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

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