Researchers have developed a new method for detecting defects in additively manufactured components. Researchers at the University of Illinois Urbana-Champaign have developed a new method for ...

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.

Researchers from Northwestern University, University of Virginia, Carnegie Mellon University, and Argonne National Laboratory have made a significant advancement in defect detection and process ...

Optical 3D metrology enables fast, non-contact surface roughness measurement of defects and roughness for precise ...

Tech Soft 3D, the world leader in providing engineering software development toolkits (SDKs), officially launches HOOPS AI, the first framework purpose-built to unlock AI and machine learning for CAD ...

Longitudinal (top) and axial (middle) images of X-Ray CT data of parts with 6 internal defects: a spherical clog, a stellated shaped clog, a cone shaped void, a blob shaped void, an elliptical warp of ...