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Reliability Test Design: Linking Fatigue and Reliability
Kurt MunsonHBM Prenscia
2016 nCode User Group Meeting
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1. Creating durability test specs
2. Introducing reliability to the durability test
What is the Weibull distribution
Reliability and confidence levels
Number of samples to be tested
Duration of test
3. Demonstration of ReliaSoft Weibull++
Agenda
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• Are used to validate a part for product.• Need to be correlated to service loading.• Should prove product durability and reliability.
Lab Durability Tests
Damageservice Damagetest
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4Durability Test Specifications
Test Type Input Loading
Constant amplitude Cyclic, single size, usually uniaxial
Block cycle Cyclic, multiple sizes, usually uniaxial
Time domain Time series, often multiaxial
Frequency domain Random PSD or sine sweep, usually uniaxial
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5Equivalent Damage Test Specification
Measured load
Simplified test load
Damage equivalence:
50,000 cycles @ +/‐850 lb =
4,000 service hours
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Requirements document:‘The part must be free of visible cracks with a reliability greater than 90% with 90% confidence after being subjected to lab loading representative of 4,000 service hours.’
Example Durability Requirement
But what about this 90% reliability and 90% confidence?
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7Deterministic Fatigue
Material Geometry Loads
Fatigue Analysis
Time to Failure
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8Probabilistic Fatigue
Material Geometry Loads
Fatigue Analysis
Time to Failure
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• The time‐to‐failure (i.e., life) distribution, can be modelled by a function called PDF (probability density function).
• Typical models/functions used are:• Weibull
• Lognormal
• Normal/Gauss
• Exponential
Probabilistic Fatigue Results
Material Geometry Loads
Fatigue Analysis
Time to Failure
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• These models are typically plotted on “Probability Paper,” which linearizes the model and expresses the Probability of Failure (i.e., % Failures) as a function of Operation (time, cycles, etc.).
• The Weibull distribution is frequently utilized, as it’s a flexible model and can describe different failure behaviors (e.g., infant mortality, wear‐out, etc.).
• The “beta” coefficient of the Weibull distribution represents the slope of the probability of failure curve, and it’s also indicative of the failure behavior.
Probability Distributions
Beta= 1
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• These models are typically plotted on “Probability Paper,” which linearizes the model and expresses the Probability of Failure (i.e., % Failures) as a function of Operation (time, cycles, etc.).
• The Weibull distribution is frequently utilized, as it’s a flexible model and can describe different failure behaviors (e.g., infant mortality, wear‐out, etc.).
• The “beta” coefficient of the Weibull distribution represents the slope of the probability of failure curve, and it’s also indicative of the failure behavior.
Probability Distributions
Beta= 2
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• These models are typically plotted on “Probability Paper,” which linearizes the model and expresses the Probability of Failure (i.e., % Failures) as a function of Operation (time, cycles, etc.).
• The Weibull distribution is frequently utilized, as it’s a flexible model and can describe different failure behaviors (e.g., infant mortality, wear‐out, etc.).
• The “beta” coefficient of the Weibull distribution represents the slope of the probability of failure curve, and it’s also indicative of the failure behavior.
Probability Distributions
Beta= 2Beta= 1
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• To demonstrate a reliability target, enough samples need to be tested, and for a given duration.
• For example, if we would want to demonstrate that at least 90% of our population will survive 1 life (i.e., X cycles), then intuitively, we can test 100 samples for 1 life and determine whether at least 90 survive.
• Can we test less samples?
• How confident are we in the result? Obviously, the more samples we test, the more confident we are.
• Can we test less than 1 life?
• There are available statistical methods to answer these questions.• These methods are available in the “RDT” tool in Weibull++
Reliability Demonstration Testing (RDT)
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• Confidence, sample size and test time are proportionally related:• High confidence = larger sample size or longer test duration.
• We can reduce the number of test samples by testing longer. • Weibull slope beta is used if we want to solve for test duration given a fixed sample
size, or, if our available test duration is other than the target.
Reliability Demonstration Testing
Target
Needed if solving for time, or test duration is other than target life
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15Software Demonstration
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www.hbmprenscia.com
Kurt Munson
Principal engineering consultant
HBM Prenscia
Thank you!
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