Modeling metallic fatigue data using the Birnbaum--Saunders distribution

TL;DR

This paper demonstrates that the Birnbaum--Saunders distribution more accurately models metallic fatigue life data than the normal distribution, improving predictions of fatigue life and survival probability across different datasets.

Contribution

It introduces the use of the Birnbaum--Saunders distribution for metallic fatigue modeling and proposes a new equivalent stress definition considering experiment type.

Findings

Birnbaum--Saunders outperforms normal distribution in fitting fatigue data.

The model provides more accurate fatigue life predictions.

A new stress measure accounts for experiment type effects.

Abstract

This work employs the Birnbaum--Saunders distribution to model the fatigue life of metallic materials under cyclic loading and compares it with the normal distribution. Fatigue-limit models are fitted to three datasets of unnotched specimens of 75S-T6 aluminum alloys and carbon laminate with different loading types. A new equivalent stress definition that accounts for the effect of the experiment type is proposed. The results show that the Birnbaum--Saunders distribution consistently outperforms the normal distribution in fitting the fatigue data and provides more accurate predictions of fatigue life and survival probability.