Defects Detection in Rotor Composite Parts using Instance Segmentation
Résumé
The manufacturing of rotor composite parts for helicopters involves different steps, including ply preparation, molding, curing, de-molding, machining of parts, quality control and finishing phases (such as painting). During the quality control, each part is systematically inspected using a Radiographic Testing (RT) approach. This is a non-destructive testing (NDT) method, which uses x-rays to examine the internal structure of manufactured components identifying any flaws or defects within the material. In this paper, an instance segmentation approach is used to detect a specific defect in radiography images of composites parts, based on a Mask R-CNN model. Instance segmentation represents a significant advancement in computer vision compared to more conventional approaches (such as classification or object detection). Application to an industrial case is presented here with a precision to predict “defects” about 85%, a recall of 96% and a F1 score of 90%. We also introduce an original labeling technique well suited for industrial purpose.
Domaines
Intelligence artificielle [cs.AI]
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ERF2024_Defect_detection_using_Instance_Segmentation_Abstract.pdf (412.9 Ko)
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