Modeling fatigue crack growth in crystalline solids with discrete dislocation plasticity

Analyses of crack growth under cyclic loading conditions are discussed where plastic flow arises from the motion of large numbers of discrete dislocations and the fracture properties are embedded in a cohesive surface constitutive relation. The formulation is the same as used to analyse crack growth under monotonic loading conditions, differing only in the remote loading being a cyclic function of time. Fatigue, i.e. crack growth in cyclic loading at a driving force for which the crack would have arrested under monotonic loading, emerges in the simulations as a consequence of the evolution of internal stresses associated with the irreversibility of the dislocation motion. A fatigue threshold, Paris law behaviour, striations, the accelerated growth of short cracks and the scaling with material properties are outcomes of the calculations. Results for single crystals and polycrystals will be discussed.

An example of the use of neural computing techniques in materials science - The modelling of fatigue thresholds in Ni-base superalloys

Two adaptive numerical modelling techniques have been applied to prediction of fatigue thresholds in Ni-base superalloys. A Bayesian neural network and a neurofuzzy network have been compared, both of which have the ability to automatically adjust the network's complexity to the current dataset. In both cases, despite inevitable data restrictions, threshold values have been modelled with some degree of success. However, it is argued in this paper that the neurofuzzy modelling approach offers real benefits over the use of a classical neural network as the mathematical complexity of the relationships can be restricted to allow for the paucity of data, and the linguistic fuzzy rules produced allow assessment of the model without extensive interrogation and examination using a hypothetical dataset. The additive neurofuzzy network structure means that redundant inputs can be excluded from the model and simple sub-networks produced which represent global output trends. Both of these aspects are important for final verification and validation of the information extracted from the numerical data. In some situations neurofuzzy networks may require less data to produce a stable solution, and may be easier to verify in the light of existing physical understanding because of the production of transparent linguistic rules. © 1999 Elsevier Science S.A.

Fatigue life and self-induced volumetric changes of CARDIFRC

A class of ultra-high-performance fibre-reinforced cementitious composites (UHPFRCC) has been developed at Cardiff university and registered under the trade name CARDIFRC. The method of its production and its mechanical and fracture properties were reported previously in a series of papers in Magazine of Concrete Research. Here the results of recent fatigue and shrinkage tests on this material are reported. As with the mechanical and fracture properties, it is shown that an even and uniform distribution of fibres throughout the bulk of the material is crucial to its superior fatigue performance and to the reduction in the shrinkage strains. © 2010 Thomas Telford Ltd.

Polarization switching induced decrease of bulk resistivity in ferroelectric Pb(Zr0.45Ti0.55)O3 thin films and a method to improve their fatigue endurance

Significant reduction of the bulk resistivity in a ferroelectric Pb(Zr 0.45Ti0.55)O3 thin film is observed before the remnant polarization started to decrease noticeably at the onset of its fatigue switching process. It is associated with the increase of charge carriers within the central bulk region of the film. The decrease of bulk resistivity would result in the increase of Joule heating effect, improving the temperature of the thin film, which is evaluated by the heat conduction analysis. The Joule heating effect in turn accelerates the polarization reduction, i.e. fatigue. Enhancing the heat dissipation of a ferroelectric capacitor is shown to be able to improve the device's fatigue endurance effectively. © 2013 Chinese Physical Society and IOP Publishing Ltd.