XIM: X-Ray Inspection Module for Automatic High Speed Inspection of Turbine Blades and Automated Flaw Detection and Classification

Under military manufacturing technology funding, a production prototype X-ray Inspection Module (XIM) has been established at General Electric Corporate Research and Development (GE-CRD) and delivered to Quality Technology (QT), General Electric Aircraft Engine Business Group (GE-AEBG). A company funded production unit has been built by GE-AEBG and delivered to the GE-AEBG manufacturing facility in Madisonville, Kentucky where it is in use in production. Computerized tomography (CT) and digital fluoroscopy (DF) images are produced with the system. The CT images provide an image cross-section, and the DF images are much like chest X-rays.The system was designed to automatically inspect and analyze flaws present in turbine blades. It was applied to two flaw types; each type in a different turbine blade. The image processing is performed on complex gray scale images with varying background. The XIM system may be used either automatically or in a manual mode with a trained operator to interpret the images and make quality decisions

Middle Earth Bestiary : an honors thesis (HONRS 499)

There is no abstract available for this thesis.Thesis (B.?.)Honors Colleg

Object instance identification with fully convolutional networks

International audienceThis paper presents a novel approach for instance search and object detection, applied to museum visits. This approach relies on fully convo-lutional networks (FCN) to obtain region proposals and object representation. Our proposal consists in four steps: a classical convolutional network is first fined-tuned as classifier over the dataset, next we build from this network a second one, fully convolutional, trained as classifier, that focuses on all regions of the corpus images, this network is used in a third step to define image global descriptors in a siamese architecture using triplets of images, and eventually these descriptors are then used for retrieval using classical scalar product between vectors. Our framework has the following features: i) it is well suited for small datasets with low objects variability as we use transfer learning, ii) it does not require any additional component in the network as we rely on classical (i.e. not fully convolutional) and fully convolutional networks, and iii) it does not need region annotations in the dataset as it deals with regions in a unsupervised way. Through multiple experiments on two image datasets taken from museum visits , we detail the effect of each parameter, and we show that the descriptors obtained using our proposed network outperform those from previous state-of-the-art approaches

XIM: X-Ray Inspection Module for Automatic High Speed Inspection of Turbine Blades and Automated Flaw Detection and Classification

Under military manufacturing technology funding, a production prototype X-ray Inspection Module (XIM) has been established at General Electric Corporate Research and Development (GE-CRD) and delivered to Quality Technology (QT), General Electric Aircraft Engine Business Group (GE-AEBG). A company funded production unit has been built by GE-AEBG and delivered to the GE-AEBG manufacturing facility in Madisonville, Kentucky where it is in use in production. Computerized tomography (CT) and digital fluoroscopy (DF) images are produced with the system. The CT images provide an image cross-section, and the DF images are much like chest X-rays.The system was designed to automatically inspect and analyze flaws present in turbine blades. It was applied to two flaw types; each type in a different turbine blade. The image processing is performed on complex gray scale images with varying background. The XIM system may be used either automatically or in a manual mode with a trained operator to interpret the images and make quality decisions.</p