Bayesian Optimised Collection Strategies for Fatigue Strength Testing

TL;DR

This paper introduces Bayesian optimized strategies for fatigue strength testing that improve data collection efficiency and accuracy, reducing prior knowledge requirements and experimental effort.

Contribution

It presents novel Bayesian sampling protocols for fatigue testing, enhancing parameter estimation and experimental flexibility over traditional methods.

Findings

Bayesian Staircase method improves parameter estimation efficiency.

Bayesian Stress Step method maintains accuracy with larger step sizes.

Protocols are validated through laboratory experiments.

Abstract

A statistical framework is presented enabling optimal sampling and analysis of constant life fatigue data. Protocols using Bayesian maximum entropy sampling are built based on conventional staircase and stress step methods, reducing the requirement of prior knowledge for data collection. The Bayesian Staircase method shows improved parameter estimation efficiency, and the Bayesian Stress Step method shows equal accuracy to the standard method at larger step size allowing experimentalists to lessen concerns of loading history. Statistical methods for determining model suitability are shown, highlighting the influence of protocol. Experimental validation is performed, showing the applicability of the methods in laboratory testing.