Advertisement

Modeling of Assembly Deviation with Considering the Actual Working Conditions

  • Wenhui ZengEmail author
  • Yunqing Rao
Regular Paper
  • 2 Downloads

Abstract

Generally, it would be more realistic to consider that the components of an assembly are flexible typically undergo some deformations. In addition, with the mobility of the parts, there is normally a wear between the contact surfaces. Ignoring the dynamic working conditions with these deformations and wear could lead to an inaccurate assembly deviation and further affect the performance, reliability and service life of products. This paper proposes modeling of assembly deviation by considering the actual working conditions to obtain the time-variant assembly gap during the service time. First, the dimensional and geometric tolerances of the parts are simulated by Monte Carlo simulation, and the assembly deviation caused by the tolerances is obtained based on modified Unified Jacobian–Torsor model. Second, the deformation of the surfaces of the parts induced by mechanical and thermal loads are calculated by finite element analysis. Third, the wear depth between the contact surfaces is derived by conducting wear tests under the simulating working condition. By integrating the deformations and wear into tolerance analysis model, the final assembly deviation is constructed by considering the actual working conditions. Finally, the proposed model is applied to the blade bearing of controllable pitch propeller (CPP) for determining the effect of the actual working conditions on its service life.

Keywords

Actual working condition Assembly deviation Service life Time-variant assembly gap 

References

  1. 1.
    Nigam, S. D., & Turner, J. U. (1995). Review of statistical approaches to tolerance analysis. Computer-Aided Design, 27(1), 6–15.CrossRefzbMATHGoogle Scholar
  2. 2.
    Xu, S., & Keyser, J. (2016). Statistical geometric computation on tolerances for dimensioning. Computer-Aided Design, 70, 193–201.CrossRefGoogle Scholar
  3. 3.
    Jin, Q., Liu, S., & Wang, P. (2015). Optimal tolerance design for products with non-normal distribution based on asymmetric quadratic quality loss. The International Journal of Advanced Manufacturing Technology, 78(1), 667–675.CrossRefGoogle Scholar
  4. 4.
    Wu, F., et al. (2009). Improved algorithm for tolerance allocation based on monte carlo simulation and discrete optimization. Computers & Industrial Engineering, 56(4), 1402–1413.CrossRefGoogle Scholar
  5. 5.
    Clément, A., et al. (1991). Theory and practice of 3-D tolerancing for assembly. In: Proceedings CIRP International Working Seminar on Computer-Aided Tolerancing, 25, 25–56.Google Scholar
  6. 6.
    Roy, U., & Li, B. (1999). Representation and interpretation of geometric tolerances for polyhedral objects. II.: Size, orientation and position tolerances. Computer-Aided Design, 31(4), 273–285.CrossRefzbMATHGoogle Scholar
  7. 7.
    Clement, A., & Riviere, A. (1993). Tolerancing versus nominal modeling in next generation CAD/CAM system. In Proceedings of the CIRP Seminar on Computer Aided Tolerancing (pp. 97–113).Google Scholar
  8. 8.
    Chen, H., et al. (2015). A solution of partial parallel connections for the unified Jacobian–Torsor model. Mechanism and Machine Theory, 91, 39–49.CrossRefGoogle Scholar
  9. 9.
    Shen, W., et al. (2015). The quality control method for remanufacturing assembly based on the Jacobian–Torsor model. The International Journal of Advanced Manufacturing Technology, 81(1), 253–261.CrossRefGoogle Scholar
  10. 10.
    Desrochers, A., & Rivière, A. (1997). A matrix approach to the representation of tolerance zones and clearances. The International Journal of Advanced Manufacturing Technology, 13(9), 630–636.CrossRefGoogle Scholar
  11. 11.
    Polini, W., & Corrado, A. (2016). Geometric tolerance analysis through Jacobian model for rigid assemblies with translational deviations. Assembly Automation, 36(1), 72–79.CrossRefGoogle Scholar
  12. 12.
    Desrochers, A., Ghie, W., & Laperrière, L. (2003). Application of a unified Jacobian–Torsor model for tolerance analysis. Journal of Computing and Information Science in Engineering, 3(1), 2–14.CrossRefzbMATHGoogle Scholar
  13. 13.
    Ghie, W., Laperriere, L., & Desrochers, A. (2003) ‘A unified Jacobian–Torsor model for analysis in computer aided tolerancing. In Recent advances in integrated design and manufacturing in mechanical engineering (63–72). Berlin: SpringerGoogle Scholar
  14. 14.
    Zuo, X., et al. (2013). Application of the Jacobian–Torsor theory into error propagation analysis for machining processes. The International Journal of Advanced Manufacturing Technology, 69(5–8), 1557–1568.CrossRefGoogle Scholar
  15. 15.
    Ghie, W., Laperrière, L., & Desrochers, A. (2010). Statistical tolerance analysis using the unified Jacobian–Torsor model. International Journal of Production Research, 48(15), 4609–4630.CrossRefzbMATHGoogle Scholar
  16. 16.
    Jin, S., et al. (2015). A small displacement torsor model for 3D tolerance analysis of conical structures. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 229(14), 2514–2523.Google Scholar
  17. 17.
    Chen, H., et al. (2015). A modified method of the unified Jacobian–Torsor model for tolerance analysis and allocation. International Journal of Precision Engineering and Manufacturing, 16(8), 1789–1800.CrossRefGoogle Scholar
  18. 18.
    Zeng, W., Rao, Y., & Wang, P. (2017). An effective strategy for improving the precision and computational efficiency of statistical tolerance optimization. The International Journal of Advanced Manufacturing Technology, 92(5), 1933–1944.CrossRefGoogle Scholar
  19. 19.
    Jayaprakash, G., Sivakumar, K., & Thilak, M. (2012). A numerical study on effect of temperature and inertia on tolerance design of mechanical assembly. Engineering Computations, 29(7), 722–742.CrossRefGoogle Scholar
  20. 20.
    Benichou, S., & Anselmetti, B. (2011). Thermal dilatation in functional tolerancing. Mechanism and Machine Theory, 46(11), 1575–1587.CrossRefGoogle Scholar
  21. 21.
    Pierre, L., Teissandier, D., & Nadeau, J. P. (2014). Variational tolerancing analysis taking thermomechanical strains into account: Application to a high pressure turbine. Mechanism and Machine Theory, 74, 82–101.CrossRefGoogle Scholar
  22. 22.
    Grandjean, J., Ledoux, Y., & Samper, S. (2013). On the role of form defects in assemblies subject to local deformations and mechanical loads. The International Journal of Advanced Manufacturing Technology, 65(9–12), 1769–1778.CrossRefGoogle Scholar
  23. 23.
    Yu, K. G., & Yang, Z. H. (2015). Assembly variation modeling method research of compliant automobile body sheet metal parts using the finite element method. International Journal of Automotive Technology, 16(1), 51–56.MathSciNetCrossRefGoogle Scholar
  24. 24.
    Jayaprakash, G., Thilak, M., & SivaKumar, K. (2014). Optimal tolerance design for mechanical assembly considering thermal impact. The International Journal of Advanced Manufacturing Technology, 73(5), 859–873.CrossRefGoogle Scholar
  25. 25.
    Mazur, M., Leary, M., & Subic, A. (2011). Computer aided tolerancing (Cat) platform for the design of assemblies under external and internal forces. Computer-Aided Design, 43(6), 707–719.CrossRefGoogle Scholar
  26. 26.
    Mazur, M., Leary, M., & Subic, A. (2015). Application of Polynomial Chaos Expansion to Tolerance Analysis and Synthesis in Compliant Assemblies Subject to Loading. Journal of Mechanical Design, 137(3), 031701-031701-16.CrossRefGoogle Scholar
  27. 27.
    Walter, M. S. J., Spruegel, T. C., & Wartzack, S. (2015). Least cost tolerance allocation for systems with time-variant deviations. Procedia CIRP, 27, 1–9.CrossRefGoogle Scholar
  28. 28.
    Carlton, J. (2012). Marine propellers and propulsion. Oxford: Butterworth-Heinemann.Google Scholar
  29. 29.
    Godjevac, M., et al. (2009). Prediction of fretting motion in a controllable pitch propeller during service. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, 223(4), 541–560.Google Scholar
  30. 30.
    Tarbiat, S., Ghassemi, H., & Fadavie, M. (2014). Numerical prediction of hydromechanical behaviour of controllable pitch propeller. International Journal of Rotating Machinery, 2014, 22–28.CrossRefGoogle Scholar
  31. 31.
    Martelli, M., et al. (2014). Controllable pitch propeller actuating mechanism, modelling and simulation. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, 228(1), 29–43.Google Scholar
  32. 32.
    Wenhui, Z., et al. (2017). Prediction of service life for assembly with time-variant deviation. IOP Conference Series: Materials Science and Engineering, 212(1), 012021.Google Scholar
  33. 33.
    Godjevac, M. (2010). Wear and friction in a controllable pitch propeller. TU Delft: Delft University of Technology.Google Scholar

Copyright information

© Korean Society for Precision Engineering 2019

Authors and Affiliations

  1. 1.Research Institute No.717 of China Shipbuilding Industrial CorporationWuhanChina
  2. 2.The State Key Laboratory of Digital Manufacturing Equipment and TechnologyHuazhong University of Science and TechnologyWuhanChina

Personalised recommendations