The effect of mean stress is a significant factor in design for fatigue, especially under high cycle service conditions. The incorporation of mean stress effect in random loading fatigue problems using the frequency domain method is still a challenge. The problem is due to the fact that all cycle by cycle mean stress effects are aggregated during the Fourier transform process into a single zero frequency content. Artificial neural network (ANN) has great scope for non-linear generalization. This paper presents artificial neural network methods for including the effect of mean stress in the frequency domain approach for predicting fatigue damage. The materials considered in this work are metallic alloys. The results obtained present the ANN method as a viable approach to make fatigue damage predictions including the effect of mean stress. Greater resolution was obtained with the ANN method than with other available methods.

The use of artificial intelligence especially based on artificial neural networks (ANN) is now prevalent in many fields of data analysis and interpretation. There have been a number of papers published in the literature on the use of ANN for fatigue characterisation. Most of these have however been developed for rather focussed application with limited capability for fatigue life prediction for a broad scope of material and loading conditions. The authors recently presented a uniquely generalised ANN model that is capable of making fatigue life prediction for a broad range of material fatigue properties and loading spectral forms. The model was developed using simulated data albeit subject to conceivable constraints between possible materials properties and load forms. This paper presents a validation of the ANN model using a Society of Automotive Engineers (SAE) random fatigue loading experimental test data. The capabilities and potentials of the model are demonstrated by comparison with the SAE random load fatigue test results and with results obtained from other predictive methods. The performance of the ANN is highly encouraging as a general tool for random loading fatigue analysis.

No abstract

High stress gradients occur at metal-to-ceramic joints due to the different thermal and mechanical properties of the materials. In some cases, the magnitude of the highly localized stresses lead to failure thus compromising the structural integrity of such joints. The study of notched ceramic bars with high stress gradients can assist with the prediction of failure of metal ceramic joints. Experiments and fracture mechanics analysis were performed on notched and un-notched POCO E.D.M 3 graphite and AS800 Silicon Nitride bars with different notch parameters. The twoparameter, multi-axial Weibull statistics method and a brittle fracture criterion based on the average stress over an area approach were used to predict the failure of the bars and the results obtained were compared with experimental results. The brittle failure criterion appears to give much better correlation with experimental results than the multi-axial Weibull statistics approach. The findings also appear to highlight the limitations of the Weibull’s statistics method in cases involving very high stress gradients.

Piston is one of the most important components in an internal combustion engine which transfers combustion energy to the crankshaft via a connecting rod. Increase in an engine’s efficiency has somehow necessitated improvement in the piston. This improvement can be achieved by better piston design or using material with superior mechanical properties. Engineers have experimented with different materials for pistons since the introduction of internal combustion engines. This paper reviews the evolution of materials for pistons since the beginning of automotive industry to present day and analyses the properties that attracted engineers to use these materials. The paper also focuses on newly developed materials that have the potentials to replace current piston materials and the work that is taking place. The current trend of changing from diesel to petrol in small internal combustion engines and the affect this will have on piston materials has been analysed. Keywords: Aluminium, Combustion Engine, Nanostructured, Piston Material, Piston.