Tag: Kamel Fezzaa
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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Responding to manufacturing demand and performance signals
Australian manufacturers are navigating a challenging environment. Just as output had returned to growth, higher fuel and freight costs, rising input prices and global supply chain disruption renewed pressure on margins and cash flow.
