A Probabilistic Framework for Minimum Low Cycle Fatigue Life Prediction

Abstract

Abstract The traditional approach to low-cycle fatigue (LCF) life prediction involves statistical characterization of total LCF life based on extensive testing of smooth fatigue specimens under multiple stress and temperature conditions. Total LCF life is modeled as a single random variable with a unimodal probability density function (PDF) from which a minimum (e.g., B0.1) life is derived. Recent studies have shown that LCF lives for some materials consist of a short-life group that initiates cracks near the first cycle of loading and a long-life group that forms cracks later in life. The combined lives of these two groups can be modeled as a bimodal distribution. Minimum LCF lives associated with the bimodal PDF are typically longer than those associated with the traditional unimodal PDF. Minimum LCF lives of the bimodal distribution are dominated by the short-life group, and the lifetimes of this group can be estimated using probabilistic damage tolerance (PDT) concepts. In this paper, a probabilistic framework is presented for prediction of minimum LCF lives of the short-life group. It extends a previously developed minimum LCF life model for smooth fatigue specimens for application to full components. It is based on a probabilistic damage tolerance methodology that was previously developed for rare material anomalies in aircraft gas turbine engine materials. The framework is demonstrated via two illustrative examples including a representative gas turbine engine component. The results promote improved understanding of the PDT approach and its application to LCF life prediction.