| Abstract Scope |
This study investigates the prediction of fatigue life and fatigue crack growth rate behaviour in additively manufactured Al 2024 alloy using machine learning (ML). The fatigue behaviour of the additively manufactured alloy is influenced by a variety of factors such as defect size and location, columnar microstructure, surface finish, residual stress, etc. Hence, it is difficult to predict the fatigue behaviour using conventional methods. Key test variables, including stress amplitude, build orientation, stress ratio, and post-processing condition, were used as input variables. Experimental data were employed to train various ML models, including Support Vector Machines, Random Forests, Decision Trees, and Convolutional Neural Networks (CNN). Given the limited availability of experimental data, the training dataset was augmented with generative AI models. The pre-processed dataset, comprising both experimental and generative AI data, was utilized to train the above ML models, with hyperparameter tuning performed using kernel functions. |