首页|University of Wisconsin Madison Researcher Highlights Research in Machine Learning (Deep-Learning-Based Segmentation of Keyhole in In-Situ X-ray Imaging of Laser Powder Bed Fusion)
University of Wisconsin Madison Researcher Highlights Research in Machine Learning (Deep-Learning-Based Segmentation of Keyhole in In-Situ X-ray Imaging of Laser Powder Bed Fusion)
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Investigators publish new report on artificial intelligence. According to news originating from Madison, Wisconsin, by NewsRx correspondents, research stated, “In laser powder bed fusion processes, keyholes are the gaseous cavities formed where laser interacts with metal, and their morphologies play an important role in defect formation and the final product quality.” Funders for this research include National Science Foundation; U.S. National Science Foundation Training-based Workforce Development. Our news correspondents obtained a quote from the research from University of Wisconsin Madison: “The in-situ X-ray imaging technique can monitor the keyhole dynamics from the side and capture keyhole shapes in the X-ray image stream. Keyhole shapes in X-ray images are then often labeled by humans for analysis, which increasingly involves attempting to correlate keyhole shapes with defects using machine learning. However, such labeling is tedious, time-consuming, error-prone, and cannot be scaled to large data sets. To use keyhole shapes more readily as the input to machine learning methods, an automatic tool to identify keyhole regions is desirable. In this paper, a deep-learning-based computer vision tool that can automatically segment keyhole shapes out of X-ray images is presented. The pipeline contains a filtering method and an implementation of the BASNet deep learning model to semantically segment the keyhole morphologies out of X-ray images.”
University of Wisconsin MadisonMadisonWisconsinUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine Learning
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