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Primary datum that is an irregular feature

I have a part that is 115 inches long and has a primary datum feature that is essentially a small segment of an enormous arc. Secondary and tertiary features are both holes drilled on the primary feature. I started with an iterative alignment to synchronize the part and model and then probed points on datum feature A, measured circles datum B then C. I then constructed a plane from datum A, defined my datums and used XACT measure to report other hole positions relative to ABC.

I feel I did this wrong... due to constructing the plane from datum A as the true vector of the constructed plane can incorporate additional error (or so I've read). I know there may be a few ways of doing this but what would be your ideal way of inspecting a part like this? Every feature on the part was relative to this datum structure.

I started with an iterative alignment, took a few points on the end faces, the edges of the part and top of the part. After this could I have just created a vector least squares best fit alignment to the primary feature (then exit best fit window) and origin to B and rotate to C?

In the beginning (after creating an iterative alignment) I tried to constrain rotation of X and Y and translation on Z with the points probed on datum A but i was not successful (not sure why), since I don't use the best fit alignment much.

(to better visualize this part, this pretty much looked like a 115" long by 8 inches wide, .500 thick with holes on it BUT it was formed, carbon fiber, and primary datum is irregular/banana shaped)
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  • You could also just do an iterative alignment with the vector points on the Datum A feature surface (and the B and C features). Then there's no need for constructions. Although, that does not work if you want to use XactMeasure. Then you need to follow Anders' suggestion.
  • Nice. Thank you. I've been a bit skeptical about directly measuring from an iterative alignment but I see where it's beginning to serve its purpose, I usually only used it as a "rough alignment". So if I used the best fit method, and iterate to the measured points, then pcdmis uses the fitting algorithm (in this case vector least squares) go align 3Dimensionally?
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  • Nice. Thank you. I've been a bit skeptical about directly measuring from an iterative alignment but I see where it's beginning to serve its purpose, I usually only used it as a "rough alignment". So if I used the best fit method, and iterate to the measured points, then pcdmis uses the fitting algorithm (in this case vector least squares) go align 3Dimensionally?
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