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aside from Golden unit, what is the best way to correlate vision systems?
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If you mean physical correlation, there need's the be some kind of calibration. If you mean data exchange, it depends heavily on the system you use. E.g. in Beckhoff's TwinCAT Vision you can develop multiple vision stations at once in one engineering environment and monitor all stations aggregated in one HMI.
Hello Jay; I believe by correlation you mean the actual validation of your machine vision inspection system's performance. This is a topic that I won't be able to answer in this limited Q&A format, but the short response is that in some applications a set of "golden" parts is the correct way to validate system performance. This works well particularly in cases where the vision task is a "go/no-go" inspection or an inspection for well-defined defects or features. For more subjective inspections, a regular statistical analysis of accepted and failed part samples can provide some validation of performance. For measurements, the vision system reliability should be validated using standard MSA (measurement systems analysis) techniques and statistics like a Gauge R&R, P/T (precision/tolerance) analysis or others. If you'd like to discuss further, feel free to contact me directly. Best Regards, David
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Jay -- your question mentions "systems" (plural). I infer from the plural that you want to know if you are getting nearly identical results from two or more nominally identical systems. A "Golden Unit" means, to me, a part that is as close to ideal as possible and should give a known result (within some tolerance band). Comparing the results from multiple systems to a Golden Unit only correlates one point on a spectrum of possible results. Yes, use a Golden Unit to start, and assuming good correlation between systems on the Golden Unit, collect a spectrum of other parts spanning the widest range of variations possible. Using those parts, look for correlation between systems. While the Golden Unit must give a prescribed result, these other parts do not need to give a prescribed result; each part gives results across the systems that agree within some tolerance.