Machine Learning Based Predictions of Fatigue Crack Growth Rate of Additively Manufactured Ti6Al4V

The present work focusses on machine learning assisted predictions of the fatigue crack growth rate (FCGR) of Ti6Al4V (Ti64) processed through laser powder bed fusion (L-PBF) and post processing. Various machine learning techniques have 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 Ti64 alloy. After tuning 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 with each other over the training as well as testing phase, based on their mean squared error and R2 scores. Extreme Gradient Boosting has performed better for the FCGR predictions providing least mean squared errors and higher R2 scores compared to other models.

Effect of Material Heterogeneity on Environmentally Assisted Cracking Growth Rate of Alloy 600 for Safe-End Welded Joints

Environmentally assisted cracking (EAC) is essential in predicting light water reactors’ structural integrity and service life. Alloy 600 (equivalent to Inconel 600) has excellent corrosion resistance and is often used as a welding material in welded joints, but material properties of the alloy are heterogeneous in the welded zone due to the complex welding process. To investigate the EAC crack growth behavior of Alloy 600 for safe-end welded joints, the method taken in this paper concerns the probability prediction of the EAC crack growth rate. It considers the material heterogeneity, combining the film slip-dissolution/oxidation model, and the elastic-plastic finite element method. The strain rate at the crack tip is a unique factor to describe the mechanical state. Still, it is challenging to accurately predict it because of the complicated and heterogeneous material microstructure. In this study, the effects of material heterogeneity on the EAC crack growth behavior are statistically analyzed. The results show that the material heterogeneity of Alloy 600 can not be ignored because it affects the prediction accuracy of the crack growth rate. The randomness of yield strength has the most influence on the EAC growth rate, while Poisson’s ratio has the smallest.

Comparison of Microstructure and Mechanical Properties of High Strength and Toughness Ship Plate Steel

E36 ship plate steel was, respectively, produced by as rolling and normalizing process (ARNP), and EH36 and FH36 ship plate steel was produced by the thermo-mechanical control process (TMCP) with low carbon and multi-element micro-alloying. The microstructure of the three grades of ship plate steel was composed of ferrite, pearlite, and carbides at room temperature. The average grain size on 1/4 width sections (i.e., longitudinal sections) of the three grades of ship plate steel was, respectively, 5.4 μm, 10.8 μm, and 11.9 μm. EH36 and FH36 ship plate steel had the higher strength due to precipitation and grain boundary strengthening effect, while the E36 ship plate steel had the lower strength due to the recovery phenomenon in the normalizing process. EH36 and FH36 ship plate steel had higher impact toughness due to lower carbon (C) and silicon (Si) content and higher manganese (Mn) content than E36 ship plate steel. E36 ship plate steel had the best plasticity due to the two strong {110} and {111} texture components. The fracture toughness KJ0.2BL(30) values of E36 and EH36 and KJ0.2BL value of FH36 ship plate steel were, respectively, obtained at 387 MPa·m1/2, 464 MPa·m1/2 and 443 MPa·m1/2. EH36 and FH36 ship plate steel had higher KJ0.2BL(30) due to lower C and Si and higher Mn, niobium (Nb), vanadium (V), and aluminum (Al) content than the E36 ship plate steel. The fatigue crack growth rate of E36 ship plate steel was higher than that of EH36 and FH36 ship plate steel due to its higher carbon content and obviously smaller grain size. The analysis results and data may provide a necessary experimental basis for quantitatively establishing the relationship between fracture toughness, yield strength and impact toughness, as well as the relationship between fatigue crack growth rate and both strength and fracture toughness.