Industrial analyzers combine machine vision with deep learning to improve recovery and reduce waste across veneer, plywood ...

Materials scientists at Rice University have developed a new workflow methodology for measuring microscopic defects in diamond and other advanced semiconductor materials. By making it easier to spot ...

Roboflow's workflow combines real and synthetic training data to develop defect detection models for manufacturing applications (Image: Roboflow) Roboflow integrates Nvidia simulation tools to train m ...

A study published in Molecules and led by researchers from the Changchun Institute of Optics, Fine Mechanics and Physics (CIOMP) of the Chinese Academy of Sciences demonstrated how deep learning can ...

Provides non-destructive 3D analysis of the location, morphology, and depth profile of internal defects in glass substratesIntroduces TAMI, a dedicated 3D analysis software platform that connects R&D ...

Artificial intelligence (AI) is able to detect foreign materials and other defects in food products faster and with more ...

This new technical paper titled “End-to-end deep learning framework for printed circuit board manufacturing defect classification” is from researchers at École de technologie supérieure (ÉTS) in ...

Detecting macro-defects early in the wafer processing flow is vital for yield and process improvement, and it is driving innovations in both inspection techniques and wafer test map analysis. At the ...

The ongoing evolution of software defect detection methodologies leveraging large language models is rapid; however, the current research landscape ...