Abstract: Severe heat generation in power electronic devices is often the biggest culprit in harming their reliability. Our work analyses the heat loss composition and numerically calculates each ...

By accounting for build orientation, layer direction, infill patterns and material-specific properties, the solution delivers prediction accuracy that early customers report enables weight reductions ...

Researchers are closing the reliability gap, tackling the physics, materials, robotics, and workflow gaps holding back large-scale industrial adoption.

A new study published in AI Materials presents a comprehensive multi-scale computational framework capable of predicting the ...

Abstract: Deep learning shows great potential in the field of structural damage recognition, but training requires a large amount of high-quality data. In this paper, we take the subway sleeper girder ...