Procedure development for predicting the sink mark of injection molded thermoplastics by finite element method

  • Xianjun SunEmail author
  • Patricia Tibbenham
  • Danielle Zeng
  • Xuming Su
  • Shiyao Huang
  • Hong-tae Kang


The sink mark is one common defect of the injection-molded thermoplastics, which is harmful to the part appearance. A procedure for the sink mark prediction was proposed to build the finite element method (FEM) model in Abaqus. The temperature and pressure distributions were imported as boundary conditions at a different time from the injection molding simulation software Moldflow. The user subroutines UEXPAN and UMAT were coded and implemented in Abaqus to describe the pressure-volume-temperature (PVT) behaviors under actual cooling conditions and the polymer constitutive relationship. The PVT curves of the thermoplastic were obtained by the piston-die method at a cooling rate of 5 °C/min. The crystallizing behaviors were measured by a differential scanning calorimetry and adapted to modify the PVT curves at different cooling rates. The tension-compression tests at different temperatures were performed to obtain the stress-strain curves. The simulation of injection molding was conducted in the software Moldflow. The deflection tests were conducted on the coordinate measuring machine (CMM) to validate the predicted sink mark. It was found that the subroutines of UEXPAN described the volume variations under different cooling rates successfully. Furthermore, the subroutine UMAT predicted stress-strain curves successfully at different temperatures and strain rates. The predicted sink mark in Abaqus agreed well with the measured one. The sink mark depth from Abaqus reached one third of the experimental one and had obvious improvement than that from Moldflow.


Sink mark Injection molding Simulation PVT Constitutive relationship 


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The authors are grateful to John Lasecki, Robert Frisch, and Dr. Jianghui Mao for their kind help on the measurement of the deflection and modeling in Abaqus.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Xianjun Sun
    • 1
    Email author
  • Patricia Tibbenham
    • 2
  • Danielle Zeng
    • 2
  • Xuming Su
    • 2
  • Shiyao Huang
    • 3
  • Hong-tae Kang
    • 1
  1. 1.College of Engineering and Computer ScienceUniversity of Michigan-DearbornDearbornUSA
  2. 2.Research and Innovation CenterFord Motor CompanyDearbornUSA
  3. 3.Ford Motor Research and Engineering(Nanjing) Co., LtdNanjing CityChina

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