Flaw Detection

The Flaw Detection Vision Tools, SurfaceFlaw, DetectFlaw, FlexFlawModel and TrainFlawModel, are used to perform advanced pattern match analysis to verify the presence or absence of defects.

The SurfaceFlaw Flaw Detection Vision Tool is used to detect small flaws based upon pixel intensity variations, without the use of a trained model. This tool is designed for detecting flaws such as scratches, nicks, tears, stains or chips, on greyscale or color images.

The DetectFlaw, FlexFlawModel and TrainFlawModel Flaw Detection Vision Tools create a model based upon a "golden" or perfect part, and then compare the model against acquired images using an advanced pattern matching algorithm to determine if there are any flaws, such as missing edges, surface scratches or logos were printed incorrectly.

In-Sight vision systems perform detailed flaw detection analysis using the Flaw Detection Vision Tool's functions:

SurfaceFlaw: Detects local intensity variations within an image to determine if flaws are present.
DetectFlaw: Creates the parameters to determine if a flaw exists or not when comparing acquired images against the model.
FlexFlawModel: Creates optional Flex algorithms to compensate for process variations.
TrainFlawModel: Creates the model of the "golden" part.

Note: The FlexFlawModel function must reference a data structure output by a TrainFlawModel function. The DetectFlaw function may reference a data structure output by either a TrainFlawModel or DetectFlaw function.