Predicting fatigue damage in composites: A Bayesian framework

Abstract

Modeling the progression of damage in composites materials is a challenge mainly due to the uncertainty in the multi-scale physics of the damage process and the large variability in behavior that is observed, even for tests of nominally identical specimens. As a result, there is much uncertainty related to the choice of the class of models among a set of possible candidates for predicting damage behavior. In this paper, a Bayesian prediction approach is presented to give a general way to incorporate modeling uncertainties for inference about the damage process. The overall procedure is demonstrated by an example with test data consisting of the evolution of damage in glass-fiber composite coupons subject to tensiontension fatigue loads. Results are presented for the posterior information about the model parameters together with the uncertainty associated with the model choice from a set of plausible fatigue models. This approach confers an efficient way to make inference for damage evolution using an optimum set of model parameters and, in general, to treat cumulative damage processes in composites in a robust sense.

Robust system identification and model predictions in the presence of systematic uncertainty

“…Bayesian model class selection has been widely used in many applications [6,14,30,44] for identifying the optimal model class among possible model classes and for obtaining the predictive distribution by posterior model averaging [12,13]. More related to modelingerror assumptions, Simoen et al [40] proposed the use of Bayesian model class selection to determine an adequate correlation model of the prediction-error uncertainty.…”

Reliability analysis and prediction for time to failure distribution of an automobile crankshaft

“…This suggested method uses the Markov process which is a modification of the previously developed Bayesian framework for fatigue damage prediction [11]. The stochastic process is applied in two levels: first, to generate synthetical data that mirrors the actual time to failure data using the failure probabilitic criterion through the Markov process; second, to assess the reliability of the component based on each of the parameteric distribution class within a set of the synthetically generated data.…”

Predicting Fatigue Damage of Composites Using Strength Degradation and Cumulative Damage Model

“…These models can be classified in the following categories [1]: fatigue life concepts [2,3], phenomenological models with stiffness [4,5], strength degradation models [6,7], continuum damage mechanics (CDM) based models [8][9][10] and micromechanics models [11,12] and uncertainty and Bayesian based probabilistic models [13][14][15].…”

“…One of the new category of approaches to model fatigue in composites are models based on uncertainty and Bayesian based probabilistic framework [13][14][15]. Most of the probabilistic models are based on quantifying model uncertainties for different fatigue models.…”

“…Most of the probabilistic models are based on quantifying model uncertainties for different fatigue models. A progressive damage in composite material using a full Bayesian prediction and updating framework is presented by Chiachío et al [13]. They used a cumulative damage model based on the theory of Markov chains to infer a complete damage progression.…”