Comparison of Random Forest and Neural Network Framework for Prediction of Fatigue Crack Growth Rate in Nickel Superalloys

Author:

Raghunandan Pratoori

Keyword:

Condensed Matter, Disordered Systems and Neural Networks, Disordered Systems and Neural Networks (cond-mat.dis-nn)

journal:

date:

2023-09-23 16:00:00

Abstract

The rate of fatigue crack growth in Nickle superalloys is a critical factor of safety in the aerospace industry. A machine learning approach is chosen to predict the fatigue crack growth rate as a function of the material composition, material properties and environmental conditions. Random forests and neural network frameworks are used to develop two different models and compare the two results. Both the frameworks give good predictions with $r^2$ of 0.9687 for random forest and 0.9831 for neural network.

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