Session: 28-02 Lifing Applications using Probabilistic Methods

Paper Number: 80220

80220 - A Probabilistic Framework for Minimum Low Cycle Fatigue Life Prediction 

The traditional approach to low cycle fatigue (LCF) life prediction involves extensive testing under multiple stress and temperature conditions and subsequent statistical fitting of the resulting strain-life data. This approach typically provides relatively few failure data at each test condition, and so the LCF life (i.e., number of cycles to failure) is often modeled as a single random variable.  The target minimum LCF life (i.e., cycles to failure) for a specified probability of occurrence (e.g., B.1 life) can be computed directly using the probability density function (PDF) of the cycles to failure random variable.

Recent studies have demonstrated that LCF lives in some materials can be classified into two distinct groups: (1) short-life, and (2) long-life.  The short life group consists of specimens that experience micro-cracking on the first cycle of loading.  The micro-crack propagates to form a macro-crack, leading to early failure of the specimen.  The long-life group includes specimens that form propagating micro-cracks well after the first load cycle (often 10³ -10⁶ cycles or more).  When the two groups are modeled as separate populations (i.e., two distinct random variables), the PDF of the combined population has a bimodal shape with less scatter compared to the unimodal PDF associated with the traditional approach.  Minimum LCF lives associated with the bimodal PDF are typically much longer than minimum LCF lives associated with the unimodal PDF.  Furthermore, the minimum LCF lives of the bimodal distribution are dominated by the short-life group random variable, and lifetimes of this group can be estimated using probabilistic damage tolerance concepts.

In this paper, a probabilistic framework is presented for prediction of minimum LCF lives.  It is an extension of a probabilistic damage tolerance methodology that was developed for rare material anomalies in aircraft gas turbine engine materials.  The framework focuses on life prediction of the portion of the population that experiences micro-cracking on the first cycle of loading (i.e., the short-life group), and provides treatment for propagation of both physically small and large cracks and associated fatigue crack growth rate scatter.  The framework is demonstrated at both the coupon and component level where a zone-based approach is used to simulate the occurrence of micro-cracks at multiple locations within the geometries. The results enable improved quantitative minimum LCF life estimates of aircraft engine materials.

Presenting Author: Michael Enright Southwest Research Institute

Presenting Author Biography: Dr. Michael Enright specializes in reliability-based life prediction with an emphasis on probabilistic fatigue and fracture. He has published over 100 peer-reviewed journal articles and conference papers (including four award winning articles) and organized a number of international conferences focused on reliability-based life prediction of gas turbine engine materials. Dr. Enright is currently responsible for development of the DARWIN probabilistic fracture mechanics software. He also serves as Adjunct Professor at the University of Texas and Trinity University in San Antonio.

Authors:

Michael Enright Southwest Research Institute
Craig Mcclung Southwest Research Institute
Jonathan Moody Southwest Research Institute
James Sobotka Southwest Research Institute
Yasin Zaman Southwest Research Institute