A New Approach to Control Assembly Variation in Selective Assembly Using Hierarchical Clustering

  • S. V. ChaitanyaEmail author
  • A. K. Jeevanantham
Conference paper


Complex assembly constitutes more than two parts. Tolerances assigned to individual components decide precision of assembly. Clearance and variation resulted in assembly decides precision of assembly and affect performance during working of assembly. During high precision mechanical assemblies, many parts become surplus due to more variation on component tolerances. Then, selective assembly is only solution to control the clearance variation. In this paper, a new methodology of hierarchical clustering approach is developed to predict the precision in assembly variation so that an assembly can confirm the desired clearance specifications. A valve train assembly of an IC engine that consists of cam-tappet-stem, is considered for the case analysis. The proposed methodology can be implemented in any number of components in real situations.


Selective assembly Clustering Clearance variation High precision assembly 


  1. 1.
    Mansoor, E.M.: Selective assembly—Its analysis and applications. Int. J. Prod. Res. 1, 13–24 (1961). Scholar
  2. 2.
    Arai, T., Takeuchi, K.: A simulation system on assembly accuracy. CIRP Ann. 41(1), 37 (1992). STC ACrossRefGoogle Scholar
  3. 3.
    Fang, X.D., Zhang, Y.: A new algorithm for minimizing the surplus parts in selective assembly. Comput. Ind. Eng. 28(2), 341–350 (1995)CrossRefGoogle Scholar
  4. 4.
    Wang, Y.: Semantic tolerancing with generalised intervals. Comput.-Aided Des. Appl. 4(1–4), 257–266 (2007)CrossRefGoogle Scholar
  5. 5.
    Kannan, S., Jayabalan, V., Jeevanantham, K.: Genetic algorithm for minimizing assembly variation in selective assembly. Int. J. Prod. Res. 41, 3301–3313 (2003). Scholar
  6. 6.
    Kannan, S.M., Jeevanantham, A.K., Jayabalan, V.: Modelling and analysis of selective assembly using Taguchi’s loss function. Int. J. Prod. Res. 46, 4309–4330 (2008). Scholar
  7. 7.
    Matsuura, S., Shinozaki, N.: Optimal binning strategies under squared error loss in selective assembly with measurement error. Commun. Stat.-Theory Methods 36, 2863–2876 (2007). Scholar
  8. 8.
    Fischer, B.R.: Mechanical Tolerance Stackup and Analysis, 2nd edn. CRC Press, Boca Raton (2011)CrossRefGoogle Scholar
  9. 9.
    Desrochers, A., Riviere, A.: A matrix approach to the representation of tolerance zones and clearances. Int. J. Adv. Manuf. Technol. 13, 630–636 (1997)CrossRefGoogle Scholar
  10. 10.
    Singh, P.K., Jain, S.C., Jain, P.K.: Advanced optimal tolerance design of mechanical assemblies with interrelated dimension chains and process precision limits. Comput. Ind. 56, 179–194 (2005)CrossRefGoogle Scholar
  11. 11.
    Marziale, M., Polini, W.: A review of two models for tolerance analysis of an assembly: vector loop and matrix. Int. J. Adv. Manuf. Technol. 43, 1106–1123 (2009)CrossRefGoogle Scholar
  12. 12.
    Khodaygan, S., Movahhedy, M.R., Fomani, M.S.: Tolerance analysis of mechanical assemblies based on modal interval and small degrees of freedom (MI-SOF) concept. Int. J. Adv. Manuf. Technol. 50, 1041–1061 (2010)CrossRefGoogle Scholar
  13. 13.
    Cao, Y., Zhang, H., Mao, J., Xusong, X., Yang, J.: Study on tolerance modeling of complex surfaces. Int. J. Adv. Manuf. Technol. 53, 1183–1188 (2011)CrossRefGoogle Scholar
  14. 14.
    Weihua, N., Zhenqiang, Y.: Cylindricity modeling and tolerance analysis for cylindrical components. Int. J. Adv. Manuf. Technol. 64, 867–874 (2013)CrossRefGoogle Scholar
  15. 15.
    Bo, C., Yang, Z., Wang, L., Chen, H.: A comparison of tolerance analysis models for assembly. Int. J. Adv. Manuf. Technol. 68, 739–754 (2013)CrossRefGoogle Scholar
  16. 16.
    Yang, Z., Popov, A.A., McWilliams, S.: Variation propagation control in mechanical assembly of cylindrical components. J. Manuf. Syst. 31, 162–176 (2012)CrossRefGoogle Scholar
  17. 17.
    Chen, H., Jin, S., Li, Z., Lai, X.: A comprehensive study of three dimensional tolerance analysis methods. Comput.-Aided Des. 53, 1–13 (2014)CrossRefGoogle Scholar
  18. 18.
    Calvo, R., Gómez, E., Domingo, R.: Vectorial method of minimum zone tolerance for flatness, straightness, and their uncertainty estimation. Int. J. Precis. Eng. Manuf. 15(1), 31–44 (2014)CrossRefGoogle Scholar
  19. 19.
    Laosiritaworn, W., Kitjongtawornkul, P., Pasui, M., Wansom, W.: ‘Die storage improvement with k-means clustering algorithm’, a case of paper packaging business. In: 4th International Symposium on Computational and Business Intelligence, pp. 212–215 (2016). 7743286Google Scholar
  20. 20.
    Söderberg, R., et al.: An information and simulation framework for increased quality in welded components. CIRP Ann. – Manuf. Technol. (2018). Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Manufacturing, School of Mechanical EngineeringVellore Institute of TechnologyVelloreIndia

Personalised recommendations