SOURCE-LINKED INTELLIGENCE

Wide-ranging Probabilistic Physics-guided Machine Learning Approach to Break Down the Limits of Current Fatigue Predictive Tools for Metals

CORDIS · observation · Publication date unknown

Wide-ranging Probabilistic Physics-guided Machine Learning Approach to Break Down the Limits of Current Fatigue Predictive Tools for Metals It appears paradoxical how today's frontier & high-impact research seeks for designing new materials to delay structural failures – especially fatigue – while the same effort is not seen concerning the way materials can be efficiently and safely used in real structural applications. BREAKDOWN aims to transform engineering products’ design methods by identifying and including a set of (sub)micro-scale material inhomogeneities characteristics in a novel probabilistic framework. The time has come to exploit modern experimental techniques to probe material properties at a small scale, which are scarcely involved in current fatigue characterisation schemes. To attain this very ambitious goal, the project will rely on a breakdown of different classes of inhomogeneities to advance the fundamental m

Read original source ↗ Open in workspace

recordType

award

status

SIGNED

region

EU

value

1499954

unit

EUR