Fatigue-life prediction of additively manufactured metals by continuous damage mechanics (CDM)-informed machine learning with sensitive features

Additive manufacturing (AM) process-induced defects make the fatigue life prediction of AM-built parts chal-lenging. A machine learning (ML) framework based on sensitive features and continuous damage mechanics (CDM) herein is proposed to predict the fatigue life of AM-built parts. The sensitive features are extracted to blunt the disturbing effect of causality among the features. The CDM theory considering AM parameters is conducive to constructing a physics-informed ML model. This work employs support vector machines and random forests to predict the fatigue life of AM-built AlSi10Mg alloy. The results demonstrate that the physical knowledge-guided ML model using sensitive features exhibits better performance of fatigue life prediction.

Automated summary

This study proposes a machine learning framework based on sensitive features and continuous damage mechanics to predict the fatigue life of additive manufacturing (AM)-built parts. By extracting sensitive features and considering AM parameters in the continuous damage mechanics theory, a physics-informed machine learning model can be constructed for prediction.