Trim Final Assembly Verification
Cognex Deep Learning defect detection tool confirms the presence and placement of components on a confusing background
The various pieces of trim involved in final assembly verification introduce a high degree of complexity that challenges traditional machine vision inspections. Human inspectors verify that all parts, such as wire bands and metal housings, are present and correctly assembled. Subtle lighting variations make it difficult to tell whether the bands are in their correct housing. Human inspectors, though skilled at identifying wire bands, can be slow and inconsistent. Cognex Deep Learning uses deep learning-based image analysis to learn the finished appearance of a piece of trim and identify missing bands as accurately as a human inspector, but with the speed and reliability of an automated system.
Using the Cognex Deep Learning defect detection tool in supervised mode, a technician trains the system on “bad” images of trim where the wire is absent, as well as known “good” images where the wire is present, to create a reference model for a complete piece of trim. Using this model, Cognex Deep Learning identifies trim pieces with missing wire bands as anomalous and defective, failing them during final inspection.