A random forest‐based fatigue damage classification framework using global loading and displacement information

Abstract

The article presents a random forest‐based damage classification methodology built upon the force and displacement data obtained via fatigue testing of Al7075‐T6 specimens. Four features, namely, average displacement (D), material stiffness (ST), energy dissipation rate (EDR), and cumulative energy dissipation (CED), are defined from the collected data. These features are classified into healthy and cracked states based on the information obtained from a high‐resolution confocal microscope and a digital microscope. Random forest‐based classifiers are, thereafter, trained and tested using the individual as well as different combinations of the four features. The results reveal that, when used individually, CED shows the best accuracy of 80% in detecting a fatigue crack. When two or more pairs of features are used, the accuracy increases significantly to beyond 95%. CED is observed to be the most important feature for developing efficient classifiers for fatigue crack detection. The results demonstrate that the random forest classifier is a viable option to accurately detect fatigue damage based on the four features using only global information.