Abstract: A method and system for nondestructively detecting and quantifying material anomalies within materials, including composite articles. The method entails performing a three-dimensional imaging scan technique, such as a computed tomography scan, of the material and a reference standard such that a test image of the material and a reference image of the reference standard appear in a plurality of two-dimensional scan views generated by the scan technique. The reference images are located in the scan views and normalized to determine at least an average value of the pixel data for the reference images. Values of pixel data of the test image are determined in each scan view, and then compared to the pixel data of the reference images to detect the presence of an anomaly in the test images. The detected anomaly in at least one of the test images of the scan views is then compared to a requirement standard for the material.

Abstract: A method for identifying defects in radiographic image data corresponding to a scanned object is provided. The method includes acquiring radiographic image data corresponding to a scanned object. In one embodiment, the radiographic image data includes an inspection test image and a reference image corresponding to the scanned object. The method includes identifying one or more regions of interest in the reference image and aligning the inspection test image with the regions of interest identified in the reference image, to obtain a residual image. The method further includes identifying one or more defects in the inspection test image based upon the residual image and one or more defect probability values computed for one or more pixels in the residual image.

Abstract: A method for automatically identifying defects in turbine engine blades is provided. The method comprises acquiring one or more radiographic images corresponding to one or more turbine engine blades and identifying one or more regions of interest from the one or more radiographic images. The method then comprises extracting one or more geometric features based on the one or more regions of interest and analyzing the one or more geometric features to identify one or more defects in the turbine engine blades.

Abstract: An anomaly detection method includes acquiring image data corresponding to nondestructive testing (NDT) of a scanned object. The NDT image data comprises at least one inspection test image of the scanned object and multiple reference images for the scanned object. The anomaly detection method further includes generating an anomaly detection model based on a statistical analysis of one or more image features in the reference images for the scanned object and identifying one or more defects in the inspection test image, based on the anomaly detection model.

Abstract: A method and system for nondestructively detecting and quantifying material anomalies within materials, including composite articles. The method entails performing a three-dimensional imaging scan technique, such as a computed tomography scan, of the material and a reference standard such that a test image of the material and a reference image of the reference standard appear in a plurality of two-dimensional scan views generated by the scan technique. The reference images are located in the scan views and normalized to determine at least an average value of the pixel data for the reference images. Values of pixel data of the test image are determined in each scan view, and then compared to the pixel data of the reference images to detect the presence of an anomaly in the test images. The detected anomaly in at least one of the test images of the scan views is then compared to a requirement standard for the material.

Abstract: An anomaly detection method includes acquiring image data corresponding to nondestructive testing (NDT) of a scanned object. The NDT image data comprises at least one inspection test image of the scanned object and multiple reference images for the scanned object. The anomaly detection method further includes generating an anomaly detection model based on a statistical analysis of one or more image features in the reference images for the scanned object and identifying one or more defects in the inspection test image, based on the anomaly detection model.

Abstract: A method for identifying defects in radiographic image data corresponding to a scanned object is provided. The method includes acquiring radiographic image data corresponding to a scanned object. In one embodiment, the radiographic image data includes an inspection test image and a reference image corresponding to the scanned object. The method includes identifying one or more regions of interest in the reference image and aligning the inspection test image with the regions of interest identified in the reference image, to obtain a residual image. The method further includes identifying one or more defects in the inspection test image based upon the residual image and one or more defect probability values computed for one or more pixels in the residual image.

Abstract: A method for automatically identifying defects in turbine engine blades is provided. The method comprises acquiring one or more radiographic images corresponding to one or more turbine engine blades and identifying one or more regions of interest from the one or more radiographic images. The method then comprises extracting one or more geometric features based on the one or more regions of interest and analyzing the one or more geometric features to identify one or more defects in the turbine engine blades.