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

Metals ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 50
Author(s):  
Nithin Konda ◽  
Raviraj Verma ◽  
Rengaswamy Jayaganthan

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.

1985 ◽  
Vol 21 (2) ◽  
pp. 130-133
Author(s):  
V. I. Pokhmurskii ◽  
A. S. Zubchenko ◽  
A. A. Popov ◽  
I. P. Gnyp ◽  
V. M. Timonin ◽  
...  

1969 ◽  
Vol 11 (3) ◽  
pp. 343-349 ◽  
Author(s):  
L. P. Pook

Some fatigue crack growth data have been obtained for age-hardened beryllium copper. The fatigue crack growth rate was found to be very dependent on the hardness and tensile mean stress. This dependence is believed to be associated with the intense residual stresses surrounding Preston-Guinier zones.


2021 ◽  
Author(s):  
Aaron Dinovitzer ◽  
Sanjay Tiku ◽  
Morvarid Ghovanlou ◽  
Mark Piazza ◽  
Thomas Jones

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