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 ...
Business Wire

Do Batteries Need Medicine? Argonne Researchers Enhance Battery Performance With Electrolyte Additives and Machine Learning

LEMONT, Ill.--(BUSINESS WIRE)--Batteries, like humans, require medicine to function at their best. In battery technology, this medicine comes in the form of electrolyte additives, which enhance ...
Devdiscourse

Smart manufacturing moves beyond Industry 4.0, forcing a rethink of industrial strategy

Manufacturing technologies have been the first domain to experience this transformation. The review documents how artificial ...
Semiconductor Engineering

Accelerating Semiconductor Innovation Through Machine Learning-Driven Modeling

Using AI and machine learning as transformative solutions for semiconductor device modeling and parameter extraction.
EurekAlert!

Neural partially linear additive model

Interpretability has drawn increasing attention in machine learning. Partially linear additive models provide an attractive middle ground between the simplicity of generalized linear model and the ...
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 ...