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Journal of Failure Analysis and Prevention

, Volume 19, Issue 3, pp 688–697 | Cite as

Optimization of Keyway Broach Design

  • Riad AhmadEmail author
  • Hani Al-Rawashdeh
  • Ahmad O. Hasan
Technical Article---Peer-Reviewed
  • 50 Downloads

Abstract

This study presents an approach to developing a method to generate an optimized keyway broach tool design based on constant cutting forces. The obtained experimental data, as a result of the studies carried out by the method of photomechanics on models of optically sensitive material, allow us to recommend it in the practice of calculating keyway broaches. The results obtained on keyway broach models can be transferred to the real structure, which is made of tool material, taking into account the geometric and force similarity.

Keywords

Stress concentration Keyway broach Stress analysis Broach optimization 

Notes

References

  1. 1.
    C. Monday, Broaching (Machinery Publication, London, 1960)Google Scholar
  2. 2.
    E. Kokmeyer, Better Broaching Operations, 1st edn. (Society of Manufacturing Engineers, Dearborn, 1984)Google Scholar
  3. 3.
    W.R. Terry, K.W. Cutright, Computer aided design of a broaching process. Comput. Ind. Eng. 11(1–4), 576–580 (1986).  https://doi.org/10.1016/0360-8352(86)90157-9 Google Scholar
  4. 4.
    W.R. Terry, R. Karni, Y.J. Huang, Concurrent tool and production system design for a surface broach cutting tool: a knowledge-based systems approach. Int. J. Prod. Res. 30(2), 219–240 (1992)Google Scholar
  5. 5.
    V. Sajeev, L. Vijayaraghavan, U. Rao, An analysis of the effects of burnishing in internal broaching. Int. J. Mech. Eng. Educ. 28(2), 163–173 (2000).  https://doi.org/10.7227/ijmee.28.2.5 Google Scholar
  6. 6.
    V. Sajeev, L. Vijayaraghavan, U. Rao, Effect of tool-work deflections on the shape of a broached hole. Int. J. Mech. Eng. Educ. 28(1), 88 (2000).  https://doi.org/10.7227/ijmee.28.1.7 Google Scholar
  7. 7.
    D.A. Gonçalves, R.B. Schroeter, Modeling and simulation of the geometry and forces associated with the helical broaching process. Int. J. Adv. Manuf. Technol. 83(1–4), 205–215 (2015).  https://doi.org/10.1007/s00170-015-7578-0 Google Scholar
  8. 8.
    P. Vogtel, F. Klocke, H. Puls, S. Buchkremer, D. Lung, Modelling of process forces in broaching Inconel 718. Procedia CIRP 8, 409–414 (2013).  https://doi.org/10.1016/j.procir.2013.06.125 Google Scholar
  9. 9.
    G. Ortiz-de-Zarate, A. Madariaga, A. Garay, L. Azpitarte, I. Sacristan, M. Cuesta, P.J. Arrazola, Experimental and FEM analysis of surface integrity when broaching Ti64. Procedia CIRP 71, 466–471 (2018).  https://doi.org/10.1016/j.procir.2018.05.033 Google Scholar
  10. 10.
    A. Axinte, N. Gindy, Tool condition monitoring in broaching. Wear 254(3–4), 370–382 (2003).  https://doi.org/10.1016/s0043-1648(03)00003-6 Google Scholar
  11. 11.
    A. Axinte, F. Bound, J. Penny, N. Gindy, Broaching of Ti-6-4—detecting of workpiece surface anomalies on dovetail slots through process monitoring. CIRP Ann. Manuf. Technol. 54(1), 87–90 (2005).  https://doi.org/10.1016/s0007-8506(07)60056-0 Google Scholar
  12. 12.
    L. Vijayaraghavan, R. Krishnamurthy, H. Chandrasekaran, Evaluation of stress and displacement of tool and workpiece on broaching. Int. J. Mach. Tool Des. Res. 21(3–4), 263–270 (1981).  https://doi.org/10.1016/0020-7357(81)90023-8 Google Scholar
  13. 13.
    J.W. Sutherland, E.J. Salisbury, F.W. Hoge, A model for the cutting force system in the gear broaching process. Int. J. Mach. Tools Manuf. 37, 1409–1421 (1997).  https://doi.org/10.1016/s0890-6955(97)00014-x Google Scholar
  14. 14.
    A. Hosseini, H.A. Kishawy, On the optimized design of broaching tools. J. Manuf. Sci. Eng. 136(1), 011011 (2013).  https://doi.org/10.1115/1.4025415 Google Scholar
  15. 15.
    D. Fabre, C. Bonnet, J. Rech, T. Mabrouki, Optimization of surface roughness in broaching. CIRP J. Manuf. Sci. Technol. 18, 115–127 (2017).  https://doi.org/10.1016/j.cirpj.2016.10.006 Google Scholar
  16. 16.
    U. Kokturk, Optimization of Broaching Tool Design, M. Sc. thesis, Industrial Engineering, Sabanci University, Istanbul, Turkey, 2004Google Scholar
  17. 17.
    P. Vogtel, F. Klocke, D. Lung, S. Terzi, Automatic broaching tool design by technological and geometrical optimization. Procedia CIRP 33, 496–501 (2015).  https://doi.org/10.1016/j.procir.2015.06.061 Google Scholar
  18. 18.
    R. Kamath Cholpadi, A. Kuttan, Mechanistic force modeling for broaching process. Int. J. Manuf. Eng. (2014).  https://doi.org/10.1155/2014/485712 Google Scholar
  19. 19.
    V. Sajeev, L. Vijayaraghavan, U.R.K. Rao, An analysis of the effects of burnishing in internal broaching. Int. J. Mech. Eng. Educ. 28(2), 163–173 (2000).  https://doi.org/10.7227/ijmee.28.2.5 Google Scholar
  20. 20.
    D.A. Axinte, N. Gindy, K. Fox, I. Unanue, Process monitoring to assist the workpiece surface quality in machining. Int. J. Mach. Tools Manuf 44(10), 1091–1108 (2004).  https://doi.org/10.1016/j.ijmachtools.2004.02.020 Google Scholar
  21. 21.
    D.A. Stephenson, J.S. Agapiou, Metal Cutting Theory and Practice (Marcel Dekker, New York, 1997)Google Scholar
  22. 22.
    R. Saravanan, P. Asokan, M. Sachidanandam, A multi-objective genetic algorithm (GA) approach for optimization of surface grinding operations. Int. J. Mach. Tools Manuf. 42(12), 1327–1334 (2002).  https://doi.org/10.1016/s0890-6955(02)00074-3 Google Scholar
  23. 23.
    N. Alberti, G. Perrone, Multipass machining optimization by using fuzzy possibilistic programming and genetic algorithms. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 213(3), 261–273 (1999).  https://doi.org/10.1243/0954405991516741 Google Scholar
  24. 24.
    Z. Khan, B. Prasad, T. Singh, Machining condition optimization by genetic algorithms and simulated annealing. Comput. Oper. Res. 24(7), 647–657 (1997).  https://doi.org/10.1016/s0305-0548(96)00077-9 Google Scholar
  25. 25.
    Y.C. Shin, Y.S. Joo, Optimization of machining conditions with practical constraints. Int. J. Prod. Res. 30(12), 2907–2919 (1992).  https://doi.org/10.1080/00207549208948198 Google Scholar
  26. 26.
    K. Challa, P.B. Berra, Automated planning and optimization of machining processes: a systems approach. Comput. Ind. Eng. 1(1), 35–46 (1976).  https://doi.org/10.1016/0360-8352(76)90006-1 Google Scholar
  27. 27.
    S.S. Rao, L. Chen, Determination of optimal machining conditions: a coupled uncertainty model. J. Manuf. Sci. Eng. 122(1), 206 (2000).  https://doi.org/10.1115/1.538898 Google Scholar
  28. 28.
    I. Erol, W.G. Ferrell, A methodology for selection problems with multiple, conflicting objectives and both qualitative and quantitative criteria. Int. J. Prod. Econ. 86(3), 187–199 (2003).  https://doi.org/10.1016/s0925-5273(03)00049-5 Google Scholar
  29. 29.
    R. Ahmad, A.O. Hasan, H. Al-Rawashdeh, Photoelastic stress analysis of crankpin fillets of a crankshaft. J Fail. Anal. Prev. 19(2), 476–487 (2019).  https://doi.org/10.1007/s11668-019-00618-w Google Scholar
  30. 30.
    M.M. Leven, Epoxy resins for photoelastic use, in Photoelasticity, ed. by M.M. Frocht (Pergamon Press Inc, New York, 1963)Google Scholar
  31. 31.
    M.M. Frocht, Photoelasticity (Pergamon Press Inc, New York, 1963)Google Scholar
  32. 32.
    A. Freddi, G. Olmi, C. Luca, Experimental Stress Analysis for Materials and Structures, vol. 3 (Springer, Berlin, 2015).  https://doi.org/10.1007/978-3-319-06086-6 Google Scholar
  33. 33.
    A.J. Muminovic, I. Saric, N. Repcic, Numerical analysis of stress concentration factors. Procedia Eng. 100, 707–713 (2015).  https://doi.org/10.1016/j.proeng.2015.01.423 Google Scholar

Copyright information

© ASM International 2019

Authors and Affiliations

  • Riad Ahmad
    • 1
    Email author
  • Hani Al-Rawashdeh
    • 1
  • Ahmad O. Hasan
    • 1
  1. 1.Department of Mechanical Engineering, Faculty of EngineeringAl-Hussein Bin Talal UniversityMa’anJordan

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