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ViDi Welding Seam Inspection

With ViDi Suite, the automated optical inspection of welding seams is now extremely simple. The software algorithm trains itself on a set of known good samples which are presented in front of the camera and creates its reference model.

With its powerful statistical algorithm, ViDi Suite can train on a large amount of images representing all the process and image variations. Once this training phase is completed, the inspection is ready to go. Defective welding seams can be reliably identified and reported.

CHALLENGES IN WELDING SEAM INSPECTION

  • Welding seams exhibit a large variety of shapes and features which can hardly be described by classical means
  • Normal and expected variations in the welding process and material need to be toleratedCognex ViDi
  • The highly reflective and irregular metallic surface renders as a complex texture in the image

 

In-Sight ViDi Read Tool

In-Sight ViDi Read applications

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

In-Sight ViDi Check applications

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

In-Sight ViDi Detect applications

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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