Fuzzy Inference System for Fatigue Parameters Prediction in Metals: from Strength to Fatigue

In order to enable engineers to make informed decisions about material selection, design, and maintenance, contributing to the safety, reliability, and longevity of components and structures, subjected to cyclic (fatigue) loading, it is essential to have knowledge of the threshold value of the stress intensity factor range and the range of plain fatigue limit of a material. Traditional experimental approaches, although offering an accurate determination of these parameters, are, however, expensive and time-consuming. It is thus evident, that there is a need for an alternative methodology, offering accurate and reliable on one hand, but fast and cheap on the other, predictions of aforementioned parameters. The main focus of this chapter is to analyse the ability of the data driven fuzzy inference system (FIS) approach to serve this goal and predict fatigue parameters of a material, knowing material's strength (static) characteristics. Results, reported in this chapter, for aluminium alloys and different steels data sets demonstrate capabilities of FIS to estimate, with the high degree of accuracy, the range of the plain fatigue limit and the range of the threshold value of the stress intensity factor, based on provided ultimate tensile strength and yield strength of a metal.