Probabilistic prediction of minimum fatigue life behaviour in α + β titanium alloys

Abstract

The objective of this work was to develop and demonstrate a probabilistic life prediction method for the prediction of minimum fatigue lives that are typically used in the design of fracture critical rotating turbine engine components. A Monte Carlo analysis was used to predict the variability in fatigue lives based on the distribution of microstructural features that lead to early crack initiation as well as the variability in small fatigue crack growth rates. Two titanium alloys, both with bimodal microstructures, were tested and analysed in this study. The distribution of critical microstructural features was calibrated based on test results and understanding of microstructure neighbourhood effects. Testing was conducted on both alloys and included both smooth and notched specimens. The predictions are presented and compared with the data for smooth and notch geometries for the various loading conditions. A parametric study was performed to identify the importance of several model inputs and to identify areas for future improvement.

Improving Support Vector Regression for Predicting Mechanical Properties in Low-Alloy Steel and Comparative Analysis

“…In response to these issues, scientists have proposed various predictive methods, including empirical formulations [1,2], linear regression analysis [3,4], finite element analysis (FEA) [5], and Monte Carlo methods [6][7][8][9]. These methods are predominantly empirical and statistical in nature.…”

A microstructure‐based homogenization model for predicting the low‐cycle fatigue initiation life of GH4169 superalloy

“…With experiment measured microstructure parameters and statistical approaches, phenomenological models have been used to model the effect of microstructure on the mean and worst-case fatigue life. [13][14][15] In recent decades, with better understanding of the fatigue crack initiation behavior from highly detailed observations in experiments, 7,16,17 finite element method (FEM)-based computational approaches for the accurate prediction of fatigue initiation life and its variability have been developed. In those approaches, a grain or part of a grain is treated as an element, and the constitutive response is described by the crystal plastic model (CPM) at integration points.…”

“…Microstructure influences on the LCF initiation life have been considered in previous works by introducing microstructure parameters as grain size 8 , 12 and inclusion size 13 into the equation coefficient of the phenomenological models. With experiment measured microstructure parameters and statistical approaches, phenomenological models have been used to model the effect of microstructure on the mean and worst‐case fatigue life 13–15 . In recent decades, with better understanding of the fatigue crack initiation behavior from highly detailed observations in experiments, 7,16,17 finite element method (FEM)‐based computational approaches for the accurate prediction of fatigue initiation life and its variability have been developed.…”

A Stochastic Model for Crack Initiation Life Prediction of an Austenitic Stainless Steel Under Constant Amplitude Loading

“…Hence, crack initiation can occupy a significant part of the fatigue lifetime (Zhixue 2001, Provan et al 1991, where microcracks nucleate and develop collectively in the so-called short crack regime to form the dominant crack (Qiao et al 2005). The growth of such cracks is largely affected by the material microstructure (Miller 1987, Plumtree et al 1991, Lukáš et al 2003, and a good comprehension of the mechanisms governing their development is crucial to predict the formation of the dominant crack (Golden et al 2019).…”