Publication15

The present work is focused on machine learning-assisted predictions of the low cycle fatigue behaviour and fatigue crack growth rate (FCGR) of 17- 4 PH SS processed through L-PBF and post-processing. Various machine learning techniques reported in the literature provided a flexible approach for explaining the complex mathematical interrelationship among processing-structure-property of the materials. In the present work, four machine learning (ML) algorithms, such as K- Nearest Neighbor (KNN), Decision Trees (DT), Random Forests (RF), and Extreme Gradient Boosting (XGB) algorithms, are implemented to analyze the Fatigue Crack growth rate (FCGR) of 17-4 PH SS alloy. After optimizing the hyper parameters for these algorithms, the trained models were found to estimate the unseen data as equally well as the trained data. The four tested ML models are compared among each other over the training as well as the testing phase based on their mean squared error and R2 scores. Extreme Gradient Boosting model has performed better for the FCGR predictions providing the least mean squared errors and higher R2 scores compared to other models.