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Argonne researchers develop AI approach to detect flaws of 3D-printed parts

A research team led by the US Department of Energy’s (DOE) Argonne National Laboratory and the University of Virginia (UVA) has developed various imaging and machine-learning techniques to detect and predict the formation of pores in 3D-printed metals in real time with near-perfect accuracy.
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GridBeyond highlights role of technology in navigating energy market swings

Energy price volatility continues to pose challenges for manufacturers and other energy-intensive industries, particularly as electricity markets experience more frequent and unpredictable price swings.