Your Reliability Engineering Professional Development Site
We’re given information about an opportunity for a new product.
We talk about what can happen when we start solution-building just with what we’re given. And we talk about an alternative start to a new engineering project.
Greg and Fred discussing bringing suppliers back to the location of OEM headquarters and factories. A common risk is finding the talent to design and build these factories.
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Chris and Fred discuss the many and varied different software package that can help you do ‘reliability stuff’ … and how we usually assume everything they do is ‘OK.’ But how do we know the software is giving us the numbers we need?
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In this episode I discuss the state of steel metallurgy knowledge and the things this knowledge have enabled including ultra-high volume steel production and the ability to engineer components with a wide range of properties for a wide range of applications.
To learn more about this subject check out https://www.imetllc.com/a-tribute-to-steel-metallurgy-knowledge/ Also, on May 17, 2023 there’s a live online course https://www.imetllc.com/product/steel-metallurgy-webinar/
In this special episode of Quality during Design Redux, we’re pulling episodes from our archive about test results analysis.
In our Season 1 – Episode 93 titled “The Fundamental Thing to Know from Statistics for Design Engineering”, we talked about hypothesis testing: how it is used for lots of data analysis techniques.
When we’re looking at results (like measures of a characteristic), we need to take care not to get too hung-up on what the statistics is trying to tell us. Yes, statistical tools are a good way for us to make decisions and the results can act as proof for us. But, there’s a practical, engineering side to results, too. We need to evaluate the statistical significance along with the practical significance.
We review an example and how to document it.
Creating a plan and generating information is part of reliability engineering, yet it’s not enough. To be a successful engineer, one must communicate well. This means we need to write, discuss, and present well. We are often called upon to examine failures and recommend solutions, examine a dataset and explain the finding, or conduct an experiment and detail the results. [Read more…]
I first met my guest at the SMTA Pan Pacific Symposium in Hawaii this past January. He was presenting a paper entitled Quantum Technology, A Theoretical Overview of the Possibilities. The more I listened to and watched his presentation, the more I wanted to learn about quantum physics and mechanics. So I selfishly invited him onto my show today so I could learn more, and perhaps you can too.
My guest today is Dr. James Whitfield. Dr. Whitfield is an associate professor of physics at Dartmouth. He earned his Bachelor’s of science and chemistry and mathematics from Morehouse University and his PhD in chemical physics from Harvard University. He was a postdoctoral fellow at Columbia University in New York, Vienna Center for Quantum Science and Technology in Vienna and Gant University and Belgium, and he is currently an Amazon visiting academic and even better than all that, he’s my guest today on the Reliability Matters Podcast.
Chris and Fred discuss an ALT or Accelerate Life Test Design Question. We love these podcasts … as we are directly answering a question from one of our listeners. Interested in hearing a response to a real-world question from a listener?
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Carl and Fred discussing a reader question about FMEAs. Specifically, whether reliability predictions (for similar systems) are valid input to the Occurrence rating in an FMEA.
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When trying to fit a probability distribution to quantitative results, sometimes the normal probability doesn’t fit. Minitab has a wealth of distributions to pick from. Do you just pick whichever one Minitab tells you fits the best? Maybe not. Just because the distribution fits your data doesn’t mean it’s a good one to use. We review my top 3 distributions for product testing and some other ones that come up but may not be appropriate to use.
We’ll also share what you need to think about when picking a distribution:
Greg and Fred discussing the right method (s) to solve quality and reliability problems specifically answering the question ‘is the approach good enough?’
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Kirk and Fred discuss the use of artificial intelligence engines such as ChatGPT in Reliability Engineering. A copy of the ChatGPT questions and responses that we discuss on this podcast is listed in the show notes below.
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How do you look at any product? See just a thing that performs a certain function? In this episode I discuss my perspective on how I see a product – as an assembly of materials – and how it influences how I help design products to meet performance, reliability, and cost goals.
SHOW NOTES
In this special episode of Quality during Design Redux, we’re pulling episodes from our archive about test results analysis.
Originally released May 2021. We talk about the importance of examining failure modes plus other topics.
If we’re not careful with or ignore failure modes, we can choose the wrong reliability model or statistical distribution. If our product performance is close to the required limits and/or we need a very accurate model, this could be a big problem.
We talk about the importance of failure modes and step-through a tensile-test example to explore these other topics: