Using X-ray beams and machine learning for detecting structural defects, such as pore formation, can help prevent failure of metal 3D-printed parts. Systematic computer-based material design uses ...

Scientists from the federally funded Argonne National Laboratory in Illinois and the University of Virginia have developed a new approach for detecting defects in metal parts produced by 3D printing.

Detecting sub-5nm defects creates huge challenges for chipmakers, challenges that have a direct impact on yield, reliability, and profitability. In addition to being smaller and harder to detect, ...