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

TL;DR

This study compares random forest and neural network models for predicting fatigue crack growth rates in nickel superalloys, demonstrating high accuracy with r^2 values above 0.96.

Contribution

It introduces a comparative analysis of machine learning frameworks for fatigue crack growth prediction in aerospace materials.

Findings

Both models achieved high prediction accuracy (r^2 > 0.96).

Neural network slightly outperformed random forest in accuracy.

Machine learning models are effective for fatigue crack growth prediction.

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.