pv magazine International

Ensemble deep learning for PV cell defect detection

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 ...
Science Daily

Researchers use machine learning to detect defects in additive manufacturing

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 ...
EurekAlert!

Ensuring flawless metal 3D printing: New research tackles high-fidelity, high-resolution process monitoring

Researchers from Northwestern University, University of Virginia, Carnegie Mellon University, and Argonne National Laboratory have made a significant advancement in defect detection and process ...
Machine Design

R&D Spotlight: Defect Detection in Metal 3D Printing, Multi-Metal Design, Plus a Novel Design Approach

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 ...
Semiconductor Engineering

Comparison Of State-Of-The-Art Models For Socket Pin Defect Detection

This article is adapted from a presentation at TestConX, March 5-8, 2023, Mesa, AZ, by Vijayakumar Thangamariappan, Nidhi Agrawal, Jason Kim, Constantinos Xanthopoulos, Ira Leventhal, and Ken Butler, ...
EurekAlert!

Researchers use machine learning to detect defects in additive manufacturing

Longitudinal (top) and axial (middle) images of X-Ray CT data of parts with 6 internal defects: a spherical clog, a stellated shaped clog, a cone shaped void, a blob shaped void, an elliptical warp of ...