A Brief Review of Multiaxial High-Cycle Fatigue

Abstract

In this article, three classes of criteria (stress-strain, energy, and critical plane) used for life estimation in multiaxial high-cycle fatigue are discussed with emphasis on nonproportional loading, and the merits or demerits of each fatigue criterion are indicated. This paper also reviews mechanisms of multiaxial high-cycle fatigue, especially under nonproportional loading, and introduces studies in China on micromechanisms in high-cycle fatigue under multiaxial loading. Suggestions for further work are proposed in the conclusions.

A survey on fatigue life analysis approaches for metallic notched components under multi-axial loading

“…You and Lee 1 reviewed the multi-axial fatigue methods for smooth specimens which were proposed between 1980 and 1995 and classified them into five kinds of theories. Ding et al 2 briefly reviewed the multi-axial high-cycle fatigue criteria for smooth specimens as well as the mechanisms of multi-axial high-cycle fatigue, and further classified the multi-axial high-cycle fatigue criteria into three kinds of theories: stress-strain, energy and critical plane. Wang 3 divided the fatigue damage parameters for smooth specimens into three groups: stress criteria, strain criteria and energy criteria, and examined the predictive capabilities of these three kinds of damage parameters with the experimental data of six materials under both proportional and non-proportional loadings.…”

A new paradigm for fatigue analysis - evolution equation based continuum approach

“…For complex load histories and general stress states these kind of aproaches become increasingly speculative. Reviews and comparisons of different high-cycle fatigue models can be found in studies by [15], [2], [9], and [11].…”

A Machine Learning Model for Predicting Progressive Crack Extension based on DCPD Fatigue Data

“…Temperature, loading conditions, and environment on top of microstructure influence the material performance [5]. While there has been steady progress over the last century on characterizing low-cycle fatigue properties, the ability to fully predict high-temperature fatigue is still in its infancy [6][7][8][9]. One tool that is gaining traction is the application of machine learning (ML) techniques [10][11][12][13][14].…”