Design Input & Specs vs. Test & Measure Capability

When defining design inputs and specifications, what does a design engineer need to consider about the test and measure capability? What are the typical ways that we assess the variability that a test or measurement introduces into our result?

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Episode Transcript

You’re listening to an installment of the Quality during Design “Versus Series”. In this series, we’re comparing concepts within quality and reliability to better understand them and how they can affect product design engineering. We have eight episodes in this series, which means we’ll be reviewing at least 16 topics let’s get started. Hello and welcome to quality during design the place to use quality thinking to create products others love for less. My name is Diana Deeney. 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. Visit quality during design.com and subscribe.

Welcome to Quality during Design for products, others love for less. I’m your host, Diana Deeney. This is the place where we talk about quality and reliability engineering, concepts, methods, and techniques, and how they fit into product design development and design engineering. Today, we’re comparing a couple of topics, namely design inputs, and specifications versus the test and measurement capability and how we’re assessing those design inputs and specs.

Just a couple of episodes ago, we talked about how there’s variability in the manufacturing process and how that compares to the design specifications and limits that we’re setting. We concluded that there’s variation in everything, and that also includes how we’re testing and measuring the characteristics of our product. How we measure and how we test is also introducing variability. In fact, let’s take a look at the definition of measurement from the ASQ.

ASQ defines measurement as “an approximation or estimate the value of the specific quantity subject to measurement, which is complete only when accompanied by a quantitative statement of its uncertainty.” What’s important about this definition is that a measurement is really just an estimate of whatever we’re measuring and that we need to understand it’s uncertainty and the variation that the measurement method itself is introducing.

Understanding that measurement and test introduces variability in the data is important for product design engineering for several reasons. One of them is that we’re setting design specifications and limits for our products. So we need to consider the test and measurement capability for one. Are we able to test it and are we able to measure it to the significance that we’re defining our spec against? We are also controlling risk. Sometimes we are defining a detection control to control a risk of something bad happening. Is that control and is that measurement adequate enough to be able to control the risk? Is our test effective at measuring our design?

We also need to consider consistency in our test and our measurement and its outputs over time. And this could be for a lot of future development work and for different reasons during the product development design. One is proving stability of design outputs over time. Or maybe we’re developing the next generation of product and it needs to perform comparably to what we’ve had in the field for years. Are we changing materials? So is the strength the same over time from when we started to after we’ve preconditioned it or put it through some stress testing?

Level of detail is another thing that we need to think about when we’re thinking about the variability introduced by our test and measurement methods. For test plans and protocols and measuring instructions, there is a rule of thumb: another engineer 10 years from now will be able to replicate your results. Why this rule of thumb? Because it helps to control our test and our measurement systems so that when we’re looking at the results, they’re not confounded by unnecessary variability in the way we test and measure or the product.

So how do we assess the way that we test and measure? How do we figure out what uncertainty is being introduced by how we’re testing and measuring? There are several measures of how we test and measure.

When considering the whole measuring process and system, we can evaluate it against its precision in accuracy. Precision has to do with repeatability and reproducibility. When we measure the same part with our measuring process and system, are we able to consistently get the same result? When we’re evaluating our measuring process for accuracy, we’re looking at what we’re observing, what we’re measuring versus a reference standard. Can we measure the parts accurately over a range of different parts sizes and is the measurement stable over time?

When we’re thinking about equipment and instruments, we can evaluate those for their consistency and their sensitivity and readability. Consistency is the ability of the equipment to give us the same reading on the instrument scale. When the same dimension is measured. Sensitivity and readability are more constant factors about the equipment and instrument.

Now, of course, those are measures of how we measure things. Precision, accuracy, consistency, sensitivity, and readability are all things we consider when we’re designing and devising a way to measure our product. So now how do we control those measurement processes and systems and equipment and instruments so that we’re confident whenever we use them? To evaluate the precision of our measuring process or system, we can do an R and R study (repeatability and reproducibility study). To ensure accuracy and consistency, we can rely on calibration. To ensure the whole measuring process is with its equipment and instruments is in control, we might rely on a measurement assurance protocol or MAP.

What’s today’s insight to action? Continue to recognize that there’s variation in everything, including how we test and measure our products. We need to understand how that variability affects the results because we’re making decisions based off of those results. Remember that rule of thumb, that 10 years from now another engineer in a similar position as you will be able to pick up and recreate and reproduce the results that you were getting with your test and measurement system. And even if there’s standard test and measurement methods are used, just double check that it’s validated for the range that you need for your design.

If you like the content in this episode, visit quality during design.com, where you can subscribe to the weekly newsletter to keep in touch. This has been a production of Deeney enterprises. Thanks for listening.