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

Department of Engineering Design, Additive Manufacturing Group & Centre of Excellence for Materials and Manufacturing for Futuristic Mobility, Indian Institute of Technology Madras, Chennai 600036, India
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Academic Editor: Yves Nadot
Metals 2022, 12(1), 50; https://doi.org/10.3390/met12010050
Received: 22 October 2021 / Revised: 26 November 2021 / Accepted: 1 December 2021 / Published: 27 December 2021
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. View Full-Text
Keywords: titanium alloy; additive manufacturing; machine learning; fatigue crack growth rate titanium alloy; additive manufacturing; machine learning; fatigue crack growth rate
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MDPI and ACS Style

Konda, N.; Verma, R.; Jayaganthan, R. Machine Learning Based Predictions of Fatigue Crack Growth Rate of Additively Manufactured Ti6Al4V. Metals 2022, 12, 50. https://doi.org/10.3390/met12010050

AMA Style

Konda N, Verma R, Jayaganthan R. Machine Learning Based Predictions of Fatigue Crack Growth Rate of Additively Manufactured Ti6Al4V. Metals. 2022; 12(1):50. https://doi.org/10.3390/met12010050

Chicago/Turabian Style

Konda, Nithin, Raviraj Verma, and Rengaswamy Jayaganthan. 2022. "Machine Learning Based Predictions of Fatigue Crack Growth Rate of Additively Manufactured Ti6Al4V" Metals 12, no. 1: 50. https://doi.org/10.3390/met12010050

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