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 ...

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 ...

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

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 ...

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

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

In today’s rapidly evolving automotive and logistics industries, efficiency, accuracy, and data intelligence have become the core pillars of competitive fleet operations. Traditional manual vehicle ...

From UN Women highlighting AI’s growing gender and racial bias, to JustAI’s $17 million Series A raise, YourStory brings you today’s headlines with the latest developments across sectors As global ...

Revolutionizing Vehicle Inspection Through AI The logistics industry depends heavily on vehicle availability and reliability. Any unexpected downtime can significantly impact delivery schedules, ...