Fatigue Crack Growth Rate Prediction in Nickle based Super-alloys using Machine Learning Algorithm

The state-of-the-art research in any field is inclined towards the use of artificial intelligence (AI) and machine learning (ML) techniques due to their versatility and ease of application without compromise in the results. Predicting the service life of structural members subjected to fatigue loading, is a daunting task and is currently dependent on experimental, finite element (FE) and/or numerical approaches. Damage tolerant (DT) design incorporates fatigue crack growth rate (FCGR) test data to avoid catastrophic structural failure and is proven to be successful in predicting the life of a component. Such predictions also prove beneficial in applications such as structural health monitoring (SHM) and planning non-destructive inspection. Prediction methods and models, currently used, are application specific or inconsistent across different applications. Hence in the present work, an ML algorithm is proposed to predict the Paris region of the FCGR curve for nickel based super alloys, viz. GTM 720 and GTM 718. Experimental dataset for various stress ratios (R) of the alloys were used to train the back propagation neural network (BPNN) algorithm. The algorithm was appropriately tuned and evaluated so as to fit the training data well and achieve good generalization. Using this trained ML algorithm, the FCGR was predicted for a different stress ratio and compared with experimental results of GTM 720 and GTM 718, available in literature. The results obtained are quite comparable indicating promising applications for ML based algorithms in FCGR prediction.