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Point recall / Outliers


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Yes, but there is no easy button.

1) Measure original feature and create a “dummy” characteristic (if needed) with desired outlier removal settings.
2) Export surface points from “dummy” characteristic using writeActualsToFile() function.
3) Read export file and extract x,y,z,i,j,k values from each line with a status of “0”
4) Write extracted data to a new file.
5) Import points from newly created file using “Recall Feature Points From File”
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I feared as much.

Thanks, but I think this would take too much time to program and test. We need the results quickly, as we already wasted time and produced wrong results. So I'd need a solution that is quick and fool-proof to implement.

My problem is as follows:
I need to measure a large number of cylindrical bores. Each is measured in three depths as maximum inscribed circles. Additionally I want to recall the circle points to a maximum inscribed cylinder.
The problem is that in one depth the bore has a number of small axial ribs. To save measuring time, we scan over the ribs and eliminate them with the outlier filter afterwards.
Now when we recall all the circle points to the cylinder, the points on the ribs are back and have to be filtered out again. But now the outlier filter also affects the points in the other depths and eliminates so many points that in the end the diameter of the maximum inscribed cylinder is bigger than each of the inscribed circles, which can of course not be true.

Of course the easiest way to resolve this would be to scan in segments between the ribs. But since we need to measure a lot of parts with up to 18 bores each, and already occupy a whole machine day in day out with only this part, I'm trying to save every little bit of measuring time.
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Is it possible that manually masked points are ignored when recalling points? It sounds logical as they are explicitly excluded by the user, not just by a filter rule. I have to be sure, but can't find any reliable information on this. At least the masked points of the cylinder look the same as in the circle and there are no filters active (for testing).
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