Applications of data-driven approaches in prediction of fatigue and fracture

Abstract

Since various mechanical components could undergo cyclic loadings in their operations, fatigue evaluation of the parts is a crucial issue. In this context, different experimental practices have been developed to investigate fatigue strength, crack growth rate, and fracture behavior of mechanical parts. Along with conventional fatigue test methods, data-driven approaches can be used to investigate fatigue life and fracture behavior of metallic and non-metallic structural elements. A data-driven analysis is a technique which uses collected data to answer the particular demand. In this contribution, we have reviewed data-driven approaches which have been applied to predict fatigue life and fracture of components. To this aim, we discuss the documented results in applied data-driven techniques in evaluating fatigue performance of metallic, composite, and 3D-printed parts. The material, fabrication process, geometric parameters, and details of data-driven approaches have been considered in review of the reported literature. Although improvements are recorded due to using data-driven approaches in prediction of fatigue behavior of parts, there are challenges and limitations which have been highlighted in the present study. Moreover, this review explains the key parameters which play crucial roles in fatigue life of metallic and non-metallic components.