The emergence of deep learning software has introduced new tools that “learn” by analyzing images
that a quality control technician has first graded and labeled. Vision systems that leverage deep learning
algorithms can be trained to recognize any number of component/assembly variations. (photo: Cognex)
Programming and reprograming an inspection system for each new assembly, grouping, package, or kit would incur the same high o;ine engineering costs as for assemblies with high
EVERY INDUSTRY VALUES ASSEMBLY VERIFICATION
Almost every industry produces some type of assembly, packaging, or kitting application, either as a ;nal inspection step, an interim inspection step prior to adding value,
or some combination of both. Similarly, deep learning technology o;ers solutions that
span virtually every market. Cognex is working with leaders across several verticals to
optimize deep learning solutions for both ;nal and in-line assembly veri;cation.
Automotive assemblies that comprise thousands of individual components are emblematic of the need for more sophisticated inspection systems. A single missing hose
or plug can stall a production line, costing millions of dollars an hour. Worse, they may
result in consumer safety issues, such as a seized engine or a fatally ;awed brake system.
Automakers negotiate contracts and their suppliers that closely stipulate acceptable levels of defects per shipment as well as the ;nancial penalties due for failing to meet those
levels. Subsequently, inadequate inspection to con;rm that ;nal assemblies are complete
and correct can spell signi;cant ;nancial penalties, loss of key customers, and costly
Similar ;nancial and safety risks occur in the food industry, as well as medical packaging and kitting. If someone with severe food allergies mistakenly ingests food containing
nuts, for example, the result could be fatal. ;e dangers of incomplete surgical kits or
incorrectly packaged medical supplies are obvious, and just as clearly unacceptable to manufacturer and customer alike.
Additionally, the high-volume production of electronics operates on razor-thin margins.
A single missing screw in a laptop or cellphone screen assembly can result in thousands of
dollars in lost revenue.
In each of these areas, Cognex is actively working with market leaders to develop deep
A DEEP LEARNING SOLUTION FOR ASSEMBLY VERIFICATION
With the release of ViDi 3. 4, Cognex’s deep learning vision so;ware, automated inspection of complex assemblies, packages, and kits is not only possible but is signi;cantly sim-pli;ed compared to traditional machine vision solutions.
As part of the ViDi 3. 4 release, Cognex added a Layout Model feature to the Blue Locate
Tool. For assembly, packaging, and kitting applications, ViDi depends primarily on the
Blue Tool and the Layout Model feature to locate and verify assemblies.
How Deep Learning is
Different from Traditional
Unlike conventional machine vision solutions, deep learning machine vision tools are not explicitly programmed. Rather than numerically defining an image feature or object within the overall
assembly by shape, size, location, or other factors, deep learning
machine vision tools are trained by example.
A neural network requires a comprehensive set of training images
that represents all potential variations in visual appearance that
would occur during production.
For feature or component location as part of an assembly process,
the image set should capture the various orientation, positions,
and lighting variation the system will encounter once deployed.
Training the system to recognize new components or assemblies
simply requires the addition of new representative data sets.