Study on Reduction in Shrinkage Defects in HPDC Component by Optimization of Localized Squeezing Process

  • Prashant BorlepwarEmail author
  • Shivkumar Biradar


In high-pressure die casting component, shrinkage defects plays a major role in leakage of fluid from components; therefore, it becomes necessary to predict the exact location of the shrinkage defect to reduce its intensity to an acceptable level. Nowadays, a localized squeezing process is one of the popular ways of reducing the shrinkage defect in high-pressure die casting components. Squeeze pins can be used to compensate for shrinkage defects in these components. The main reason for the formation of shrinkage porosity at the critical location of a given component is large and poorly fed hot spot. In this paper, shrinkage defects are reduced from level III to level I by determining optimum values of squeeze pin parameters by DOE and flow simulation, obtained results are implemented in order to test and verify effectiveness of the method. An excellent agreement is indicated for the simulation result and the experimental results.


squeezing process flow simulation HPDC process shrinkage defect 


  1. 1.
    E. Niyama, T. Uchida, M. Morikawa, S. Saito, Am. Foundrymen’s Soc. Int. Cast. Met. J. 7(3), 52–63 (1982)Google Scholar
  2. 2.
    S. Polyakov, A. Korotchenko, J. Bast, Use of the Niyama criterion to predict porosity of the mushy zone with deformation. Arch. Found. Eng. 11, 131–136 (2011)Google Scholar
  3. 3.
    E. Anglada, A. Meléndez, I. Vicario, E. Arratibel, G. Cangas, Simplified models for high pressure die casting simulation. Proc. Eng. 132, 974–981 (2015)CrossRefGoogle Scholar
  4. 4.
    H. Yuanyuan, J. Xie, Z. Liu, Q. Ding, W. Zhu, J. Zhang, W. Zhang, CA Method with machine learning for simulating the grain and pore growth of aluminum alloys. Comput. Mater. Sci. 142, 244–254 (2018)CrossRefGoogle Scholar
  5. 5.
    S. Singh, A.I. Kanli, S. Sevgen, A general approach for porosity estimation using artificial neural network method: a case study from Kansas gas field. Stud. Geophys. Geod. 60(1), 1–11 (2016)CrossRefGoogle Scholar
  6. 6.
    I. Ghosh, S.K. Das, N. Chakraborty, An artificial neural network model to characterize porosity defects during solidification of a356 aluminum alloy. Neural Comput. Appl. 25(3), 653–662 (2014)CrossRefGoogle Scholar
  7. 7.
    R. Ashiri, F. Karimzadeh, B. Niroumand, On effect of squeezing pressure on microstructural characteristics heat, treatment response and electrical conductivity of an Al Si Mg Ni Cu alloy. Mater. Sci. Technol. 30, 1162–1169 (2014)CrossRefGoogle Scholar
  8. 8.
    A.R. Adamane, L. Arnberg, E.E. Fiorese, G. Timelli, F. Bonollo, Influence of injection parameters on the porosity and tensile properties of high pressure die cast Al Si alloys: a review. Am. Found. Soc. 9, 43–53 (2015)Google Scholar
  9. 9.
    M.R. Bodhayana, N. Ramesha, Tool design for pressure dies casting of Housing Component. Int. J. Theory Appl. Res. Mech. Eng. 3, 30–33 (2014)Google Scholar
  10. 10.
    H.M. ManjunathSwamy, J.R. Nataraj, C.S. Prasad, Design optimization of gating system by fluid flow and solidification simulation for front axle housing. Int. J. Eng. Res. Dev. 4, 83–88 (2012)Google Scholar
  11. 11.
    Y. Ling, J. Zhou, H. Nan, Y. Yin, X. Shen, A shrinkage cavity prediction model for gravity castings based on pressure distribution: a casting steel case. J. Manuf. Process. 26, 433–445 (2017)CrossRefGoogle Scholar
  12. 12.
    S. Yue, G. Wang, F. Yin, Y. Wang, J. Yang, Application of an integrated CAD/CAE/CAM system for die casting dies. J. Mater. Process. Technol. 139, 465–468 (2003)CrossRefGoogle Scholar
  13. 13.
    H.-J. Kwon., H.-K. Kwon: Computer aided engineering (CAE) simulation for the design optimization of gate system on high pressure die casting (HPDC) process, in Robotics and Computer–Integrated Manufacturing, pp. 1–7 (2018)Google Scholar
  14. 14.
    KCh. Apparao, A.K. Birru, Optimization of Die casting process based on Taguchi approach. Mater. Today: Proc. 4, 1852–1859 (2017)CrossRefGoogle Scholar
  15. 15.
    B.H. Hu, K.K. Tong, X.P. Niu, I. Pinwill, Design and optimization of runner and gating systems for the die casting of thin-walled magnesium telecommunication parts through numerical simulation. J. Mater. Process. Technol. 105, 128–133 (2000)CrossRefGoogle Scholar
  16. 16.
    P. Vispute, D. Chaudhari, Utilizing flow simulation in the design phase of a casting die to optimize design parameters and defect analysis. Mater. Today Proc. 4, 9256–9263 (2017)CrossRefGoogle Scholar
  17. 17.
    A.R. Adamane, L. Arnberg, E. Fiorese et al., Influence influence of injection parameters on the porosity and tensile properties of high-pressure die cast al-si alloys- a review. Int. J. Metalcast. 9, 9–43 (2015)CrossRefGoogle Scholar
  18. 18.
    H. Dini, N.-E. Andersson, Anders E.W. Jarfors, Effect of process parameters on distortion and residual stress of high pressure die cast AZ91D components. Int. J. Metalcast. 12–3, 487–497 (2018)CrossRefGoogle Scholar

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© American Foundry Society 2019

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringMITAurangabadIndia

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