Hello , it's Dianna Deeney . In these last few episodes of the Quality During Design podcast , we've been talking about reliability during design . We reviewed ways to manage risks during a design process and then how to work with reliability engineers during design , the value they can bring and the contributions that they make to the design process .
One of the many contributions reliability engineers can make is test plans and test for reliability . There are several ways to get reliability data to make design decisions , and one of those ways is reliability life testing of the product .
I previously did an episode on this kind of testing and it explores accelerated stress testing as one subset of other reliability life testing methods . We talk about when it's a good idea , how we can approach doing it and what we can do with the results .
So if accelerated life testing is new to you or you're not quite sure how it works and how it gets incorporated into the design process , this episode is going to clear things up . So let's get started . We want to ensure our designs perform reliably , as expected and intended .
With today's high reliability products and quick release to market , we probably don't have enough time to just test our parts at normal use rates . It would take too long because our products are so reliable or we'll miss our window of opportunity to get our product to market .
There are reliability prediction methods we could use , some of which are standards based and others use physics with failure methods . Outside of those , we also have a way to test our own products . Today , we talk about reliability life testing options , specifically accelerated stress testing .
I'll tell you how it all fits together and what types of things you can do with the results . Hello and welcome to Quality During Design , the place to use quality thinking to create products others love for less . My name is Dianna . I'm a senior level quality professional and engineer with over 20 years of experience in manufacturing and design .
Listen in and then join the conversation at QualityDuringDesign . com . Before we start accelerating our testing , let's slow down and get a broader perspective of reliability life testing . We want our product to be reliable .
Part of designing for reliability is understanding how our part is going to fail , what stresses are going to bring about that failure and what the normal operation of our product is going to be . We want to test our product to identify design weaknesses , including its limits .
We could perform qualitative life testing , which is the shake and bake or halt testing , something I covered in an earlier podcast episode . This type of testing we use to just get failures and then design them out , but with a design we're working on now we want to test it to failure and then analyze the results , get failure rate data .
We want to do quantitative reliability life testing . We want to be able to generate normal use failures while measuring times or cycles to failure under a stress load . We want to better understand the failure and then generate a model to predict how reliable our part will be in its use case . Why would we want to spend resources generating a model like this ?
To test our product is going to require parts , test time and personnel , so it's going to cost us to do this . With quantitative reliability life testing , we get reliability information about our product under normal use conditions or that we can extrapolate to normal use conditions .
We can use this data to calculate probabilities of failure , which could help us with risk assessments , estimate product returns or warranties or help us compare design choices . It gives us a better understanding of our product so we can make informed decisions based on data . Here is one scenario .
The reliability of our system needs to meet this reliability requirement 99% reliability in system startup to at least 300 revolutions per minute is required after 600 on-off cycles of operation with 95% confidence when operating in an environment with a temperature range of minus 15 degrees Celsius to 40 degrees Celsius .
How do we verify that We can use quantitative reliability life testing ? By the way , there's a previous quality during design episode that explains reliability requirements and how we came up with that example .
A different scenario is that our product is made up of several components , but there is one component in particular that if it fails , the whole system is irreparable or dangerous to operate Because of how it's used and the failures that we've seen in the past . We're concerned with the vibration it sees and use .
We chose our critical part as a reliable part independently , but how reliable is it going to be when it's within the system ? Do we need to place use limits on it ? We could perform quantitative reliability life testing to test our system , understand how vibration is going to affect the failure rate and calculate reliability at different vibration levels .
We can perform this reliability life testing under normal use conditions , but remember that part of this type of testing is producing failures . If our design and the components we chose are very reliable , then the time or number of cycles that we'll have to run our products well . It might be a really long time before we start seeing failures .
This is where accelerated life testing is a good option . Accelerated life testing is reliability life testing , except that we're accelerating the failures . The main purpose of accelerated life testing is to reduce the length or time that we're testing . The failure modes are going to be the same whether it is at normal stress levels or at higher stress levels .
This is an important detail . No new failure modes are introduced . We're only accelerating the test to get failures more quickly . The way we accelerate life testing is by increasing the rate that we're going to get failures . We can do this in three ways .
We can increase the number of products that we test , so we increase the number of failures within a given time . We can compress the time to test by speeding up the number of cycles to simulate longer use under normal conditions . An example of this is activating a switch multiple times per minute when it would really only see one or two per week .
Or we can increase the stresses that generate failures . This last way to increase stresses is called accelerated stress testing . Stresses that are commonly used in accelerated stress testing are temperature , humidity , vibration , voltage , current and radiation . Where do we even get started ? Following our vibration scenario , we've got our test objective .
We want to quantify our product's reliability . In other words , we want to be able to calculate probabilities of failures of our system . We know the type of test we want to perform is accelerated life test because it will take too long to generate failures under normal conditions .
We know our design is susceptible to vibration , so we'll decide to plan for some accelerated stress testing . We're going to expose our parts to vibration levels beyond what it will see in normal use conditions to accelerate the test and produce failures in a shorter time .
The hardest part about accelerated stress testing is understanding , choosing and calculating the right stress and stress levels to test . For complex designs we may need to focus on a dominant failure . To understand the stresses that cause that failure , we can look to experts , field data of similar products or use physics of failure .
We can also perform some preliminary testing to understand the stress factors that affect our product , including DOE Design of Experiments . As far as the level of stress , a rule of thumb is that the stress levels we pick are higher than the spec limits but lower than the destruct limits .
There is a risk in setting up the experiment incorrectly , but that's why you talk with your reliability engineering friends . Here's what's going to happen when we decide to move forward with an accelerated stress test .
First , we're going to design a test that applies stresses at levels that exceed the normal stresses that our product would see , with the goal to accelerate a certain failure mechanism . Next , we'll estimate a way to use the accelerated results to predict normal use results .
We do this by picking a model to be able to extrapolate from one stress level to a different stress level . This model is going to be a measure of stress against a measure of life , like time or cycles . When liability engineers may mention terms like acceleration model , life stress relationship or life characteristic , they're referring to this model .
Common examples of models are the Arrhenius model , the Erring model and the Power Law model and more , including ways to combine stress models for multiple stresses . If our estimated acceleration factor is on the order of 100 times the normal use , then practitioners warn that we'll likely not get useful results from our accelerated life test .
Then we're going to perform a test and collect the data . We'll end up with failures or suspensions , which is when the parts survive through all the testing and don't fail .
We'll have failure modes , the corresponding stress at the time of failure and the time or cycle when that failure occurred , and we'll have a record of whatever other cumulative stress schedules our parts survive through the test . Finally , we'll analyze our data . We'll choose a probability distribution to fit our accelerated stress data .
This distribution is likely going to be the Weibull exponential or log-normal distribution And we'll use the acceleration model to translate the high stress test results to normal use stress levels . We'll end up with a model of the reliability life of our product under normal use conditions for that failure mode .
Having these results allows us to use statistics and reliability analyses to calculate reliability measures like failure rate and probabilities of failure at certain stresses . We can use those measures for design choices , warranty decisions , risk management and other design decisions like whether to perform preventive maintenance or to do screening testing at manufacturing .
Is this type of test an investment ? Yes , it becomes easier to justify if we have a portfolio of similar products where we can reuse test methods in fixed stream or even be able to reuse the results . It's also easier to justify if the stakes are high with product failure .
There are a lot of independent test houses that have the equipment , fixed stream and know-how to be able to help design and perform accelerated stress testing . Having quantitative reliability life data takes all the guesswork out of a lot of the design decisions , and this is a case where upfront work is an investment for huge benefits later .
To recap reliability life testing is testing our products to failure in order to improve its reliability , either from identifying and eliminating failures or being able to model the system , to calculate probabilities , to make decisions . Accelerated life testing is a subset where we are forcing failures to occur more quickly .
Accelerated stress testing is then a subset of that where we're increasing the stresses seen to produce the same failure modes more quickly . We reviewed three levels of topics today to talk about accelerated stress testing , which means there are many other options for reliability life testing . What is today's insight to action ?
If we've designed a product to be highly reliable and we need to verify the reliability requirements or want to develop a stress life model for a failure mode , accelerated stress testing is an option . The benefit is reduced test time and more information for us to be able to make decisions about our product .
There is a lot of planning and investment involved , but it can be done with long-term benefits in mind . I'm adding a reference to RelyaSoft's Accelerated Life Testing reference to this podcast blog . It's an e-textbook but there is also a downloadable PDF . If Accelerated Life Testing is a topic you're interested in , then I recommend that you download this reference .
There are earlier episodes of Quality During Design that expand upon some of the ideas we talked about today . Episode 6 , halt watch out for that weakest link . explores the purposes of HALT Highly Accelerated Life Testing as a qualitative accelerated life test where we try to design out the weakest component .
This episode also links to an independent test house with videos and pictures of the equipment and fixed train that can be used in accelerated life tests . Episode 31 , five Aspects of Good Reliability Goals and Requirements . Explores how we can set reliability requirements that we can verify through reliability life testing methods .
Episode 36 , when to Use DOE Design of Experiments . Explains how it can be used to explore the factors that affect our design , which is a method that can be used to help identify the stresses we want to test for accelerated stress testing . Episode 30 , using Failure Rate Functions to Drive Early Design Decisions .
Reviews how we can use one of the outputs of a quantitative accelerated life test the Failure Rate Function . And finally , episode 10 , how to Handle Competing Failure Modes talks about how to analyze reliability life data with different failure modes .
Although our accelerated stress test should focus on one failure mode , treating the parts that don't end up failing as suspensions is the carryover idea to this episode . Please visit this podcast blog and others at QualityDuringDesigncom . Subscribe to the weekly newsletter to keep in touch .
If you like this podcast or have a suggestion for an upcoming episode , let me know . You can find me at QualityDuringDesigncom On LinkedIn or you could leave me a voicemail at 484-341-0238 . This has been a production of Deeney Enterprises . Thanks for listening .