Reduction of testing effort for fatigue tests: Application of Bayesian optimal experimental design

Abstract

S-N curve parameter determination is a time- and cost-intensive procedure. A standardized method for simultaneously determining all S-N curve parameters with minimum testing effort is still missing. The Bayesian optimal experimental design (BOED) approach can reduce testing effort and accelerates uncertainty reduction during fatigue testing for S-N curve parameters. The concept is applicable to all S-N curve models and is exemplary illustrated for a bilinear S-N curve model. We demonstrate the fatigue testing workflow for the bilinear S-N curve in detail while discussing steps and challenges when generalizing to other S-N curve models. Applying the BOED to the bilinear S-N curve models, minor errors and uncertainties for all S-N curve parameters are obtained after only 10 experiments for data scatter values below 1.1. For such, the relative error in fatigue limit estimation was less than 1% after five tests. When S-N data scatter higher than 1.2 is concerned, 17 tests were required for robust analysis. The BOED methodology should be applied to other S-N curve models in the future. The high computational effort and the approximation of the posterior distribution with a normal distribution are the limitations of the presented BOED approach.