Durability Simulation and the Value of Product Development Resources

What value does your company gain by deploying product development resources one way vs. another when it comes to durability?

R&D organizations are built around what it takes to get the product into production.  The costs of the organization include wages for the engineers and technicians, the costs of the capital equipment used in development and testing, and the overhead from administrative functions.  These are all fixed costs, and in the rubber industry it is typical to see R&D budgets that amount to somewhere between 1% and 5% of sales.

The R&D program lifecycle is iterative.  It goes something like this: design, build, test, qualify for production, launch product.  A quick way to understand product development costs is to look at how long it takes for one design-build-test-launch iteration.  If it takes your tech center one year per iteration, then the cost of one pass through the cycle is something like (company annual sales) x (R&D rate per annual sales)/(number of parallel development programs executing at a given time in your tech center).  For a $2B company with a 2.5% research budget and 10 development programs in the works, this works out to $5M/iteration.

How much of this cost is burned on durability issues?  Potentially all of it, at least within any one given iteration.  At worst, a non-qualifying test result leads to a “back to the drawing board” restart of the iteration.  The durability tests required for qualification can only be made after the prototype is in hand, so a restart means the whole team ends up revisiting and reproducing to correct a failed iteration.  Over the long run, if your iteration failure rate is 1 in 5 iterations (20%), that means you are burning $5M x 20% = $500k per product.

How much of this cost can realistically be avoided?  The big opportunity lies in the fact that the old “build and break” paradigm does not immediately hold accountable design decisions that lead to poor durability, and it does not have enough band-width to allow for much optimization.  A “build and break” only plan is a plan for business failure.  Poor decisions are only tested and caught after big investments in the iteration have all become sunk costs.  The advent of simulation has fueled a new “right the first time” movement that empowers the engineer to very rapidly investigate and understand how alternative materials, alternative geometries, or alternative duty cycles impact durability.  The number of alternatives that can be evaluated and optimized by an analyst before committing other resources is many times greater.  “Right the first time” via simulation is a model that is increasingly favored by OEMs and suppliers because it works.  Expect to halve your iteration failure rate.


Integrated Durability Solutions for Elastomers

Will the durability of your new rubber product meet the expectations of your customers? 

Do you have a comprehensive capability that fully integrates all of the disciplines required to efficiently achieve a targeted durability spec?

Your engineers use finite element analysis (FEA) to model the elastomer component in the complex geometry and loading cycle for the desired product application.  One traditional approach to predicting durability is to develop a rough estimate of lifetime by looking at maximum principal strain or stress in relation to strain-life or stress-life fatigue curves obtained for the material using lab specimens in simple tension.  The difficulties and uncertainties with this method were discussed in a recent blog post.


A modern approach to elastomer durability is to use the Endurica CL™ durability solver for FEA.  This software uses rubber fracture mechanics principles and critical plane analysis to calculate the fatigue lifetime – which is the number of times the complex deformation cycle can be repeated before failure – for every element of the model.  This provides engineers with the ability to view lifetime throughout the FEA mesh, allowing them to modify design features or make material changes as needed to resolve short-lifetime areas.

A sound finite element model of the elastomer product in the specified loading situation and fundamental fatigue material parameters from our Fatigue Property Mapping™ testing methods are the two essential inputs to the Endurica CL software.  This is illustrated in the figure below.

The requisite elastomer characterization methods can be conducted by us through our testing services or by you in your laboratory with our testing instruments.  For some companies, consulting projects are a route to taking advantage of the software before deciding to license the unique predictive capabilities.  The following diagram shows how our products and services are integrated.

For companies that are just getting started with implementing our durability solutions, the following is a typical testing services and consulting project:

  1. We use our Fatigue Property Mapping™ testing methods, through our collaboration with Axel Products Physical Testing Services, to characterize the properties of cured sheets of rubber compounds sent to us by the client. The minimum requirements for fatigue modeling are crack precursor size and crack growth rate law, and these are quantified within our Core Fatigue Module.  Special effects like strain-induced crystallization and aging/degradation are accounted for using other testing modules when applicable.
  2. The client sends us the output files from their finite element analysis (FEA) of their elastomer part design for the deformation of their complex loading cycle. It is common for the goal to be a comparison of either two designs, two distinct loading profiles, two different rubber compounds, or combinations of these variations.  Our software is fully compatible with Abaqus™, ANSYS™, and MSC Marc™, so the simulations can be conducted on any of these FEA platforms.  In some situations where a client does not have their own FEA capabilities, one of Endurica’s engineers will set up the models and perform the analyses instead.
  3. The fatigue parameters and FEA model are inputted to Endurica CL fatigue solver to calculate values of the fatigue lifetime for every element of the model. The lifetime results are then mapped back onto the finite element mesh in Abaqus, ANSYS, or MSC Marc so that the problem areas (short lifetime regions) within the geometry can be highlighted.
  4. We review the results with the client and discuss any opportunities for improving the fatigue performance through design and material changes.

Advanced implementors of our durability solutions have licensed the Endurica CL software and are using our rubber characterization methods in their laboratories on a routine basis, with instruments provided through our partnership with Coesfeld GmbH & Co. (Germany).  One recently publicized example of a company using the Endurica approach to a very high degree is Tenneco Inc., which you can read about here.

We want to help you #GetDurabilityRight, so please contact me at cgrobertson@endurica.com if you would like to know more about how Endurica’s modern integrated durability solutions for elastomers can help enable a product development path that is faster, less expensive, and more confident.


Wohler Curves or Fracture Mechanics?

Endurica uses a fracture mechanics based description of rubber’s fatigue behavior, rather than the classical Wohler curve (ie S-N curve) approach.  This is why:

1) Wohler curves in rubber show the combined effects of several nonlinear processes, but they do not easily deconvolve into useful information about the individual processes.  This means that Wohler curve users struggle to trace the causes of fatigue failures any deeper than the single monolithic empirical SN curve.  When the customer or the boss asks why the part is failing, Wohler curve users end up falling back on the old “rubber is mysterious” defense.  Meanwhile, users of critical plane analysis + fracture mechanics are hypothesis testing. They can check what events and what loading directions are most damaging, and what material parameters (crack precursor size, strain crystallization, threshold, crack growth rate law, thermal effects, etc.) can be exploited to gain leverage and solve the issue.

2) Fatigue failure in rubber is often dominated by “special effects”: dependence on strain level, dependence on R ratio, dependence on temperature, dependence on rate, dependence on ageing, etc. The Wohler curve crowd must choose between ignoring/oversimplifying these special effects, or running an experimental matrix that rapidly scales to an infeasibly huge size as more variables are added.  While fracture mechanics users obtain a wealth of information from a single test specimen (one test can probe many different strain levels, temperatures, rates, etc), Wohler curve users obtain 1 data point per tested specimen.  Look in the rubber technical literature and count the number of S-N-curves that are given, relative to the number of fatigue crack growth rate curves.  Google/scholar returns less than 2000 results for “rubber Wohler curve”, and 78700 results for “rubber crack growth curve”.  There is a reason that crack growth rate curves outnumber Wohler curves.

3) SN based methods are not conservative. Wohler curve users end up assuming that a crack will show up perpendicular to a max principal stress or strain direction.  This assumption only works when you have the very simplest loading cases, no compression, and no strain crystallization.  Users of fracture mechanics + critical plane analysis don’t worry about whether they have simple loading, finite straining,  out-of-phase loading, compressive loading, changing principal directions, and/or strain crystallization.  Critical plane analysis checks every possible way a crack might develop and is therefore assured to always find the worst case regardless of detailed mechanisms.

4) Wohler curves are messy. They depend strongly on crack precursor size, which naturally varies specimen-to-specimen, batch-to-batch, and between lab mix and factory processes.  During SN curve testing, the size of the crack is neither measured nor controlled.  This accounts for the extra scatter that is typical in these tests.  In fracture mechanics testing, on the other hand, the crack is measured and controlled, leading to more repeatable and reliable results.  Noisy data means that the Wohler curve crowd has trouble differentiating between material or design options.  Users of fracture mechanics benefit from cleaner results that allow more accurate discrimination with less replication.

A Wohler curve does have one valuable use.  The Wohler curve can be used to calibrate the crack precursor size for a fracture mechanics analysis. It only takes a few data points – not the entire curve, since the crack precursor size does not depend on strain level, or other “special effects” variables.  Our recommended practice is to run a small number of nucleation style tests for this purpose only, then leverage fracture mechanics to characterize the special effects.

The bottom line is that, for purposes of general fatigue life prediction in rubber, the Wohler curve method looses technically and economically to the fracture mechanics + critical plane analysis based method that is used in modern fatigue solvers.


Fatigue Life Analysis of Free Surfaces

Free surfaces are critical in fatigue analysis because cracks in a physical part tend to form and grow fastest on such surfaces.  Extra care is required when analyzing free surfaces because typical 3D solid finite elements have their worst accuracy at the free surface (gauss points are not located on the free surface, and hydrostatic pressure profile does not conform adequately to element shape function).  Fortunately, the problem is not hard to resolve: free surfaces can easily be skinned with membrane elements.  Membrane elements are specially formulated to produce an exact state of plane stress.

Let’s look at fatigue life predictions that have been computed with a skin of membrane elements, and compare them with predictions computed from the underlying 3D solid elements.

To study the differences in fatigue life calculations, three simple loading cases were used: simple tension, planar tension, and bending. For each case the fatigue life is calculated for both the surface and solid elements.  The results are shown in the table below.

The fatigue life results show that the shortest life always occurred on the free surface. The life for the solid elements varied from 16% to 25% longer than the surface elements. In each case, the critical failure location was on the surface of the part and in the same location for both the solid and surface calculations. The colored contours of fatigue life are shown below for each of the cases.

Figure 1. Fatigue life on simple tension specimen. Isometric view.


Figure 2. Fatigue life on planar tension specimen. Cross-section view through the center of the specimen.


Figure 3. Fatigue life on bending specimen. Cross-section view through the center of the specimen.


Mesh refinement affects the fatigue life results. A mesh refinement study was performed on the bending case. The mesh refinement study consists of the standard mesh model shown above, a coarse mesh model and a fine mesh model. The number of elements in each model triples with each increase in mesh density. The results are shown below.

Figure 4. Mesh Density Analysis on bending specimen.

This mesh density analysis shows that as mesh density increases, the difference in the bulk and surface results decreases. The bulk and surface results converge to a single value. The amount that solid elements on the surface of the part extend into the interior of the part decreases as smaller elements are used. Since the smaller solid elements have a strain history closer to the surface they more closely match the surface element strains and the life results converge to a single value.

Bottom Line:  if you have free surfaces, skin your model with membrane elements for high accuracy results.  Refining your mesh at the surface may help somewhat, but skinning with membranes is far more reliable.




Durability Simulation and the Value of Competitive Advantage

Durability simulation is impacting product development business models in several big ways.  There are cost and risk avoidance impacts.  There is a time-to-market impact.  There is a quality/warranty impact.  And the biggest impact may be competitive advantage.  It’s certainly been in the news.

Rubber component suppliers must compete to win the business of Original Equipment Manufacturers (OEMs).  Having plant capacity is not enough.  The OEM also wants to know that the supplier can meet their durability spec.  The OEM wants to know that if there is a problem down the road, the supplier knows how to find it and fix it quickly.  It is a strong competitive advantage to be able to show the OEM a simulation of the component operating under their loads, along with fatigue calculations that support their warranty.

What is the value of this advantage?

Let’s assume that you are competing with 2 other suppliers for a contract worth $1M.  Since there are 3 competitors (including yourself), you can say that before award, the contract is, statistically speaking, only worth 1/3 of $1M to each competitor.  But at award, the winner takes all, and this means that 2/3 of the ultimate contract value depends completely on being the best option of the three.

This result can be generalized for any number n of competitors.  The fraction of the contract value that competitive advantage wins is (n-1)/n.  Using this rule, we see that for 2 competitors, 1/2 of the contract value comes from competitive advantage.  For 10 competitors, 9/10 comes from competitive advantage.  The more competitors you have, the more valuable it is to have an advantage.

  • How much of your new business win depends on being good with durability issues?
  • Are you the best at solving durability issues? (and do your clients know it!)
  • How much should you be investing in competitive advantage?

Strain-Induced Crystallization in High Cis Butadiene Rubber: Fact or Fiction?

There is an ongoing drive to create synthetic rubber that can give mechanical performance matching the properties of natural rubber (NR) – excellent strength, tear resistance, and fatigue crack growth resistance – which are attributed to the ability of NR to strain crystallize.  I have attended several technical conferences in the tire and rubber field where I have witnessed presentations about new grades of polybutadiene (butadiene rubber (BR)) with high cis-1,4 structure, wherein a claim is made about the improved ability of the BR to undergo strain-induced crystallization (SIC) for better mechanical properties in tires and other rubber applications.  Similar statements can be found in technical marketing materials and in patents.1,2  To what extent is this true?  This post takes a closer look at this topic.

It is first useful to show why strain-induced crystallization is key to the unique mechanical properties of natural rubber.  One of the most conclusive studies on the function of strain-induced crystals to slow the growth of cracks in cyclic fatigue is the work by Brüning et al.3  The movie below from this research shows an edge crack region in NR reinforced with 40 phr of N234 carbon black that is experiencing cyclic deformation from 0% to 70% planar tension at a frequency of 1 Hz.  Each pixel at each time in the video represents a wide-angle X-ray diffraction pattern collected in real time while stretching in a synchrotron X-ray beam, with red color used to indicate crystallinity and black for purely amorphous regions.  You can see the crystals form during stretching which self-reinforces the rubber at the crack tip region to resist further growth of the crack.  These experiments are very revealing, but they are extremely difficult and expensive to perform, as they require scattering analysis expertise, specially designed stretching devices with precise spatial control, and access to one of the few national laboratory sites where synchrotron X-ray studies can be performed.

Stereoregular polymers can crystallize in a window bounded by the glass transition temperature (Tg) and the melting temperature (Tm). When stretched, a polymer can crystallize at temperatures above its normal melting temperature due to melting temperature elevation caused by the well-known chain orientation/entropy effect.4  Chain orientation results in an increase in melting temperature from Tm to Tm,SIC.  This is shown schematically in the figure below for NR and BR.  The Tg and Tm for BR are significantly lower than the values for NR.  Therefore, even after perfecting the structure of polybutadiene to achieve >98% cis-1,4 structure though catalyst and process innovations, BR still has an intrinsic disadvantage compared to NR when it comes to SIC in the common temperature range where durable rubber components are employed.


Gent and Zhang5 recognized the lower Tm,SIC for BR compared to NR for samples crystallized in uniaxially strained conditions.  Ultra high cis BR with 98% cis content did not show any evidence of strain-induced crystallization at 23 °C whereas NR clearly exhibited SIC in a study by Kang and coworkers.6  In an investigation by Toki et al.,7 it was necessary to reduce the temperature to 0°C before high cis BR showed a crystalline X-ray scattering pattern at a strain of 5 (500% elongation).

As an aside, I will caution the use of the entropy-driven melting temperature increase as the only explanation for strain-induced crystallization above the normal Tm.  Natural rubber is quite a slow crystallizing polymer in the unstretched state, even when annealed in the prime crystallization regime midway between Tg and Tm.  In stark contrast, strain-induced crystallization occurs very fast in NR which highlights the complexity of behavior beyond the over-simplification given in the figure above.

So, why is there not more focus on synthetic high cis-1,4-polyisoprene which has the same polymer microstructure and strain crystallization window as NR?  The short answer is that the butadiene monomer is much more commercially available than isoprene at many synthetic rubber plants around the world.  I remember the early years in my industrial R&D career when I was working with some very talented polymer chemists at Bridgestone / Firestone.   I was developing new molecular architectures for improved synthetic rubber to use in various tire compounds.  The chemists told me that they could make almost any polymer design I wanted, as long as it could be made from styrene and/or butadiene.  This restriction reflected the fact that these two monomers were the commonly available feedstocks at the commercial plants where the polymers would eventually be produced.

In closing, it is a fact that high cis polybutadiene can strain crystallize at sub-ambient temperatures, but it is fiction that it will strain crystallize in the same manner as natural rubber at room temperature and above.

Do you want to see if your rubber will really exhibit strain-induced crystallization in a practical way that is relevant to end-use applications?  Contact me (via email: cgrobertson@endurica.com) to learn more about the Non-Relaxing Module in Endurica’s portfolio of testing services that was specifically developed to efficiently highlight SIC effects.8-10


1 http://arlanxeo.de/uploads/tx_lxsmatrix/sustainable_mobility_01.pdf

2  S. Luo, K. McKauley, and J. T. Poulton, “Bulk polymerization process for producing polydienes”, U.S. Patent 8,188,201, granted May 29, 2012 to Bridgestone Corporation.

3 K. Brüning, K. Schneider, S. V. Roth, and G. Heinrich, “Strain-induced crystallization around a crack tip in natural rubber under dynamic load”, Polymer 54, 6200 (2013).; movie provided to Endurica LLC by the authors (movie to be used only with permission from Karsten Brüning).

4 P. J. Flory, “Thermodynamics of crystallization in high polymers. I. Crystallization induced by stretching”, Journal of Chemical Physics 15, 397 (1947).

5 A. N. Gent and L.-Q. Zhang, “Strain-induced crystallization and strength of elastomers. I. cis-1,4-polybutadiene”, Journal of Polymer Science Part B: Polymer Physics 39, 811 (2001)

6 M. K. Kang, H.-J. Jeon, H. H. Song, and G. Kwag, Strain-induced crystallization of blends of natural rubber and ultra high cis polybutadiene as studied by synchrotron X-ray diffraction”, Macromolecular Research 24, 31 (2016).

7 S. Toki, I. Sics, B. S. Hsiao, S. Murakami, M. Tosaka, S. Poompradub, S. Kohjiya, and Y. Ikeda, “Structural developments in synthetic rubbers during uniaxial deformation by in situ synchrotron X-ray diffraction”, Journal of Polymer Science Part B: Polymer Physics 42, 956 (2004).

8 K. Barbash and W. V. Mars, “Critical Plane Analysis of Rubber Bushing Durability under Road Loads”, SAE Technical Paper 2016-01-0393, 2016, https://doi.org/10.4271/2016-01-0393.

9 W. V. Mars and A. Fatemi. “A phenomenological model for the effect of R ratio on fatigue of strain crystallizing rubbers”, Rubber Chemistry and Technology 76, 1241 (2003).

10 http://endurica.com/specifying-strain-crystallization-effects-for-fatigue-analysis/




Specifying Strain Crystallization Effects for Fatigue Analysis

Endurica CL and fe-safe/Rubber provide several material models for defining cyclic crack growth under nonrelaxing conditions.  Nonrelaxing cycles occur when the ratio R is greater than zero.  R is defined as

where T is the energy release rate (note that T will always be greater than or equal to zero).

The crack growth rate under nonrelaxing conditions is, in general, a function of both Tmax and R. For purposes of calculation, it is convenient to define an “equivalent” energy release rate Teq that gives the same steady state rate of crack growth as the operating condition on the nonrelaxing crack growth curve, but which is instead on the fully relaxing crack growth curve.  In other words,

Using this scheme, you can set up models for both amorphous and strain-crystallizing rubbers, depending on your definition of Teq.  Amorphous rubbers follow the well-known Paris model, and strain-crystallizing rubbers follow the Mars-Fatemi model (or you can define a lookup table).

Paris Model (Amorphous):

The Paris model is the simplest to derive, as it does not involve any material parameters.  It defines the equivalent energy release rate as

This definition is only suitable for rubbers that do not strain-crystallize.

For strain-crystallizing rubbers, one of the other two models should be used.

Mars-Fatemi Model (Strain-crystallizing):

The Mars-Fatemi model accounts for strain crystallization by treating the power-law slope, F, of the Thomas fatigue crack growth rate law   as a function of R, where


The exponential version is more compact, but the polynomial version is more flexible.

By substituting F(R) into the fatigue crack growth rate equations for relaxing and nonrelaxing cases, and doing a bit of algebra, the following relationship is obtained


Lookup Table (Strain-crystallizing):

The most flexible and accurate way to define strain crystallization is via a lookup table.  The lookup table takes R as an input and returns x(R) as an output.  This function can be defined as the fraction x(R) by which the nonrelaxing crack growth curve is shifted between the fully relaxing crack growth curve (x=0), and the vertical asymptote at Tc (x=1), at a given R.

This can be rearranged into the desired Teq (Tmax,R) form, as follows


Visualizing the differences between the models helps gain a better understanding of how strain crystallization can affect fatigue performance.  Since all of these models can be represented in the same form of Teq(Tmax,R), we show 2-D contour plots of Teq with R on the x-axis and ∆T on the y-axis.  ∆T is used instead of Tmax to make it easier to compare back to the simple Paris model.

From the figures above, we see that for the Paris model, the equivalent energy release rate depends only on ∆T.  When using this model, changes in R will have no effect on fatigue performance (when ∆T is also held constant).

For strain-crystallizing rubbers, changes in R should influence fatigue performance.  This is seen in the figures for the Mars-Fatemi and lookup table models.

The Mars-Fatemi example uses the following parameters:

The lookup table example uses Tc=10.0 kJ/m2 and Lindley’s data for unfilled natural rubber (P. B. Lindley, Int. J. Fracture 9, 449 (1973)).

For these models, there is a significant decline in Teq as R increases.  This effect is most pronounced when Tmax is much smaller than the critical energy release rate Tc.  Also, there is a point where the effect is reversed (around R=0.8 in these examples) and the high R-ratio starts to have a negative effect on fatigue performance.


A material’s strain crystallization properties’ impact on fatigue performance under non-relaxing conditions should not be ignored.  Whether you are seeking to take advantage of strain-crystallization effects or simply comparing the results of different materials/geometries/loadings, strain-crystallization should be accurately represented in your simulations.

Follow these tips to take advantage of strain crystallization and help ensure your fatigue performance is the best it can be.

  • Take advantage of Endurica’s material characterization service (the FPM-NR Nonrelaxing Module generates the strain crystallization curve) or use your own in-house testing to create an accurate strain crystallization model of your material (the nonrelaxing procedure is available for the Coesfeld Tear and Fatigue Analyser).
  • Use output requests like DAMAGE_SPHERE, CEDMINMAX and CEDRAINFLOW to observe R-ratios for your duty cycles.



  1. B. Lindley, Int. J. Fracture 9, 449 (1973)

Mars, W. V. “Fatigue life prediction for elastomeric structures.” Rubber chemistry and technology 80, no. 3 (2007): 481-503.

Mars, W. V. “Computed dependence of rubber’s fatigue behavior on strain crystallization.” Rubber Chemistry and Technology 82, no. 1 (2009): 51-61.

Barbash, Kevin P., and William V. Mars. Critical Plane Analysis of Rubber Bushing Durability under Road Loads. No. 2016-01-0393. SAE Technical Paper, 2016.




Tire Society 2017 – Best Question

Every year, the top minds from academia, government and industry gather in Akron to share their work at the Tire Society annual meeting, and to enjoy a few moments of professional camaraderie.  Then we all return to fight for another year in the trenches of the technology wars of our employers.

This year, the meeting offered the latest on perennial themes: modal analysis, traction, materials science, noise, simulation, wear, experimental techniques for material characterization and for model validation.  Too much to summarize with any depth in a blog post.  If you are interested, you should definitely resolve to go next year.  Endurica presented two papers this year.

I presented a demonstration of how the Endurica CL fatigue solver can account for the effects of self-heating on durability in a rolling tire.  Endurica CL computes dissipation using a simple microsphere model that is compatible, in terms of discretization of the shared microsphere search/integration domain, with the critical plane search used for fatigue analysis.  In addition to defining dissipative properties of the rubber, the user defines the temperature sensitivity of the fatigue crack growth rate law when setting up the tire analysis.  In the case considered, a 57 degC temperature rise was estimated, which decreased the fatigue life of the belt edge by a factor of nearly two, relative to the life at 23 degC.  The failure mode was predicted at the belt edges.  For 100% rated load, straight ahead rolling, the tire was computed to have a life of 131000 km.

The best audience question was theoretical in nature: are the dissipation rates and fatigue lives computed by Endurica objective under a coordinate system change?  And how do we know?  The short answer is that the microsphere / critical plane algorithm, properly implemented, guarantees objectivity.  It is a simple matter to test: we can compute the dissipation and fatigue life for the same strain history reported in two different coordinate systems.  The dissipation rate and the fatigue life should not depend on which coordinate system is used to give the strain history.

For the record, I give here the full Endurica input (PCO.hfi) and output (PCO.hfo) files for our objectivity benchmark.  In this benchmark, histories 11 and 12 give the same simple tension loading history in two different coordinate systems.  Likewise, 21 and 22 give a planar tension history in two coordinate systems.  Finally, 31 and 32 give a biaxial tension history in two coordinate systems.  Note that all of the strain histories are defined in the **HISTORY section of the .hfi file.  In all cases, the strains are given as 6 components of the nominal strain tensor, in the order 11, 22, 33, 12, 23, 31.  The shear strains are given as engineering shear components, not tensor (2*tensor shear = engineering shear).

The objectivity test is successful in all cases because, as shown in the output file PCO.hfo, both the fatigue life, and the hysteresis, show the same values under a coordinate system change.  Quod Erat Demonstrondum.




Durability Analysis in CAE: panel discussion of metals vs. polymers at the SAE World Congress

The relationship between crack nucleation and fracture mechanics experiments for polymers was first documented in 1964 by Gent, Lindley and Thomas (Journal of Applied Polymer Science, 8, 455, 1964.) 

Some weeks ago, I attended the WCX 2017 SAE World Congress and Exhibition, where a Technical Expert Panel Discussion on the topic of Durability Analysis in CAE was held.  The panel was moderated by Yung-Li Lee (FCA US LLC), and included topic experts Abolhassan Khosrovaneh (General Motors LLC), Xuming Su (Ford Motor Co., Ltd.), and Efthimio Duni (FCA EMEA). The discussion was excellent and wide ranging, owing both to the panelists, and also to the audience, which (judging by the high engagement) was very well versed with the core of the topic, as well as its frontiers. I will not attempt to give a complete summary of the event, but I do want to highlight a memorable discussion thread, and to offer a few thoughts.

I do not know who raised the topic.  It could have been a doctoral student or young professional.  Clearly, it was a person wanting to align his own efforts well relative to larger industry trends.  He started out with the observation that the classical crack nucleation methods (in which fatigue behavior is defined by a stress-life or strain-life curve) are quite popular in the automotive sector for analyzing fatigue of metals.  He also observed that modern tools for rubber take a different approach based upon a fracture mechanics method (in which fatigue behavior is defined by a crack growth rate curve). He then asked (I’m paraphrasing from memory here):

  • Which method (nucleation vs. fracture mechanics) is preferred for analysis of polymers?
  • Should we try to unify all testing and analysis efforts for metals and polymers under the same method?

The panelists made several points in responding to this prompt. They started with the point that differences in methodology may be hard to avoid, if only because metals and polymers are so different in composition, molecular structure, and microstructure.  Of course, it is possible to use fracture mechanical methods with metals, although there are some limitations implied by the granular crystalline structure of metals when cracks are very small.  Likewise, it is also possible to use stress-life methods with polymers, although certain aspects of the material behavior may be incompatible with the usual procedures, leading to questionable results.  From a practical standpoint, it would be quite difficult to change the methods used by the industry for metal fatigue analysis – the methods are quite mature at this point, and they have been implemented and validated across so many codes and projects that it is hard to imagine what could be gained by making a change.  For polymers, CAE durability methods are newer, and we should use what works.

There is a final point that I believe will ultimately define how this all plays out.  It is that 1) fatigue analysis for polymers is usually driven by multiple “special effects”, and that 2) the economics of the testing required to characterize these effects scales very differently between the two approaches.

Let me illustrate with a typical example:  we have a Natural Rubber compound used in a high temperature application, for an extended time, under nonrelaxing loads.  Let’s compare our options:


Option 1

Stress-Life Method

Option 2

Fracture Mechanics + Critical Plane Method

To use the stress-life method, we will need to develop curves that give the effect of 4 parameters on the fatigue life: 1) strain amplitude, 2) mean strain, 3) temperature, and 4) ageing.  The experiment is a simple cycle-until-rupture procedure, with one test specimen consumed per operating condition tested.


Let’s assume that we measure each of the four parameters at only 3 levels, and that we will require 3 replicates of each experiment.  The total number of fatigue experiments we need is therefore:


N = 3 amplitudes x 3 means x 3 temperatures x 3 ageing conditions x 3 replicates = 35 = 243 fatigue to failure tests


With the fracture mechanics method, a single run of the experiment solicits the crack at many different operating conditions, enabling observation of the crack growth rate at each condition.  Using Endurica’s standard testing modules, the example testing program (including replication) would require the following procedures:


Core module: 9 experiments (amplitude effect)

Nonrelaxing module: 3 experiments (mean effect)

Thermal module: 12 experiments (temperature effect)

Ageing module: 30 experiments (ageing effect)


243 tests required 54 tests required


In this example, the fracture mechanics method is almost 243/54 = 4.5x more efficient than the stress-life method!  If you need more than 3 levels, or if you have more than 4 key operating parameters, the experimental cost for the stress-life method quickly becomes completely impractical, relative to the fracture mechanics method. Based on these scaling rules, and on the fact that polymers exhibit so many special effects, you can now appreciate why the fracture mechanics method must prevail for polymers.  For metals, the case is less compelling: there aren’t as so many special effects, and the industry testing norms are already well established.

Bottom line: for fatigue of polymers, the economics of testing for ‘special effects’ strongly favors a fracture mechanics approach.  This fact is certain to shape the future development of fatigue life prediction methods for polymers.


Welcome to the Endurica Blog

Welcome to the Endurica blog, written by founder William Mars, Ph.D. These earlier posts (AND MANY MORE) are available on Will’s LinkedIn page:


Tire Society 2016: Notes on Advances in Computing Durability –  September 22, 2016



Strain Crystallization and Durability of Elastomers – August 26, 2016


Maximum Principal Stress Damage – August 3, 2016



Microstructure in Elastomers: Flaw or Feature? – March 26, 2016

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