Reverse Engineering of a Piston Using Knowledge Based Reverse Engineering Approach

  • A. Durupt
  • S. Remy
  • G. Ducellier
Conference paper


This paper focuses on Reverse Engineering (RE) in mechanical design. RE is an activity which consists in creating a full CAD model from a 3D point cloud. The aim of RE is to enable an activity of redesign in order to improve, repair or update a given mechanical part. Nowadays, CAD models obtained using modern software applications are generally “frozen” because they are sets of triangles of free form surfaces. In such models, there are not functional parameters but only geometric parameters. This paper proposes the KBRE (Knowledge Based Reverse Engineering) methodology which allows managing and fitting manufacturing and/or functional features. In this paper, specific geometric algorithms are described. They allow extracting design intents in a point cloud in order to fit these features.


Reverse Engineering CAD model Knowledge based system 


  1. 1.
    Laroche, F., Bernard, A., Cotte, M. (2006) Methodology for simulating ancient technical systems. International Review of Numerical Engineering. Integrated Design Production, 2:9–28.Google Scholar
  2. 2.
    Bernard, A., Fischer, A. (2002) New trends in rapid product development. CIRP Annals-Manufacturing Technology, 52(2):635–652.CrossRefGoogle Scholar
  3. 3.
    Durupt, A., Remy, S., Ducellier, G., Eynard, B. (2008) From a 3D point cloud to an engineering CAD model: A knowledge product based approach for reverse engineering. Virtual and Physical Prototyping, 3(2):51–59.CrossRefGoogle Scholar
  4. 4.
    Durupt, A., Remy, S., Ducellier, G., Derigent, W. (2008) A new knowledge based approach for the reverse engineering of a product. Design 2008, 1:753–760.Google Scholar
  5. 5.
    Benko, P., Varady, T. (2004) Segmentation methods for smooth point regions of conventional engineering objects. Computer Aided Design, 36(6):511–523.CrossRefGoogle Scholar
  6. 6.
    Besl, P., Jain, R. (1988) Segmentation through variable order surface fitting. IEEE Transaction on Pattern Analysis and Machine Intelligence, 10(2):167–192.CrossRefGoogle Scholar
  7. 7.
    Hoffman, R.L., Jain, A.K. (1987) Segmentation and classification of ranges images. IEEE Transaction on Pattern Analysis and Machine Intelligence, 9(5):608–620.CrossRefGoogle Scholar
  8. 8.
    Yokoya, N., Levine, M. (1989) Range image segmentation based on differential geometry: A hybrid approach. IEEE Transaction on Pattern Analysis and Machine Intelligence, 11(6):643–649.CrossRefGoogle Scholar
  9. 9.
    Alrashdan, A., Motavalli, S., Fallah, B. (2000) Automatic segmentation of digitized data for reverse engineering applications. IIE Transactions on Robotics and Automation, 32(1):59–69.Google Scholar
  10. 10.
    Tai, C.C., Huang, M.C. (2000) The processing of data points basing on design intents in reverse engineering. International Journal of Machine Tools & Manufacture, 40(13):1913–1927.CrossRefGoogle Scholar
  11. 11.
    Thompson, W., Owen, J., De st Germain, H., Stark, S., Henderson, H. (1999) Feature based reverse engineering of mechanical parts. IEEE Transactions on Robotics and Automation, 15(1):57–65.CrossRefGoogle Scholar
  12. 12.
    Werghi, N., Fisher, R., Robertson, C., Ashbrook, A. (1999) Object reconstruction by incorporating geometric constraints in reverse engineering. Computer Aided Design, 31(6):363–399.MATHCrossRefGoogle Scholar
  13. 13.
    Mills, B.I., Langbein, F.C., Marshall, A.D., Martin, R.R. (2001) Approximate symmetry detection for reverse engineering. Sixth ACM Symposium on Solid Modelling and Applications, ACM, New York, NY, pp. 241–248.Google Scholar
  14. 14.
    Karniel, A., Belsky, Y., Reich, Y. (2005) Decomposing the problem of constrained surface fitting in reverse engineering. Computer Aided Design, 37(4):399–417.CrossRefGoogle Scholar
  15. 15.
    Shah, J.J., Anderson, D., Kim Y.S., Joshi, S. (2001) A discourse on geometric feature recognition from CAD models. Journal of Computing and Information Science in Engineering, 1(1):41–51.CrossRefGoogle Scholar
  16. 16.
    VPERI (2003) Virtual Parts Engineering Research Initiative, The final report (
  17. 17.
    Mohaghegh, K., Sadeghi, M.H., Abdullah, A. (2006) Reverse engineering of turbine blades on design intent. International Journal of Advanced Manufacturing Technology, 32(9–10):1009–1020.Google Scholar
  18. 18.
    Fisher, R.B. (2004) Applying knowledge to reverse engineering problems. Computer Aided Design, 36(6):501–510.CrossRefGoogle Scholar
  19. 19.
    Urbanic, R.J., Elmaraghy, H.A, Elmaraghy, W.H. (2007) A reverse engineering methodology for rotary components from point cloud data. International Journal of Advance Manufacturing Tool, 37(11–12):1146–1167.Google Scholar
  20. 20.
    Yong, C., Peien, F., Zhougqin, L. (2005) A genetic based approach for the principle conceptual design of mechanical products. International Journal of Advanced Manufacturing Technology, 27(3–4):225–233.Google Scholar
  21. 21.
    Skarka, W. (2007) Application of MOKA methodology in generative model creating using CATIA. Engineering Applications of Artificial Intelligence, 20(5):677–690.CrossRefGoogle Scholar
  22. 22.
    Chapman, C., Pinfold, M. (2001) The application of a knowledge based engineering approach to the rapid design and analysis of an automotive structure. Advanced in Engineering Software, 32(12):903–912.MATHCrossRefGoogle Scholar
  23. 23.
    Ashby, M.F., Brechet, Y.J.M., Cebon, D., Salvo, L. (2004) Selection strategies for materials and processes. Materials and Design, 25(1):51–67.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Institut Charles Delaunay Laboratoire des Systèmes Mécaniques et d’Ingénierie Simultanée (ICD LASMIS), Université de Technologie de TroyesTroyesFrance

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