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 ...

Advances in deep learning have transformed the field of infrastructure maintenance, particularly in the automated detection and characterisation of defects in sewer pipelines. Leveraging large volumes ...

Researchers have tested eight stand-alone deep learning methods for PV cell fault detection and have found that their accuracy was as high as 73%. All methods were trained and tested on the ELPV ...

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 ...

Magnetic flux leakage (MFL) testing is a widely established non‐destructive evaluation technique used to assess the integrity of ferromagnetic materials in applications such as pipeline inspection and ...

Automated optical inspection (AOI) is a cornerstone in semiconductor manufacturing, assembly and testing facilities, and as such, it plays a crucial role in yield management and process control.

In industry, the detection of anomalies such as scratches, dents, and discolorations is crucial to ensure product quality and safety. However, conventional methods rely on heavy computational ...

Failure analysis (FA) is an essential step for achieving sufficient yield in semiconductor manufacturing, but it’s struggling to keep pace with smaller dimensions, advanced packaging, and new power ...