FATIGUE CRACK GROWTH - THE COMPLICATIONS.

A vast body of experimental data has been accumulated on the constant amplitude crack growth response of structural metals in moist laboratory air. Usually the data is presented as plots of crack growth rate, da/dN, against stress intensity range, DELTA K. In order to extrapolate this data to fatigue crack growth in more active or more inert environments, to crack growth under variable amplitude loading, or to crack growth under multi-axial or mixed mode loading, the mechanisms of crack advance and crack closure should be considered. This paper briefly reviews the crack closure phenomenon and discusses the dominant causes of accelerated and retarded growth under changes in environment or type of loading. It is argued that simple constant amplitude data is often surprisingly accurate when used to predict crack growth in more complex situations. However, there are some cases where constant amplitude data lead to dangerously non-conservative predictions of fatigue life.

A discrete dislocation analysis of fatigue crack growth

Cyclic loading of a plane strain mode I crack under small scale yielding is analyzed using discrete dislocation dynamics. The dislocations are all of edge character, and are modeled as line singularities in an elastic solid. At each stage of loading, superposition is used to represent the solution in terms of solutions for edge dislocations in a half-space and a non-singular complementary solution that enforces the boundary conditions, which is obtained from a linear elastic, finite element solution. The lattice resistance to dislocation motion, dislocation nucleation, dislocation interaction with obstacles and dislocation annihilation are incorporated into the formulation through a set of constitutive rules. An irreversible relation between the opening traction and the displacement jump across a cohesive surface ahead of the initial crack tip is also specified, which permits crack growth to emerge naturally. It is found that crack growth can occur under cyclic loading conditions even when the peak stress intensity factor is smaller than the stress intensity required for crack growth under monotonic loading conditions; however below a certain threshold value of ΔKI no crack growth was seen.

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.

Cognitive Tomography Reveals Complex, Task-Independent Mental Representations

Humans develop rich mental representations that guide their behavior in a variety of everyday tasks. However, it is unknown whether these representations, often formalized as priors in Bayesian inference, are specific for each task or subserve multiple tasks. Current approaches cannot distinguish between these two possibilities because they cannot extract comparable representations across different tasks [1-10]. Here, we develop a novel method, termed cognitive tomography, that can extract complex, multidimensional priors across tasks. We apply this method to human judgments in two qualitatively different tasks, "familiarity" and "odd one out," involving an ecologically relevant set of stimuli, human faces. We show that priors over faces are structurally complex and vary dramatically across subjects, but are invariant across the tasks within each subject. The priors we extract from each task allow us to predict with high precision the behavior of subjects for novel stimuli both in the same task as well as in the other task. Our results provide the first evidence for a single high-dimensional structured representation of a naturalistic stimulus set that guides behavior in multiple tasks. Moreover, the representations estimated by cognitive tomography can provide independent, behavior-based regressors for elucidating the neural correlates of complex naturalistic priors. © 2013 The Authors.

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.