22. Do Our Measurements Agree?

What do you do when your measurements don’t match what your supplier or customer measured? In this Notepad Series video we show you a technique that can help you compare the measurements—not just the differences in the average values but also the variability in the measurements. It’s very helpful for interpreting differences in data.

21. Seeing and Believing

Think about the last time you saw a zebra: was it black with white stripes, or white with black stripes? In this Notepad Series video we show how “stripes” of light and dark in surface texture can be an indicator of wear. The spacing of those stripes will depend on the surface geometry—and we’ll show … Read more

20. The Profile Graph

Trying to understand surface texture using just a parameter or two is like trying to drive at night using only your speedometer and a compass. Those tools can tell you which direction you’re headed, and how fast…but they can’t help you to correct. The situation is the same in the world of surfaces: a number … Read more

19. The Probability Parameters

Sanding a wood project — it makes a beautiful final finish (even if it takes a lot of work to get there!) A plateaued surface finish is similar to a sanded wood finish: we start with a rough process that creates the valley regions, then we go back over it with a finer process to … Read more

18. The Gaussian Filter

When you’re driving, your tires, shock absorbers, and suspension remove most of the harsh vibrations from the road. In surface texture analysis, filtering has much the same effect: separating larger “waviness” structures from the finer “roughness.” The most common filter type is the Gaussian filter, which acts like the perfect shock absorber in terms of … Read more

17. Measurement Settings

The measurement settings for roughness gages can be intimidating. There are a lot of options, and it’s not always clear what they do. In this video we make it easy for you as we point out the two most important settings: the wavelengths which define “roughness” (filtering and cutoffs), and the length of data we … Read more