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Generation of Sequence of Machining Operations Through Visualization of End Product

  • G. V. S. S. SharmaEmail author
  • P. Srinivasa Rao
  • B. Surendra Babu
Conference paper
Part of the Lecture Notes on Multidisciplinary Industrial Engineering book series (LNMUINEN)

Abstract

Geometric modeling in a computer facilitates spatial visualization of the component. This visualization is applied for generating the manufacturing process sequence of the component. In the present work, a generic procedure for generating machining process sequence through spatial visualization is proposed and specifically applied onto a case study of connecting rod manufacturing. A graphical tree approach is adopted for identification of locating surface for succeeding machining operations. Cluster diagram groups the similar machining operations in order to simplify the machining information flow and lays the foundation for the machining process route. The spatially visualized process sequencing (SVPS) procedure charted in this paper helps the process engineers in charting the manufacturing process sequence in the design stage and before production of the component. The present study employs the spatially visualized computer-aided geometric model, rooted tree graph and cluster diagram for the industrial case study on connecting rod process sequencing.

Keywords

Spatial visualization Machining process sequence Cluster diagram Spatially visualized computer aided process sequencing 

References

  1. 1.
    Niu, Z., Martin, R.R., Langbein, F.C., Sabin, M.A.: Rapidly finding CAD features using database optimization. Comput. Aided Des. 69, 35–50 (2015).  https://doi.org/10.1016/j.cad.2015.08.001CrossRefGoogle Scholar
  2. 2.
    Chu, X., Tso, S., Tu, Y.: A novel methodology for computer-aided process planning. Int. J. Adv. Manuf. Technol. 16(10), 714–719 (2000).  https://doi.org/10.1007/s001700070023CrossRefGoogle Scholar
  3. 3.
    Sadaiah, M., Yadav, D., Mohanram, P., Radhakrishnan, P.: A generative computer-aided process planning system for prismatic components. Int. J. Adv. Manuf. Technol. 20(10), 709–719 (2002).  https://doi.org/10.1007/s001700200228CrossRefGoogle Scholar
  4. 4.
    Sharma, G., Dumpala, R.: Teaching of mechanical engineering concepts through three-dimensional geometric modeling. Int. J. Mech. Eng. Educ. 0306419015603013 (2015).  https://doi.org/10.1177/0306419015603013CrossRefGoogle Scholar
  5. 5.
    Deja, M., Siemiatkowski, M.S.: Feature-based generation of machining process plans for optimised parts manufacture. J. Intell. Manuf. 24(4), 831–846 (2013).  https://doi.org/10.1007/s10845-012-0633-xCrossRefGoogle Scholar
  6. 6.
    Subrahmanyam, S., Wozny, M.: An overview of automatic feature recognition techniques for computer-aided process planning. Comput. Ind. 26(1), 1–21 (1995).  https://doi.org/10.1016/0166-3615(95)80003-4CrossRefGoogle Scholar
  7. 7.
    Li, W., Ong, S.-K., Fuh, J.Y., Wong, Y., Lu, Y., Nee, A.Y.: Feature-based design in a distributed and collaborative environment. Comput. Aided Des. 36(9), 775–797 (2004).  https://doi.org/10.1016/j.cad.2003.09.005CrossRefGoogle Scholar
  8. 8.
    Babic, B., Nesic, N., Miljkovic, Z.: A review of automated feature recognition with rule-based pattern recognition. Comput. Ind. 59(4), 321–337 (2008).  https://doi.org/10.1016/j.compind.2007.09.001CrossRefGoogle Scholar
  9. 9.
    Whybrew, K., Britton, G., Robinson, D., Sermsuti-Anuwat, Y.: A graph-theoretic approach to tolerance charting. Int. J. Adv. Manuf. Technol. 5(2), 175–183 (1990).  https://doi.org/10.1007/BF02601605CrossRefGoogle Scholar
  10. 10.
    Sharma, G., Rao, P.S., Babu, B.S.: Process-based tolerance assessment of connecting rod machining process. J. Ind. Eng. Int. 1–10 (2016).  https://doi.org/10.1007/s40092-015-0138-2CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.GMRITRajamIndia
  2. 2.Centurion UniversityParlakhemundiIndia
  3. 3.GITAMVisakhapatnamIndia

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