It sounds like magic but it’s really advanced science and technology

When people ask me what Endurica does I tell them: You give us a computer file of one full use cycle of your design – be it a tire design or one rotation of a pump that you’re building a seal for – along with a sample of the rubber you’re making the product of and Endurica will tell you when it will break and where. There are many companies who can do that for metals but we’re the only ones who have figured it out for rubber. It all started with our founder’s Ph.D. work in mechanical engineering and his years in tire design. We actually have more clients outside of the U.S. than in, and our non-disclosure agreements don’t allow us to share names but some of the clients who have published technical papers using our software include General Motors, Caterpillar and Tenneco.

I’ve learned that over-engineering seems to be the status quo in the rubber industry. Because Endurica’s methods aren’t as well-known as we would like, many companies do things the way they always have: test the rubber part for a lifetime of use at the most intense conditions to ensure it fails LONG past the time it could ever be used. That build-and-break routine is so embedded in the industry it led to an interesting insight from an engineer who stopped by our booth at a recent conference.

 We don’t have time to do it right, but we do have time to do it over.
     – 2019 SAE World Congress Event Attendee

It seems the company they worked for budgets for five to seven full development cycles (design, build, test to breaking point. Re-design; build…..) I’m told that in the tire industry each round of this process  easily tops $50,000 when you factor in the engineering time, breaks in actual production schedules for samples to be made, plus months in physical testing. It seems that because many do not understand Endurica’s processes and the foundational science/engineering/technology behind it they continue with the accepted norm of “make and break” even though it costs them hundreds of thousands of dollars annually.

To prevent failure how much do YOU plan to fail?

If that is too strong of a question let me ask it this way: How many design cycles do you have in the budget this year? Simulation is a powerful tool in design and if you are designing on computer already, adding Endurica’s methods to your simulations is the next logical step to, as we say, Get Durability Right.

Consider using the same design budget you already have but replace just one “round” of traditional design with the purchase of Endurica’s training and a software license. By adding our software to your simulation design system (Abaqus, ANSYS or MSC/Marc)  you can have results within HOURS (not the months of traditional testing) for the durability of each version of your product design. Envision the impact this technology could have on your firm: reduced time to market; greater design flexibility, increased profitability; reduced costs in both engineering and production…

If there was a better way, would you take it?

Endurica does not advocate that you go directly from simulation to production. We simply make it easier for you to do MANY design cycles to get the best design possible before you do actual FEA testing on the best possible option. Maybe it’s time to reconsider your budget for design cycles, and factor in budget money for both the training to thoroughly understand the science behind Endurica’s methods as well as the software which will enable you to have INFINITELY MORE design iterations for the same overall budget. It isn’t magic but it is pretty advanced science and technology. Let’s talk.

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Endurica 2019 Updates Released

Endurica CL

Endurica CL received many improvements over the past year.  These improvements cover a wide variety of different aspects of the software:

Reducing Run-time

Our investments in code benchmarking and performance are paying off! We’ve been able to make internal optimizations to the code that reduce analysis run-times by approximately 30%. 

HFM and HFO Formatting

To make our output cleaner and more meaningful, small changes have been made to the number formatting in the HFM and HFO files.

All results reported in scientific notation are now formatted in standard form where the leading digit before the decimal point is non-zero (previously the leading digit was always zero).  This gives one more significant figure to all the results without increasing the output file size.

Signal compression

The shortest fatigue life for the analysis is now printed to the console and HFM file with six significant figures.  Previously, the life was reported with only two significant figures.  This change makes it easier to quickly compare two different analyses, especially when the analyses have similar fatigue lives.

New features have been added to Endurica CL to make it easier to process and analyze histories.  Using the new COMPRESS_HISTORY output request, you can generate new HFI files containing compressed versions of your original history.  The generated history is composed of the rainflow counted cycles from your original history.  An optional output parameter allows you to further compress the signal by specifying the minimum percentage of the original damage that should be retained in the new history.  When keeping a percentage of the damage, the cycles are sorted from most to least damaging so that the generated history always contains the most damaging cycles and discards the least damaging cycles.

This output request is useful when you want to reduce a long complex history while keeping the important damaging cycles.  This can reduce file sizes and simplify experimental testing setups as well as give you a deeper insight into your duty cycle. 

Endurica DT

Endurica DT is our incremental fatigue solver.  With Endurica CL, your analysis starts at time zero and integrates the given strain history until end-of-life.  With Endurica DT, you can start and end at a series of times that you specify.  This lets you accumulate many different histories and loading conditions repeatedly until end-of-life.

Endurica DT gives you new ways to control your analyses, and we have been using it over the past year in many applications.  For example, fatigue results for laboratory test procedures that involve multiple loading stages (such as FMVSS No. 139 for light vehicle tires, or block cycle schedules for automotive component applications) can be fully simulated using Endurica DT. You can also compute residual life following some scheduled set of load cases. 

Endurica DT can also be used to accumulate the actual loads measured on a part in situ.  This allows you to create a digital twin that keeps a near real-time record of the part’s current simulated damage state and the part’s remaining fatigue life. 

Stiffness Loss Co-Simulation

Endurica DT now includes a stiffness loss co-simulation workflow that allows you to iteratively update the stiffness of your part over a series of time steps, based on the amount of damage occurring in the part.  The stiffness loss is computed per element so you will have a gradient where the more damaged regions become softer.  Endurica DT computes the current fraction h of stiffness loss based on the stress and strain, and the finite element solver computes the stress and strain based on the current fractions of stiffness loss. The capability accurately predicts the effects of changing mode of control during a fatigue test.  For example, stress controlled fatigue tests show shorter life than strain controlled fatigue tests. 

Endurica EIE

Endurica EIE, our efficient interpolation engine, quickly generates long, complex histories using a set of precomputed finite element results (i.e. the ‘nonlinear map’).  We first launched EIE last year with the ability to interpolate 1-channel and 2-channel problems.  We have recently added the ability to interpolate 3-channel problems. 

In the example below, EIE was benchmarked with three-channels.  Three separate road load signals were computed from a single nonlinear map.  With EIE, you don’t need to rerun the finite element model for each history.  Instead, EIE interpolates from the nonlinear map, providing the equivalent results with a 60x speed-up in compute time. 

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Behind the Scenes Tour of Endurica Software Development and QA Practices

Ever wonder what it takes to consistently deliver quality and reliability in our software releases?  Here’s a brief overview of the systems and disciplines we use to ensure that our users receive timely, trouble-free updates of Endurica software.

Automation:

Throughout the life of our software, changes are made to our source code for a variety of reasons.  Most commonly, we are adding new features and capabilities to our software.  We also make updates to the code to improve performance and to squash the inevitable bugs that occasionally occur.

With each change committed to the code repository, the software needs to be built, tested, and released.  Endurica’s workflow automates these steps so that any change to the source repository triggers a clean build of the software.  A successful build is automatically followed by a testing phase where our suite of benchmarks is executed and compared to known results.  Finally, the build is automatically packaged and stored so that it is ready to be delivered.  At each step along the way, a build error or failed test will cancel the workflow and send an alert warning that the release has been rejected, so that the issue can be addressed, and the workflow restarted.

Figure 1: Endurica’s build and testing process ensures that high quality standards are met for every new release. Black arrow: normal flow, Red arrow: on error or failed test.

Reliability:

The automated testing phase that every release goes through helps ensure the reliability of our software.  For example, every Endurica CL release must pass all 70 benchmarks.  Each benchmark is a separate Endurica CL analysis made up of different materials, histories, and output requests.  Results from a new build are compared to known results from the previous successful build.  If results do not agree, or if there are any errors, the benchmark does not pass and the build is rejected.

The testing phase prevents “would-be” bugs from making it into a release and makes sure that any issues get resolved.

Repeatability:

The automated nature of our development workflow naturally helps with repeatability in our releases.  Each build flows through the same pipeline, creating consistent releases every time.  There is less worry, for example, that a component will be forgotten to be included.  It also allows us to recreate previous versions if comparisons need to be made.

Traceability:

Our version control system enables us to easily pinpoint where and when prior changes were introduced into the software.  Each release is tied to a commit in the repository. This allows any future issues to be easily traced back and isolated to a small set of changes in the source for quick resolution.

Responsiveness:

Automating the build and release pipelines greatly increases our responsiveness.  If an issue is discovered in a release, the problem can be resolved, and a fully corrected and tested release can be made available the same day.  We can also quickly respond to user feedback and suggestions by making small and frequent updates.

The systems and disciplines we use in our development process make us very efficient, and they protect against many errors. This means we can spend more of our time on what matters: delivering and improving software that meets high standards and helps you to get durability right.twittergoogle_pluslinkedinmail

Will Mars on the Rubber Industry: A Look Back 10 Years, Where We Are Now, A Look Ahead 10 Years

Q: With regards to fatigue life prediction methods, where was the rubber industry 10 years ago?

Will There was plenty of great academic work and good understanding of fundamentals, but the methods were only deployed – if at all – via “homebuilt” solutions that could never support a broad enough audience to really impact daily product design decisions.  Simulation methods and experimental methods shared theoretical foundations but they were poorly integrated.  They suffered from operational problems, noisy data and open-ended test duration.  It was possible to analyze a crack if you could mesh it, but the added bookkeeping and convergence burdens were usually not sustainable in a production engineering context.  Mostly, analysts relied on tradition-based crack nucleation approaches that would look at quantities like strain or stress or strain energy density.  These were not very accurate and they were limiting in many ways, even though they were widely used.  They left companies very dependent on build and break iterations.

Q: Where is the industry today?

Will: The early adopters of our solutions have been off and running now for a number of years.  Our critical plane method has gained recognition for its high accuracy when dealing with multiaxial cases, cases involving crack closure, cases involving strain crystallization.  Our testing methods have gained recognition for high reliability and throughput.  Our users are doing production engineering with our tools.  They are consistently winning on durability issues.  They are handling durability issues right up front when they bid for new business.  They are expanding their in-house labs to increase testing capacity and they are winning innovation awards from OEMs.  They are using actual road-load cases from their customers to design light-weight, just-right parts that meet durability requirements.  The automotive industry has lead adoption but aerospace, tires, energy, and consumer products are also coming up.  We have users across the entire supply chain: raw material suppliers, component producers and OEMs.  The huge value that was locked up because durability was previously so difficult to manage is now unlocked in new ways for the first time.  This has been the wind in Endurica’s sails for the last 10 years.

Q: Where do you see the industry in 10 years?

Will: In 10 years, OEMs will expect durability from all component producers on day 1, even for radical projects.  They will expect designs already optimized for cost and weight.  They will push more warrantee responsibility to the supplier.  They will monitor durability requirements via shared testing and simulation workflows.  Suppliers will pitch solutions using characterization and simulation to show their product working well in your product.  The design and selection of rubber compounds to match applications will enter a golden age as real-world customer usage conditions will finally be taken fully into account.  Where design and selection was previously limited by the budget for a few build and break iterations, and low visibility of design options, they will soon be informed by an almost unlimited evaluation of all possibilities.  Where simulation methods have traditionally had greatest impact on product design functions, we will also start to see rubber part Digital Twins that track damage accumulation and create value in the operational functions of a business.  Durability is definitely set to become a strong arena for competition in the next 10 years.

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EIE – Effect of Map Discretization on Interpolation Accuracy

Overview

The accuracy of the interpolated results performed by EIE is dependent on the discretization of the map. Specifically, the results will become more accurate as the map’s point density increases. This study uses a simple 2D model to quantify the accuracy of results interpolated from maps with different densities.

Model

A 1 mm x 1 mm rubber 2D plane strain model with two channels is used. The square’s bottom edge is fixed and the top edge is displaced in the x and y directions as shown below. The x displacement corresponds to channel 1 and the y displacement corresponds to channel 2. The working space of the model is defined by the x displacement ranging from 0 mm to 0.8 mm and the y displacement ranging from -0.08 mm to 0.8 mm.

Plane strain model with two channels

The model is meshed with 100 8-node, quadrilateral, plane strain, hybrid, reduced integration elements (shown below).

100 element mesh

History

We define as the benchmark reference solution a history that covers the model’s entire working space with a high density of points. An evenly spaced grid of 128×128 points for a total of 16384 points is used as the history (shown below). It is important that this history is more refined than the maps that we will create to ensure that we are testing all regions of our maps.

128×128 history points

These points are used to drive the finite element model and the results are recorded. For this study, we record the three non-zero strain components and the hydrostatic pressure (NE11, NE22, NE12, and HP) for each element at each time point. In summary, there are 4 result components, 100 elements, and 16384 time increments. This set of results is the reference solution since it is solved directly by the finite element model. We will compare this solution to our interpolated results to measure our interpolation accuracy.

Maps

Six maps with different levels of refinement are used to compute interpolated results for our history points. All of the maps structure their points as an evenly spaced grid. The first map starts with two points along each edge. With each additional map, the number of points along each edge is doubled so that the sixth and final map has 64 edge points. The map points for the six maps are shown below.

Six maps with increasing levels of refinement

The map points for these six maps are used to drive the finite element model’s two channels. The strain and hydrostatic pressure results from the FEA solutions are recorded at each map point in a similar way to how the results were recorded for the FEA solution that was driven by the history points. Next, EIE is used six times to interpolate the map point results at each resolution onto the high resolution reference history points.

We now have seven sets of history results: the true set of results and six interpolated sets of results.

Results

To compare our results, we look at the absolute difference between the sets of results. The absolute error is used, opposed to a relative error, since some regions of the model’s working space will give near zero strain and hydrostatic pressure. Division by these near zero values would cause the relative error to spike in those regions.

Since we have 100 elements and 4 components per element, there are a lot of results that could be compared. To focus our investigation, we look at the element and component that gave the maximum error. The figure below shows contour plots for each of the six maps for this worst-case element and component. The component that gave the maximum error was NE12. The title of each of the contour plot also shows the maximum error found for each of the plots.

Error contours for the worst-case element and component. Titles report the maximum log10 error.

You can see that the error decreases as the map density increases. Also, you can identify the grid pattern in the contour plots since the error gets smaller near the map points.

Plotting the maximum error for each of the maps against the number of map points on a log scale is shown below. The slope of this line is approximately equal to 1 which is expected since a linear local interpolation was used to compute the results.

Maximum error vs the number of points for each of the six maps
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Top 10 Reasons to Celebrate Endurica’s 10-Year Anniversary

In considering ways to capture the contributions and essence of Endurica LLC to celebrate its tenth year of existence – and educating myself some more about the company I joined a little more than a year ago – I decided to put together the following top 10 list.  Enjoy this informative snapshot of Endurica.

10 years of providing software and testing solutions for elastomer applications to #GetDurabilityRight in automotive, tire, aerospace, sealing, defense, consumer products, energy, and medical industries.

9 countries are using Endurica’s elastomer fatigue analysis software products (Endurica CL™, fe-safe/Rubber™, Endurica DT™, and Endurica EIE™) for finite element analysis (FEA).

8 specialized elastomer characterization modules are available in our Fatigue Property Mapping testing services.

7 years ago, the first training course was offered by Endurica. Today there are three courses that are each taught multiple times around the world every year.

6 is the number of full-time teammates working at Endurica LLC.

5 types of integrated durability solutions are offered by Endurica: FEA software, material characterization services, testing instruments, training, and consulting.

4 patents for Endurica’s innovative technology (3 granted plus 1 pending application). 

3 testing instruments are available in the Americas region through our partnership with Coesfeld GmbH & Co. KG (Germany).

2 members of the Endurica team received the Sparks-Thomas Award from the Rubber Division of the American Chemical Society for outstanding contributions and innovations in the field of elastomers.

1st (and only) commercial FEA software to predict when and where cracks will show up in an elastomer product with complex loading and geometry for users of Abaqus™, ANSYS™, and MSC Marc™.twittergoogle_pluslinkedinmail

Just Because You Can Doesn’t Mean You Should

When you have an unmet simulation or testing need, should you build or buy the capability?

There are testing instruments and software packages available in the market – which have been improved through years of R&D and quality management – that can meet the needs of a technical team in their product development efforts. Despite these turn-key resources, we sometimes see a company tasking some of its engineers to build their own.

Why does this happen?

Companies hire smart and creative engineers and scientists with advanced degrees to populate their R&D centers. It is common, and even expected in many situations, for a graduate student to create customized equipment or software as a part of a Ph.D. or M.S. research project. Pushing the boundaries of science and technology often requires such development of devices or code. Also, limited research funding in academia can force students to build their own equipment. When young engineers start their industrial careers after graduate school, they carry with them the mindset of building and programming things themselves. These individuals excitedly offer to create when a new analysis or measurement need arises within a company, and managers like to encourage the enthusiasm of their technical staff.

But, even if your sharp engineer can build a DIY testing device or computer program that recreates the state-of-the-art commercial products created by teams of engineers across many years, is this an efficient and strategic use of the engineer’s abilities? If your company makes tires, for example, then shouldn’t you have your smart people focused on making better tires rather than making testing instruments or software?  What are the labor costs, and the opportunity costs, of your highly-skilled engineer building a piece of testing equipment compared to the price of the commercial instrument or relative to the return you could make on an actual improvement to your product? Unless you are in a position to surpass the commercial solution, there is no competitive advantage in the DIY solution. Once you have created your own solution, who will maintain and support it? Will you be able to keep it up to date with advances in technology? Do you have the capabilities and resources to validate your solution more strongly than the market has already validated the commercial solution?

Through my 15 years of experience in materials research and development in the tire and rubber industry, I have seen several pieces of home-built testing equipment collecting dust within companies. Either they were half finished and abandoned or could only be reliably operated by the creator who moved to another department or company.

There can be circumstances where the needed instrument or simulation product is not commercially available. Sometimes the capability exists in the marketplace, but it is not discovered because the maker mindset leads to a halfhearted search. For customized solutions, you may consider working with a vendor to leverage their expertise in creating the required device or program.

If your analysis and testing needs are in the rubber fatigue and lifetime area, please talk to us before you decide to invest in creating your own solutions. Our solutions embody decades of experience. They are the most competitive and strongly validated solutions you can buy. Endurica has specialized finite element analysis software that predicts elastomer durability for complex geometries and loads, and we offer testing instruments for accurately characterizing the fracture mechanics of elastomers through our partnership with Coesfeld GmbH & Co. KG. We can take you quickly to the forefront of fatigue management capabilities.

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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.twittergoogle_pluslinkedinmail

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.twittergoogle_pluslinkedinmail

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.twittergoogle_pluslinkedinmail

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