To understand how ViDi 3. 4 solves the assembly challenge, consider the common challenges outlined earlier in this article: variability, mix, and changeover. Unlike traditional
vision solutions, where multiple algorithms must be chosen, sequenced, programmed, and
configured to identify and locate key features in an image, ViDi’s Blue Tool learns by analyzing images that have been graded and labeled by an experienced quality control technician. A single ViDi Blue Tool can be trained to recognize any number of products, as well
as any number of component/assembly variations.
Acar door panel assembly verification, for instance, includes checks for specific window switches and trim pieces
depending on the door being assembled.
When components are combined into larger assemblies, such as a printed circuit board, then defects, random product placement, lighting variations, and other factors can quickly overwhelm a traditional vision
system. (photo: Cognex)
Programming traditional vision systems to confirm the size, location, and position for every critical
component is time consuming and impractical. (photo: Cognex)
A car door panel assembly verification, for instance, includes checks for specific window
switches and trim pieces depending on the door being assembled. The same factory can produce doors for different trim levels as well as for different countries. A single Blue Tool can be
trained to locate and identify each type of window switch and trim piece by using an image
set that introduces these different components. By training the tool over a range of images, it
develops an understanding of what each component should look like and is able to locate and
distinguish them in production.
Unlike conventional machine vision systems, which require different algorithms combined in different ways for each object of interest in an image, ViDi’s Blue Tool can locate any
number of different components without explicit programming. By capturing a collection of
images, it incorporates naturally occurring variation into the training, solving the challenges
of both product variability and product mix during assembly verification.
Finally, to make sure that the correct type of window switches and trim pieces are installed,
ViDi 3. 4 uses Layout Models. With a Layout Model, the user simply draws different regions of
interests in the image’s field of view to tell the system to look for a specific trained component
(driver’s side window switches) in a specific location. The Layout Model is also accessible and
configurable through the runtime interface. No additional off-line development is required,
simplifying product changeovers.
DEEP LEARNING CONTINUES TO EVOLVE
The number of factors required to verify final assembly combined with the difficulty of
training conventional machine vision systems to navigate broad variations has made it nearly
impossible to automate final assembly inspection. Deep learning technology is rewriting that
script, however. With deep learning tools, manufacturers in every industry can now automate
the assembly verification process by breaking the application into two steps.
The first step is to train the deep learning neural network to locate each component type. The
second step is to verify each component is of the correct type and in its correct location.
As manufacturers continue to employ deep learning tools, they can accumulate production
images to re-train their systems to accommodate future manufacturing variances. This may
help limit future liability in the event that unknown defects affect a product that has been
For more information download our free guide, Deep Learning Image Analysis for Assembly
Verification (URL: https://www.cognex.com/assembly-verification-deep-learning).