Bayesian optimized collection strategies for fatigue strength testing. Issue 1 (8th October 2022)

• Record Type: Journal Article
• Authors: Magazzeni, Christopher Massimo; Rose, Rory; Gearhart, Chris; Gong, Jicheng; Wilkinson, Angus J.
• Is Part Of: Fatigue & fracture of engineering materials & structures. Volume 46:Issue 1(2023)
• Page Start: 228
• Page End: 243
• Publication Date: 2022-10-08
• Subjects: fatigue -- high cycle fatigue -- optimum design -- statistical model -- statistics of extremes; Materials -- Fatigue -- Periodicals; Fracture mechanics -- Periodicals; 620.1123
• DOI: 10.1111/ffe.13859
• Languages: English
• ISSNs: 8756-758X

Abstract

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. Highlights: We present anopen‐source Bayesian analysis for fatigue data. Bayesian maximum entropy sampling is used in novel protocols for fatigue testing. Better efficiency in measuring fatigue behavior was demonstrated. Methods were verified through mesoscale ultrasonic experiments.