Microstructure-sensitive critical plastic strain energy density criterion for fatigue life prediction across various loading regimes

Abstract

<jats:p>In the present work, we postulate that a critical value of the stored plastic strain energy density (SPSED) is associated with fatigue failure in metals and is independent of the applied load. Unlike the classical approach of estimating the (homogenized) SPSED as the cumulative area enclosed within the macroscopic stress–strain hysteresis loops, we use crystal plasticity finite element simulations to compute the (local) SPSED at each material point within polycrystalline aggregates of a nickel-based superalloy. A Bayesian inference method is used to calibrate the critical SPSED, which is subsequently used to predict fatigue lives at nine different strain ranges, including strain ratios of 0.05 and −1, using nine statistically equivalent microstructures. For each strain range, the predicted lives from all simulated microstructures follow a lognormal distribution. Moreover, for a given strain ratio, the predicted scatter is seen to be increasing with decreasing strain amplitude; this is indicative of the scatter observed in the fatigue experiments. Finally, the lognormal mean lives at each strain range are in good agreement with the experimental evidence. Since the critical SPSED captures the experimental data with reasonable accuracy across various loading regimes, it is hypothesized to be a material property and sufficient to predict the fatigue life.</jats:p>

Microstructure-sensitive estimation of fatigue life using cyclic thermodynamic entropy as an index for metals

“…Research reveals that in Phase II of the temperature evolution the average value of PSE per cycle is fairly constant and remains stable for most of the specimen's fatigue life [15,37,38]. In Phase III, which typically accounts for much less than 10 percent of the fatigue life, macro-cracks are formed, and temperature increases rapidly due to the associated high dissipative energy [20].…”

“…Rich volumes of literature offer different theoretical [7][8][9] and experimental [6,10] approaches to predict fatigue life, among which microstructure-sensitive and thermodynamically-based models are becoming widespread [11][12][13]. Importantly, recent crystal-plasticity models show a strong correlation between energy dissipation and crystalline deformation and call for further development of models that can effectively treat microstructure-sensitive materials [14,15]. Deterministic or probabilistic microstructure-sensitive approaches have been developed to gain insight into the true nature of metals at micro-scale.…”

“…Deterministic or probabilistic microstructure-sensitive approaches have been developed to gain insight into the true nature of metals at micro-scale. Notable contributions include the work of Bandyopadhyay et al [15] who used crystal plasticity finite element (CPFEM) to calculate the locally-stored microplastic strain energy density as the sum of the contribution of all the slip systems. Dislocation glide was the primary mechanism incorporated into the crystal plasticity model, and it was postulated that the accumulation of the strain energy until failure is a material property, independent of the load amplitude.…”

A Review of the Failure and Damage Forms of Metals under Cyclic Loading

“…Accordingly, the micro-energy dissipation is calculated for nickel-based super alloy and used to obtain fatigue life. Locally-stored plastic strain energy of 718Plus Nickle-based was obtained using crystal plasticity finite element (CPFEM) simulations by Sangid et al [22]. A Bayesian framework was used to calibrate the critical plastic strain energy density.…”

Machine Learning-Enabled Uncertainty Quantification for Modeling Structure–Property Linkages for Fatigue Critical Engineering Alloys Using an ICME Workflow

“…Recent advances in UQ for fatigue modeling [8,9] have established the use of Bayesian and genetic algorithm techniques for crystal plasticity model parameter and model form UQ. Additionally, Bayesian inference and Taylor expansion-based uncertainty propagation methods have been used to propagate uncertainties associated with model reduction error, data sparsity error, and microstructural uncertainty using a crystal plasticity finite element modeling (CPFEM) model for dual-phase alpha-beta-titanium alloys [10,11].…”