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Thermoplastic Library

The thermoplastic library includes the following material models. Each of these models provides unique and powerful predictions for different classes of thermoplastics. Each material model is available for both implicit and explicit FE simulations, and is suitable for large deformation analyses.

 Material Model Name



Linear Elastic (LE)

Linear elastic model that also incorporates the PolyUMod failure models.



Neo-Hookean (NH)

Neo-Hookean hyperelastic model that supports the PolyUMod failure models.



Eight-Chain (EC)

Arruda-Boyce eight-chain hyperelastic model that supports the PolyUMod failure models [J. Mech. Phys. Solids, 41, 389-412, 1993].  Uses a more accurate implementation of the inverse Langevin function than most other commercial implementations.


Anisotropic Eight-Chain (AEC) 

Anisotropic version of the eight-chain model [Bischoff et al, Trans. ASME, 69, 570-579, 2002]. 
  Supports different failure models



Arruda-Boyce (AB) 

Arruda-Boyce viscoplastic model for thermoplastic materials [Mech. Mater., 19, 193-212, 1995].
  Supports different failure models


Dual Network
Fluoropolymer (DNF) 

Specifically developed for fluoropolymers, (e.g. Teflon), but is suitable for many thermoplastics [Mech. Materials, 37, 899-913, 2005].
  Strain-rate effects
  Temperature effects
  Both viscoelastic and viscoplastic behavior
  Supports different failure models


Hybrid Model (HM)

Specifically developed for UHMWPE, but is suitable for many thermoplastics [Biomaterials, 24, 1365-1380, 2003].


Three-Network (TNM) 

The TNM is an alternative to the Hybrid model that gives similar quality predictions but also includes temperature effects and is more numerically efficient [SES, 45th Annual Meeting, 2008].
  Large deformation behavior
  Strain-rate effects
  Temperature effects
  Supports different failure models


Parallel Network (PNM) 

Advanced model that contains an arbitrary number of elastic and flow elements connected in parallel.
  Suitable for large deformation, thermomechanical predictions of many different classes of polymers
  Supports different failure models


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