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.


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.


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?

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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