Abstract
Additive manufacturing (AM) process-induced defects make the fatigue life prediction of AM-built parts challenging. 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.
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