Reliability assessment of display delamination considering adhesive properties based on statistical model calibration and validation

  • Jung Suk Nah
  • Jongsoo LeeEmail author


In this study, the delamination status of a display in response to a pad-drop impact is investigated using a computer simulation. Furthermore, reliability of display delamination and stress is assessed, considering the uncertainty factors such as material properties and noise that affect the degree of delamination. Considering that adhesive properties of optical clear adhesive are required to observe the degree of delamination, cohesive zone model is formed, and cohesive parameters are determined by comparing the results of peel test and finite element analysis. In this process, statistical model calibration and validation comprising three steps is employed: uncertainty propagation, statistical model calibration, and statistical model validation. The probability distributions of adhesive properties obtained by this model are compared with those obtained by a deterministic model. The result reveals that the statistical model calibration and validation decreases the cost while retaining the predictive capability. In addition, the reliability of display delamination is evaluated, considering the adhesive properties and the experimental conditions having uncertainties as variables. Based on the variables, the uncertainty of the response function is propagated, and the delamination probability is predicted. The study helps establish that the failure of display delamination in the case of a pad drop simulation can be predicted statistically through reliability assessment.


Delamination Adhesive properties Statistical model calibration and validation Pad drop simulation Reliability assessment 



This research is supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Science, ICT & Future Planning (2017R1A2B4009606).


  1. ABAQUS: User’s Guide, Version 2018. Dassault Systemes Simulia, Inc., Providence (2018)Google Scholar
  2. António, C.C., Hoffbauer, L.N.: Uncertainty propagation in inverse reliability-based design of composite structures Int. J. Mech. Mater. Des. 6, 89–102 (2010)CrossRefGoogle Scholar
  3. Blackman, B.R.K., Hadavinia, H., Kinloch, A.J., Williams, J.G.: The use of a cohesive zone model to study the fracture of fibre composites and adhesively-bonded joints. Int. J. Fract. 19, 25–46 (2003)CrossRefGoogle Scholar
  4. Chae, Y., Chae, G.S., Youn, Y.O., Woo, S., Shin, S.H., Lee, J.: Optimal design of thickness and Young’s modulus of multi-layered foldable structure considering bending stress, neutral plane and delamination under 2.5 mm radius of curvature. Int. J. Precis. Manuf. 19(8), 1143–1154 (2018)CrossRefGoogle Scholar
  5. Cui, S., Blackman, B.R.K., Kinloch, A.J., Talyor, A.: Duability of asphalt mixtures: effect of aggregate type and adhesion promoters. Int. J. Adhes. Adhes. 54, 100–111 (2014)CrossRefGoogle Scholar
  6. Dillard, D.A., Pocius, A.V.: The Mechanics of Adhesion. Elsevier, New York (2002)Google Scholar
  7. Doh, J., Lee, J., Ahn, H.S., Kim, S.W., Kim, S.H.: Reliability based design of the automotive components considering degradation properties of polymeric materials. Trans. KSAE 24(5), 596–604 (2016)CrossRefGoogle Scholar
  8. Ferson, S., Oberkampf, W.L., Ginzburg, L.: Model validation and predictive capability for the thermal challenge problem. Comput. Appl. Mech. Eng. 197, 2408–2430 (2008)CrossRefzbMATHGoogle Scholar
  9. Fonseca, J.R., Friswell, M.I., Mottershead, J.E., Leesa, A.W.: Uncertainty identification by the maximum likelihood method. J. Sound Vib. 288, 587–599 (2005)CrossRefGoogle Scholar
  10. Georgiou, I., Hadavinia, H., Ivankovic, A., Kinloch, A.J., Tropsa, V., Williams, J.G.: Cohesive zone models and the plastically deforming peel test. J. Adhes. 79, 239–265 (2003)CrossRefGoogle Scholar
  11. Gorelchenko, P., Zhang, B., and Hu, G.: Cover glass behavior in handheld device drop: modeling; validation and design evaluation. In: ASTR (2016)Google Scholar
  12. Jacobs, J.H., Etman, L.F.P., Keulen, F., Rooda, J.E.: Framework for sequential approximate optimization. Struct. Multidisc. Optim. 27, 384–400 (2004)CrossRefGoogle Scholar
  13. Jung, B.C., Park, J., Oh, H., Kim, J., Youn, B.D.: A framework of model validation and virtual product qualification with limited experimental data based on statistical inference. Struct. Multidisc. Optim. 51, 573–583 (2015)CrossRefGoogle Scholar
  14. Khuri, A.I., Mukhopadhyay, S.: Response surface methodology. Wiley Interdiscip. Rev. Comput. Stat. 2, 128–149 (2010)CrossRefGoogle Scholar
  15. Lee, C.C., Shih, Y.S., Wu, C.S., Tsai, C.H., Yeh, S.T., Peng, Y.H., Chen, K.J.: Development of robust flexible OLED encapsulations using simulated estimations and experimental validations. J. Phys. D Appl. Phys. 45, 275102 (2012)CrossRefGoogle Scholar
  16. Lee, C.J., Lee, S.K., Ko, D.C., Kim, B.M.: Evaluation of adhesive properties using cohesive zone model: mode I. Trans. KSME A 33(5), 474–481 (2009)CrossRefGoogle Scholar
  17. Liu, S., Wang, X., Ma, B., Gan, Z., Zhang, H.: Drop test and simulation of portable electronic devices. In: 2005 6th International Conference on Electronic Packaging Technology, vol. 6 (2005)Google Scholar
  18. Liu, Y., Chen, W., Arendt, P., Huang, H.Z.: Toward a better understanding of model validation metrics. J. Mech. Des. 133, 071005 (2011)CrossRefGoogle Scholar
  19. Mohammed, I.K., Charalambides, M.N., Kinloch, A.J.: Modelling the interfacial peeling of pressure-sensitive adhesives. J. Nonlinear Fluid Mech. 222, 141–150 (2002)MathSciNetCrossRefGoogle Scholar
  20. Moore, D.R., Williams, J.G.: A protocol for determination of the adhesive fracture toughness of flexible laminates by peel testing: fixed arm and T-peel methods. Comput. Struct. 183, 320–330 (2018)CrossRefGoogle Scholar
  21. Nguyen, K.-H., Ju, H.-W., Truong, V.-H., Kweon, J.-H.: Delamination analysis of multi-stage composite curved beam using an out-of-autoclave material. Compos. Struct. 183, 320–330 (2018)CrossRefGoogle Scholar
  22. Oberkampf, W.L., Trucano, T.G., Hirsch, C.: Verification, validation, and predictive capability in computational engineering and physics. Appl. Mech. Rev. 57, 345–384 (2004)CrossRefGoogle Scholar
  23. Rosa Paiva, M.M., António, C.C., da Silva Lucas, F.M.: Multiobjective optimization of mechanical properties based on the composition of adhesives. Int. J. Mech. Mater. Des. 13, 1–24 (2017)CrossRefGoogle Scholar
  24. Ramamurthi, M., Lee, J.S., Yang, S.H., Kim, Y.S.: Delamination characterization of bonded interface in polymer coated steel using surface based cohesive model. Int. J. Precis. Eng. Manuf. 14(10), 1755–1765 (2013)CrossRefGoogle Scholar
  25. Shin, C.M., Oh, H.C., Kim, K.Y., Cheong, N.R., Lee, B.C.: Numerical analysis of the ball drop test for the impact resistance properties of the high-hardness plastic cover. Proceedings of the Society of CAD/CAM Conference, pp. 163–169 (2013)Google Scholar
  26. Thacker, B.H.: ASME Standards Committee on Verification and Validation in Computational Solid Mechanics. ASME, New York (2001)Google Scholar
  27. Youn, B.D., Jung, B.C., Xi, Z., Kim, S.B., Lee, W.R.: A hierarchical framework for statistical model calibration in engineering product development. Comput. Appl. Mech. Eng. 200, 1421–1431 (2011)CrossRefzbMATHGoogle Scholar
  28. Yun, F., Tsai, C.C., Tsai, J.L.: Characterizing mechanical behaviors of a flexible AMOLED during the debonding process. Microsyst. Technol. 22, 2397–2406 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.School of Mechanical EngineeringYonsei UniversitySeoulKorea

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