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PTFE (Teflon) Material Models


Fluoropolymers (PTFE, Teflon) have many unique and useful mechanical properties: very low friction coefficient, excellent chemical resistance, and a ductile response. Performing accurate FE simulations of components made of PTFE can be challenging due to the early onset of non-linear viscoplastic flow. Another challenging aspect of fluoropolymer modeling is that these materials often have a very high melt viscosity which can lead to microporosity which in turn can give the material different flow resistance (yield stress) in tension and compression. In this study I examined how different commonly used material models can predict the large strain time-dependent response of this important material class.

Experimental Data Used for the PTFE Material Model Study

In this study I used uniaxial tension and compression data at different strain rates, and one volumetric compression data set. The data that I used comes with MCalibration (see image below), so you can easily reproduce my study if you are interested.

PTFE Data in MCalibration
PTFE Experimental Data

Figure 1. Experimental data used in the study.

In the following sections I will go through 8 different candidate material models in order from worst to best!

Results #8: Abaqus Elastic-Plastic with Creep


Figure 2. The Abaqus elastic-plastic with isotropic hardening and creep has an average error of 29%. This model cannot predict a different yield stress in compression vs tension, and the unloading predictions are bad. As expected, a metal plasticity model is not good here.

Results #7: Ansys MISO Plasticity with Creep

Figure 3. The Ansys MISO plasticity model with creep has the same problems as the Abaqus elastic-plastic model. The predictions are not good.

Results #6: Abaqus Elastic-Plastic-Combined Hardening


Figure 4. The Abaqus elastic-plastic with combined kinematic hardening is essentially the same as the Ansys Chaboche model. The predictions from this model “look better” than the predictions from the isotropic plasticity models. The error in the models predictions is still very large.

Results #5: Bergstrom-Boyce Viscoplasticity

Figure 5. The Bergstrom-Boyce (BB) model was developed for elastomers, and does not accurately capture the unloading response of thermoplastics.

Results #4: PolyUMod DNF

Figure 6. The Dual Network Fluoropolymer (DNF) model is part of the PolyUMod library. The DNF model is reasonably accurate, but not accurate enough to be one of the top 3 models.

Results #3: Abaqus PRF


Figure 7. The average error for the Abaqus PRF model with 3 networks (Yeoh + Power flow) is 10.8%. Not too bad, but the error is 23% larger than the best model. Particularly the unloading predictions are weak.

Results #2: Ansys TNM


Figure 8. The Ansys (and PolyUMod) TNM model is a good material model for many thermoplastics. In this case the average error is 10.2%. Not too bad.

Results #1: PolyUMod TNV

Best PTFE Model - TNV

Figure 9. Once again, the PolyUMod TNV model is the most accurate material model for a thermoplastic. The average error is 8.8%. Excellent.

Summary PTFE Material Model Study

Here is a comparison between the different material models. The PolyUMod TNV model is the most accurate model for PTFE. Note that I have previously shown that the TNV model is also the most accurate model for thermoplastic-elastomers and PEEK). If you have not tried the TNV model, then request a free trial license.


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