................................. 13 2.2 Feature recognition ............................................ 13 2.2.1 Basic concept of feature recognition ......................... 15 2.2.2 Classification of feature recognition techniques ............. 16 2.3 Graph-based method. ............................................ 17 2.4 Rule-based method .............................................. 18 2.5 Concavity/ convexity of geometric entity ........................ 19 2.5.1 Concave/convex faces ......................................... 19 2.5.2 Concave/convex edges ......................................... 20 2.5.3 Concave/convex loops ......................................... 22 2.6 Feature interaction ............................................ 22 CHAPTER 3 GENERATION AND RECOGNITION FEATURES ............................................................ 24 3.1 Feature extraction and classification from CAD model ........... 24 3.1.1 Edge classification .......................................... 25 3.1.2 Loop classification .......................................... 27 3.1.3 Face classification ........................................... 28 3.2 Feature Classification ......................................... 28 3.2.1 Single entry feature (SEF) ................................... 31 3.2.2 Multiple-entry feature (MEF) ................................. 34 3.3 Feature definition ............................................. 35 3.3.1 Holes ........................................................ 35 3.3.2 Pockets ...................................................... 36 3.3.3 Step feature ................................................. 38 3.3.4 Slot feature ................................................. 40 3.3.5 Open pocket feature .......................................... 44 3.4 Feature recognition ............................................ 45 CHAPTER 4 IMPLEMENTATION AND RESULTS .............................. 54 4.1 System implementation .......................................... 54 4.2 Execution method ............................................... 55 4.3 Example of system execution .................................... 56 CHAPTER 5 CONCLUSIONS .............................................. 68 5.1 Conclusions .................................................... 68 5.2 Further research ............................................... 68 BIBLIOGRAPHY ........................................................ 69

    1. Xu, X. and S. Hinduja, Recognition of rough machining features in 212D
    components. Computer-Aided Design, 1998. 30(7): p. 503-516.
    2. Sundararajan, V. and P.K. Wright, Volumetric feature recognition for
    machining components with freeform surfaces. Computer-Aided Design, 2004.
    36(1): p. 11-25.
    3. Gao, S. and J.J. Shah, Automatic recognition of interacting machining features
    based on minimal condition subgraph. Computer-Aided Design, 1998. 30(9):
    p. 727-739.
    4. Dimov, S.S., E.B. Brousseau, and R. Setchi, A hybrid method for feature
    recognition in computer-aided design models. Proceedings of the Institution of
    Mechanical Engineers, Part B: Journal of Engineering Manufacture, 2007.
    221(1): p. 79-96.
    5. Shah, J.J., et al., A discourse on geometric feature recognition from CAD
    models. Journal of computing and information science in engineering, 2001.
    1(1): p. 41-51.
    6. Sunil, V.B., R. Agarwal, and S.S. Pande, An approach to recognize interacting
    features from B-Rep CAD models of prismatic machined parts using a hybrid
    (graph and rule based) technique. Computers in Industry, 2010. 61(7): p. 686-701.
    7. Moskalenko, S., Modeling of an automatic CAD-based feature recognition and
    retrieval system for group technology application. 2014.
    8. Fu, M.W., et al., An approach to identify design and manufacturing features
    from a data exchanged part model. Computer-Aided Design, 2003. 35(11): p.
    979-993.
    9. Nagarajan, S. and N.V. Reddy, Step-based automatic system for recognising
    design and manufacturing features. Molecular Physics, 2010. 48(1): p. 117-144.
    10. Case, K. and J. Gao, Feature technology: an overview. International Journal of
    Computer Integrated Manufacturing, 1993. 6(1-2): p. 2-12.
    11. Cunningham, J.J. and J.R. Dixon. Designing with features: the origin of
    features.
    12. Wilson, P.W. and M. Pratt, Requirements for support of form features in a solid
    modelling system. 1985, Technical Report.
    13. Gavankar, P. and M.R. Henderson, Graph-based extraction of protrusions and
    depressions from boundary representations. Computer-Aided Design, 1990.
    22(7): p. 442-450.
    14. Shah, J.J. and M. Mäntylä, Parametric and feature-based CAD/CAM:
    concepts, techniques, and applications. 1995: John Wiley & Sons.
    15. Van't Erve, A.H. and H. Kals, a generative computer-aided process planning
    system for part manufacturing'Annals of the CIRP. Volume 35/1/1986, pp325-329.
    16. Choi, B.K., M.M. Barash, and D.C. Anderson, Automatic recognition of
    machined surfaces from a 3D solid model. Computer-Aided Design, 1984.
    16(2): p. 81-86.
    17. Anderson, D.C. and T.C. Chang, Geometric reasoning in feature-based design
    and process planning. Computers & Graphics, 1990. 14(2): p. 225-235.
    18. Butterfield, W.R., et al., Part features for process planning. CAM-I report R-85-PPP-03, 1986.
    19. Gindy, N.N.Z., A hierarchical structure for form features. The International
    Journal of Production Research, 1989. 27(12): p. 2089-2103.
    20. Wong, T.N. and K.W. Wong, Feature-based design by volumetric machining
    features. International journal of production research, 1998. 36(10): p. 2839-2862.
    21. Nyirenda, P.J., et al., A generic taxonomy for defining freeform feature classes.
    Computer-Aided Design and Applications, 2005. 2(1-4): p. 497-506.
    22. Xu, X. and S. Hinduja, Determination of finishing features in 2½ D
    components. Proceedings of the Institution of Mechanical Engineers, Part B:
    Journal of Engineering Manufacture, 1997. 211(2): p. 125-142.
    23. Lin, A.C., S.Y. Lin, and S.B. Cheng, Extraction of manufacturing features
    from a feature-based design model. International Journal of Production
    Research, 1997. 35(12): p. 3249-3288.
    24. Xu, X., Feature recognition methodologies and beyond. Transactions of
    Mechanical Engineering, 2001. 25(1): p. 1.
    25. Han, J., M. Pratt, and W.C. Regli, Manufacturing feature recognition from
    solid models: a status report. IEEE Transactions on Robotics and Automation,
    2000. 16(6): p. 782-796.
    26. Venuvinod, P.K. and S.Y. Wong, A graph-based expert system approach to
    geometric feature recognition. Journal of Intelligent Manufacturing, 1995.
    6(3): p. 155-162.
    27. Verma, A.K. and S. Rajotia, Feature vector: a graph-based feature recognition
    methodology. International journal of production research, 2004. 42(16): p.
    3219-3234.
    28. Babic, B., N. Nesic, and Z. Miljkovic, A review of automated feature
    recognition with rule-based pattern recognition. Computers in Industry, 2008.
    59(4): p. 321-337.
    29. Corney, J.R., Graph-based feature recognition. 1993.
    30. Xu, X.W., Novel surface and volumetric feature interactions for process
    planning. International journal of computer applications in technology, 2005.
    24(4): p. 185-194.
    31. Joshi, S. and T.-C. Chang, Graph-based heuristics for recognition of machined
    features from a 3D solid model. Computer-Aided Design, 1988. 20(2): p. 58-6

    QR CODE