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Data-driven Based Fracture Prediction of Notched Components by Hossein Talebi; Bahador Bahrami; Mohammad Daneshfar; Sara Bagherifard; Majid R. Ayatollahi is a Physics and Astronomy article available to read on EtoBox.

What is Data-driven Based Fracture Prediction of Notched Components about?

A data-driven approach is developed to predict the fracture load of a notched component. To do so, more than 1500 fracture tests (507 unique experimental data points) on mixed-mode I/II loading of notched brittle samples were collected from the literature. After pre-processing the raw data, six features of maximum tangential stress ( σ θ θ max ) , maximum tangential stress angle ( θ σ θ θ max ) , ultimate tensile strength ( σ u ) , fracture toughness ( K I c ) , notch opening angle ( 2 α ) and notch tip radius ( ρ ) were selected by using the neighbourhood component analysis (NCA) technique. To predict the fracture load of various types of notched samples, several machine learning (ML) models were trained using the methods of Gaussian process regression (GPR), decision tree ensemble and artificial neural network (ANN). Then, the Bayesian optimization algorithm was applied to find the optimum hyperparameters for each model. Lastly, the performance of the models in predicting fracture load was evaluated against 124 unseen data points. The results revealed the high potential of data-driven methods for assessing the fracture load of notched brittle components with acceptable precisions

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Author

Hossein Talebi; Bahador Bahrami; Mohammad Daneshfar; Sara Bagherifard; Majid R. Ayatollahi

Publisher

The Royal Society

Published

2023

Field

Physics and Astronomy (Physical Sciences)