Additive manufacturing, such as 3D printing, provides an excellent opportunity to design metamaterials: materials with an ...
A new study explores deep learning for image-based defect detection during 3D printing, looking to catch bad builds.
“Semiconductor lithography inspection requires reliable detection of small pattern defects such as bridge, burr, pinch, and contamination. In this study, we propose a two-stage vision-language ...
A new research review looks at how computer vision and machine learning could be used to spot defects in 3D printed concrete. That sounds like a narrow research topic. It isn’t. Construction 3D ...
A high-precision, real-time system to detect defects in fabric (Hole, Oil, Crack, Stain, Damage) using YOLOv8. This project features a modern Flask-based web interface for easy interaction and ...
The system, developed by Panevo, a Canadian clear technology and manufacturing analytics company, reportedly achieved approximately 97% detection reliability with minimal false positives of Muskoka’s ...
This research presents a deep learning-based automated product defect detection system to address limitations of conventional manual inspection techniques that are labor-intensive and prone to errors.
ABSTRACT: Rail defects, both internal and external, pose significant safety risks. Acoustic Emission (AE) technology has emerged as a promising method for monitoring damage progression and detecting ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results