Welding Seam Inspection
Cognex Deep Learning defect detection and classification tools simplify the automated inspection and classification of welding seam defects
Cognex Deep Learning inspects the integrity of critical powertrain components like pistons, whose complex surface texture make traditional machine vision inspections difficult. A piston’s welding seams are highly variable, making abnormalities difficult to distinguish. Certain welding anomalies, like missing, overpowered, or underpowered welds, are unwanted. Other anomalies, like overlapping seams, are desirable and required for safety reasons. Dark image areas introduce additional complications. Given the many possible flaws and lighting challenges, deep learning-based analysis offers a simple and robust alternative to traditional machine vision inspection.
With Cognex Deep Learning the automated analysis of metal piston welding seams becomes simple. The engineer trains the software with the defect detection tool in supervised mode on a set of “bad” images representing all welding anomalies, including overlapping seams, and on “good” samples without any anomalies. In this way, all anomalies—both those that are desired as well as those that are a cause for rejection—are identified as defects. In the second part of the inspection, the engineer uses the classification tool to classify seam defects by type. Based on the model developed during supervision, the software extracts information about specific defects and separates overlapping seams into their own class. By using the defect detection tool and classification tool together, the automotive manufacturer is assured that the inspection system identifies all welding seams and successfully classifies the overlapping seams.