www.hbkworld.com Open in urlscan Pro
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1 forms found in the DOM

GET https://www.hbkworld.com/en/search.html

<form class="search-form search-field" role="search" action="https://www.hbkworld.com/en/search.html" method="get">
  <input type="hidden" name="_charset_" value="utf-8">
  <div class="search-field__wrapper" data-nc="SearchField" data-quick-suggestions-enabled="true"
    data-quick-suggestions="/content/experience-fragments/hbkworld/global/en/header/header-v2---megamenu/jcr:content/body/col_1/header/search/quick-suggestions.suggestions.json" data-quick-search-results-enabled="true"
    data-quick-search-results="/content/experience-fragments/hbkworld/global/en/header/header-v2---megamenu/jcr:content/body/col_1/header/search/quick-results.results.json">
    <div class="search-field__input-wrapper">
      <input class="search-field__input" type="text" name="q" placeholder="Search Here" autocomplete="off">
      <span class="search-field__input-icon"></span>
    </div>
    <div class="cmp-list__button hbk-button hbk-button--secondary">
      <button class="cmp-button" type="submit">
        <span class="cmp-button__text">Go!</span>
      </button>
    </div>
    <div class="quick-lists" data-quick-lists="">
      <dl class="quick-suggestions" data-quick-suggestions="">
        <dt>Suggested search terms...</dt>
      </dl>
      <dl class="quick-results" data-quick-results="">
        <dt>Quick results</dt>
      </dl>
    </div>
  </div>
</form>

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The cookie policy was last updated on 19.06.2023
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WHAT IS A COOKIE?

A cookie is a small data file stored in your computer, tablet or smartphone. A
cookie is not a program that can contain harmful malware or virus.


HOW OUR WEBSITE USES COOKIES

Some cookies perform essential functions for our website. Cookies also help us
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You can see the specific services that store cookies and why they do it, under
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SOFTWARE AND SOLUTIONS FOR DURABILITY, FATIGUE, AND RELIABILITY ENGINEERING
PROFESSIONALS


UNIQUE AND INTEGRATED APPROACH FOR PRODUCT TEST, DESIGN AND MAINTENANCE
OPTIMIZATION

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DURABILITY TESTING, RELIABILITY, AND FAILURE MODE COMPARISON

Let’s assume you are an engineer tasked with validating the durability of a
structural part that is susceptible to fatigue cracking. Proving ground tests
are often used to assess component durability, but unfortunately they can be
costly and time consuming. A laboratory durability test is desirable due to
reduced variability, cost, and time but you then face the challenge of moving
the durability test into the lab while replicating the same failure behavior.  

If fatigue is the failure mode, fatigue methodologies like Stress-Life (SN) can
be used to create an equivalent fatigue damage test specification. This way, the
complicated loading seen on the proving ground can be converted into simplified
test lab loading that produces the same amount of fatigue damage. Once a
relative damage relationship has been determined, the lab test can be run and
results from the proving ground and lab can be compared statistically
with reliability methods to assess the failure behavior and answer these
critical questions:  


 * Are these failure modes the same? 
 * Is the lab test representative of real product usage? 


THIS PROCESS CAN BE PERFORMED IN THREE GENERAL STEPS:

Step 1: Assessing fatigue cyclic content

The structural loading this part experiences in the proving ground has been
measured and is shown above. This measured data contains a broad spectrum of
cycles – from small to large – all of which contribute to the overall fatigue
damage.   

The most common method of quantifying cyclic content for fatigue calculations
is rainflow cycle counting.  Results are shown below. 



Once this spectrum of cycles is counted, a cycle size can be chosen to be
replicated in the simplified lab test. The cycle size should not be larger than
the largest cycle observed in the measured loading or else the failure mode may
be changed. Additionally, a very small cycle size might require a very lengthy
test in order to replicate equivalent fatigue damage. Therefore, a balance must
be made to select a cycle size that is large enough for the lab test to complete
within a target duration, yet not too large. 

Step 2: Calculating equivalent damage loading

The final step is to compare the failure behavior from the track test and lab
test(s). It is possible to perform forensics on the broken faces of a failed
part to assess the failure mode. Alternatively, the life data results from each
population of failed parts can be model statistically and compared. While
several statistical models could be used, Weibull is often selected due to its
flexibility. The parameters  and  can describe the failure behavior and time to
failure respectively. When using a level of confidence, these parameters can
then be described as ranges.



Step 3: Failure mode comparison

Suppose you followed the steps above and create two possible test loading
specs: 

Lab test A: A large cycle repeated 50,000 times 

Lab test B: A small cycle repeated 200,000 repeats 

Test A has clear benefits in that it will take less time to get results! But
maybe you want to know which option is a better durability test.  

You run the product validation test using both newly-created equivalent damage
lab loadings, failing 6 samples in each test. Fatigue damage calculations allow
the lab failure times to be correlated back to equivalent proving ground
laps. Previous testing on the proving ground also returned 6 failures.



The final step is to compare the failure behavior from the proving ground and
lab test(s). It is common to perform forensics on the broken faces of broken
parts to assess the failure mode – with the assumption that similar failure
surfaces implies similar failure modes.

Statistics can also aid this comparison. The life data results from each
population of failed parts can be statistically modelled and compared. While
several statistical models could be used, the Weibull distribution is often
selected due to its flexibility. Weibull parameters β and η can describe the
failure behavior and time to failure, respectively. These values can be
calculated for each set of failure data and then compared to assess similarities
or differences. Further, confidence bounds can be introduced and these
parameters can then be described as ranges.

Once each population is modeled, β and η at a specified level of confidence can
be compared. Visually, the easiest comparison method is on a contour plot.
Separation between contours signifies populations are statistically different at
the specified level of confidence.



This plot shows several contours of Weibull parameters: black is lab test A, red
is lab test B, and orange is the proving ground. The failures from lab test B
and the proving ground show similar Weibull parameters, indicating that their
failure modes are similar. Lab test A shows drastically different Weibull
characteristics, indicative of a different failure mode entirely. This provides
us with statistical evidence that while lab test A has the benefit of a shorter
test duration, it’s actually resulted in a different failure mode! 

 

This example was used during a workshop at the 2019 Prenscia User Group
Meeting to explore the approach, share best practices, and discover ways to
apply this method in daily practice. We have found that this approach works well
for creating lab test from proving grounds with equivalent damage. We are
confident that if you need to create a lab test specification, this method will
allow you to reduce test time and cost while assessing failure mode behavior. 

See all upcoming opportunities to get engaged with HBK tools at an event
or training course near you.

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