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Conceptual diagram showing AI-based analysis and prediction of how powder characteristics and process conditions affect defects and component performance in metal 3D printing processes.
 (Source: Korea Institute of Materials Science (Kims))
Quality control in AM

AI enables defect-aware prediction of metal 3D-printed part quality

Metal additive manufacturing is moving fast in metalworking, but one issue still blocks wider shop-floor adoption: internal defects that don’t show up until parts fail in service. A team from Kims and the Max Planck Institute has developed an explainable AI model for laser powder bed fusion that evaluates not just how much porosity a part has, but what kind of pores form, where they sit, and how they weaken mechanical performance.

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