AI plays a role in improving defect capture rate and distinguishing between yield-killing and nuisance defects. New developments in wafer edge inspection are proving essential to bonded wafer yields.

A systematic review newly published in the Journal of Pipeline Science and Engineering maps machine learning (ML) advances for pipelines across the full lifecycle: reliability-based design, structural ...

Abstract: This paper presents a novel approach to automating aircraft surface inspection by leveraging deep learning techniques for the classification of thirteen different types of defects across the ...

To showcase the launch of its physical AI automation platform at Automate 2026, Universal Robots demonstrated its UR7e robots ...

Artificial intelligence is transforming how we live and work, from personalized recommendations to health care innovation.

Main difference between Edge AI and traditional cloud-based AI is how ML models are processed and deployed in both models ...

Abstract: Automated optical inspection (AOI) is widely used by manufacturers for the detection of defects in printed circuit boards (PCBs). Recent works have proposed to apply deep learning for defect ...

Artificial intelligence (AI) can put together readings from multiple sensors more effectively than classic technology, ...

A new study explores deep learning for image-based defect detection during 3D printing, looking to catch bad builds.

CNA is using AI, analytics and cloud technologies to improve underwriting, streamline operations and support long-term ...

Materials testing remains a cornerstone of engineering and manufacturing, ensuring that components and structures—from ...

Firms that deploy AI and automated equipment can potentially gain advantages in safety, speed and cost, but the legal infrastructure to ...