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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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What spending reveals about downstream consumer demand for manufactured goods

For manufacturers supplying consumer-facing markets, understanding demand conditions has become increasingly important in an environment shaped by cautious spending, rising costs and intense competition.