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Perpendicularity graph


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It looks like the error magnification on the perpendicularity is 100. If you change it to 1000 so it is the same as the flatness, then you should see the same as the flatness but including the angle between the feature and the datum. But the angle in the perpendicularity might push it off the page.
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In a Piweb report like shown, both the minimum (always 0) and maximum points, shown with the red circles, are influencing the perpendicularity result. That is, if the low points were higher, your result would be better, and likewise if the high points were lower.

Piweb plot is a good presentation of this because the blue circle is the collection of measured points, and the grid-base is a representation of the nominal orientation of the plane. So you can clearly see that the blue collection of measured points lies in a plane that is "tilted" (relative to nominal).
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Hey Patrick, I understand know flatness SHOULD be Min Feature but I was asking why Shabu was using LSQ.

For perpendicularity, Calypso defaults to LSQ and I usually don't second guess the defaults in Calypso so it was a surprise to see it on Shabu's plot.
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Exactly! In a hard-gauging world it doesn't matter at all!

However, in Calypso, you will get different results, so some input from the programmer is required to ensure you get the real result (most accurate answer given the measurement data).
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Nope 🙂 , and for precisely the reasoned that you mentioned.
Form, however, (if you look at a plot using MIn Feature) evenly distributes the Min point & Max point. Whereas, if you change the evaluation method to LSQ, the MIn & Max points are no longer evenly distributed, and you get a *larger* out of round result.

So, it looks like, even though LSQ is not the "correct" method to use, it is "safer" to use, if your main concern is to ensure that questionable parts (bordeline "passing") do not make it to the customer.
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In my testing, I saw no difference in the results but I don't know how much I can rely on dispersion data. However, I found a part with some data and found no difference in the results. Mind you, the results shown are in metric.

I know the 2 algorithms affect how the derived feature is calculated, which like you mentioned, changes the values of the min and max point. Is it possible for the 2 evaluations to find different min and max points? Doesn't appear to in my testing.

I honestly don't care about the derived feature because it's all about the points.

Screenshot 2021-03-10 162045.jpg

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You most certainly will see a difference sometimes. 3889_5d54d36077150f3e24f012d8ee60931e.jpg
3889_bcaafaed7c730ac7831c10aee9d4bccc.jpg
My guess as to why you don't see the same difference is that outlier elimination does a fantastic job of making Min Zone very similar to LSQ. In the example above, outlier elimination has been turned off.

The reason there is a difference at all is that in these orientation controls, there are still some degrees of freedom that Calypso has to deal with, and the evaluation method is involved in how Calypso locks down those degrees of freedom.
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This is the result of changing only the evaluation method of the considered plane (with some admittedly dreadful data that helps exaggerate the deviation). The datum was "measured" without dispersion, and the datum evaluation method was left at Outer Tangential in both cases.
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