Self-organized criticality as a precursor of fatigue: application to shape memory alloys

TL;DR

This paper investigates fatigue failure in shape memory alloys by analyzing acoustic emission avalanches and their critical exponents, applying self-organized criticality models to predict failure with high reliability.

Contribution

It introduces a novel approach using critical rupture models from earthquake and stock market theories to forecast fatigue failure in materials.

Findings

Critical exponents evolve with cyclic loading.

Acoustic emission avalanches follow power-law distributions.

Failure can be reliably predicted using self-organized criticality models.

Abstract

Fatigue failure can be thought by studying the collective motions of defects inside materials instead of focusing on the growth of a pre-existing micro-crack. An experimental study of the statistical distribution of acoustic emissions avalanches along cycling is presented. The evolutions of critical exponents through cyclic driving are estimated to track changes in the dissipation modes and consequently identify fatigue failure precursors. We also use critical rupture models developed for earthquakes and stock market crashes predictions to forecast the time to failure with good reliability.