Automotive Final Assembly Verification
Ensure all automotive components are present and correctly assembled using Cognex Deep Learning
Graphical programming environment for deep learning-based industrial image analysis
Machine vision is used throughout the automotive component manufacturing process to rigorously monitor and catch quality defects. Final assembly verification has traditionally been done with human operators, as the various pieces of trim involved in final assembly introduce a high degree of complexity that challenges traditional machine vision inspections. Human inspectors verify that all parts, such as interior pieces like door trim, window switches, and door handles, are present and correctly assembled. Exterior checks for color, badges, headlights, and other components are also done at the final stage of car assembly. Human inspectors, though skilled at identifying varying parts as different models move down the line under changing lighting conditions, can be slow and inconsistent.
Cognex Deep Learning learns the finished appearance of the many car components to identify improperly placed parts. It is able to do this as accurately as a human inspector, but with the speed and reliability of an automated system. Using the location tool a user can build up a component library of trained features. This library of components can contain a wide range of parts, from badges to door handles, for the tool to locate and identify within the image. By adding a verification step using the location tool, the software can provide a pass or fail result based on all of the components that must be assembled. Using this approach, final assembly verification can now be automated.