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% = $1M 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.

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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.

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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.

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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?
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