Journal of Mechanical Science and Technology

, Volume 30, Issue 11, pp 5143–5152 | Cite as

Minimization of variation in volumetric shrinkage and deflection on injection molding of Bi-aspheric lens using numerical simulation

  • R. Joseph Bensingh
  • S. Rajendra Boopathy
  • C. Jebaraj


The profile of a bi-aspheric lens is such a way that the thickness narrows down from center to periphery (convex). Injection molding of these profiles has high shrinkage in localized areas, which results in internal voids or sink marks when the part gets cool down to room temperature. This paper deals with the influence of injection molding process parameters such as mold surface temperature, melt temperature, injection time, V/P Switch over by percentage volume filled, packing pressure, and packing duration on the volumetric shrinkage and deflection. The optimal molding parameters for minimum variation in volumetric shrinkage and deflection of bi-aspheric lens have been determined with the application of computer numerical simulation integrated with optimization. The real experimental work carried out with optimal molding parameters and found to have a shallow and steep surface profile accuracy of 0.14 and 1.57 mm, 21.38-45.66 and 12.28-26.90 μm, 41.56-157.33 and 41.56-157.33 nm towards Radii of curvatures (RoC), surface roughness (Ra) and waviness of the surface profiles (profile error Pt), respectively.


Bi-Aspheric lens Injection molding Numerical simulation Taguchi and variation in volumetric shrinkage 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    X. Chen and F. Gao, A study of packing profile on injection molded part quality, Material Science and Engineering: A, 358 (2003) 205–213.CrossRefGoogle Scholar
  2. [2]
    T. C. Chang and E. Faison, Shrinkage behaviour and optimization of injection molded parts studied by the Taguchi method, Polym. Eng. Sci., 41 (2001) 703–710.CrossRefGoogle Scholar
  3. [3]
    M. Altan, Reducing shrinkage in injection moldings via the Taguchi, ANOVA and neural network methods, Mater Design, 31 (2010) 599–604.Google Scholar
  4. [4]
    G. H. Hu and Z. S Cui, Effect of packing parameters and gate size on shrinkage of aspheric lens parts, J. Snanghai Jiaotong Univ (Sci), 15 (2010) 84–87.CrossRefGoogle Scholar
  5. [5]
    N. Bhagavatula, D. Michalski, B. Lilly and G. Glozer, Modelling and verification of ejection forces in thermoplastic injection molding, Modelling Simul. Mater. Sci. Eng., 12 (2004) 239 -S254.CrossRefGoogle Scholar
  6. [6]
    R. Thomas and N. McCaffery, The prediction of real product shrinkages, calculated from a simulation of the injection molding process, Annual Technical Conf. (1989) 371–375.Google Scholar
  7. [7]
    K. M. Tsai, Effect of injection molding process parameters on optical properties of lenses, Appl. Opt., 49 (2010) 6149–6159.CrossRefGoogle Scholar
  8. [8]
    T. S. Kwak, T. Suzuki, W. B. Baeb, Y. Uehara and H. Ohmori, Application of neural network and computer simulation to improve surface profile of injection molding optic lens, J. Mater. Process. Technol., 170 (2005) 24–31.CrossRefGoogle Scholar
  9. [9]
    H. E. Lai and P. J. Wang, Study of process parameters on optical qualities for injection-moulded plastic lenses, Appl. Opt., 47 (2008) 2017–2027.CrossRefGoogle Scholar
  10. [10]
    X. Lu and L. S. Khim, A statistical experimental study of the injection moulding of optical lenses, J. Mater. Process. Technol., 113 (2001) 189–195.CrossRefGoogle Scholar
  11. [11]
    A. M. C. Aghanajafi and A. M. C. Ghazvin, Thermal analysis of HVAC and solar panels using genetic optimization algorithm, J. of Mechanical Science and Technology, 30 (3) (2016) 1405–1412.CrossRefGoogle Scholar
  12. [12]
    J. Beaumont, Shear induces flow imbalance and melt flipper in Autodesk Moldflow injection molding simulation, Autodesk University (2011).Google Scholar
  13. [13]
    R. Harold, Analysis of variance in experimental design, New York, USA. Springer (1992).zbMATHGoogle Scholar
  14. [14]
    D. C. Montgomery, Design and analysis of experiments, Fifth Ed., New York: Wiley (2001) 65–72.Google Scholar
  15. [15]
    S. W. Kim and L. S. Turng, Developments of threedimensional computer-aided engineering simulation for injection moulding, Modelling Simul. Mater. Sci. Eng., 12 (2004) 151–173.CrossRefGoogle Scholar
  16. [16]
    G. Taguchi and S. Konishi, Taguchi method, orthogonal arrays and linear graphs: Tools for quality engineering, American Supplier Institute (1987) 35–58.Google Scholar
  17. [17]
    G. Venkateshwarlu, M. J. Davidson and G. R. N. Tagore, Influence of process parameters on the cup drawing of aluminium 7075 sheet, Int. J. of Engg., Sci and Tech., 2 (11) (2010) 41–49.Google Scholar

Copyright information

© The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • R. Joseph Bensingh
    • 1
  • S. Rajendra Boopathy
    • 2
  • C. Jebaraj
    • 3
  1. 1.Central Institute of Plastics Engineering and TechnologyChennaiIndia
  2. 2.College of EngineeringAnna UniversityChennaiIndia
  3. 3.Vellore Institutes of TechnologyChennaiIndia

Personalised recommendations