Abstract
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.
Highlights
Ti6Al4V is known for its high specific strength, fracture toughness, high temperature mechanical properties, and excellent corrosion resistance properties
Luo et al [18] have adopted a machine learning approach to predict the fatigue life of IN718 alloy influenced by pores, which revealed that increase in the size and the number of pores and decrease in the distance from the center of the pore to the surface of the specimen may promote fatigue crack initiation and could decrease the fatigue life
Fatigue crack growth rate behaviour of Ti6Al4V alloy fabricated by LPBF with respect to 9 different specimen conditions was analyzed by using machine learning techniques
Summary
Ti6Al4V is known for its high specific strength, fracture toughness, high temperature mechanical properties, and excellent corrosion resistance properties. Thijs et al [23] investigated the microstructural evaluation during laser powder bed fusion of Ti6Al4V and reported that the direction of the elongated grains depends on the local heat transfer condition, which is determined by the scanning strategy and is the independent variable for this current study These microstructural features ought to be optimized for fatigue design and the manufacture of components using additive manufacturing technology. Feature importance analysis provides the rankings of the independent variables based on their importance This analysis showed that SIF is the most influencing parameter followed by post processing technique and built orientation for the FCGR behavior of Ti64 alloy fabricated using LPBF
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