With ViDi Suite, the automated visual inspection of complex machined parts is now extremely simple. The software algorithm trains itself on a set of known good samples which are recorded while being rotated to create its reference model.
Once this training phase is completed, the inspection is ready to proceed. Defective areas on the surface of the machined part can be reliably identified and reported. With the flexibility of ViDi Suite, the machined part can be rotated in front of the camera to perform the inspection without the need for precise synchronization between image acquisition and rotation.
CHALLENGES IN MACHINED PART INSPECTION
- Typically there are many different types of complex shapes
- Varying surface properties depending on machining tool quality and varying properties of the blank material need to be tolerated
- Some defects only show under a very particular combination of illumination, camera and surface orientation
Learn more at Cognex here.
In-Sight ViDi Read Tool
The In-Sight ViDi Read tool deciphers badly deformed, skewed, and poorly etched codes using optical character recognition (OCR). It works right out of the box, dramatically reducing development time, thanks to the deep learning pretrained font library. Simply define the region of interest and set the character size. In situations where new characters are introduced, without vision expertise, this robust tool can be retrained to read application-specific codes that traditional OCR tools are not able to decode.
In-Sight ViDi Check Tool
The In-Sight ViDi Check tool uses artificial intelligence to reliably detect complex features and objects and verifies parts and kits are assembled correctly based on their location within a pre-defined layout. It can be trained to create an extensive library of components, which can be located in the image even if they appear at different angles or vary in size.
In-Sight ViDi Detect Tool
The In-Sight ViDi Detect tool is ideal for finding anomalies on complex parts and surfaces, even in situations where defects can be unpredictable in their appearance. It learns from images of good parts in order to identify defective parts. This allows the tool to detect a wide range of defects that do not need to be pre-defined at the time of training.
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