Graphical Feature Construction-Based Deep Learning Model for Fatigue Life Prediction of AM Alloys

Fatigue failure poses a serious challenge for ensuring the operational safety of critical components subjected to cyclic/random loading. In this context, various machine learning (ML) models have been increasingly explored, due to their effectiveness in analyzing the relationship between fatigue life and multiple influencing factors. Nevertheless, existing ML models hinge heavily on numeric features as inputs, which encapsulate limited information on the fatigue failure process of interest. To cure the deficiency, a novel ML model based upon convolutional neural networks is developed, where numeric features are transformed into graphical ones by introducing two information enrichment operations, namely, Shapley Additive Explanations and Pearson correlation coefficient analysis. Additionally, the attention mechanism is introduced to prioritize important regions in the image-based inputs. Extensive validations using experimental results of two laser powder bed fusion-fabricated metals demonstrate that the proposed model possesses better predictive accuracy than conventional ML models.