Griinke, Tyler John (2013) Development of an artificial neural network (ANN) for predicting tribological properties of kenaf fibre reinforced epoxy composites (KFRE). [USQ Project]
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Abstract
Study in the field of tribology has developed over time within the mechanical engineering discipline and is an important aspect of material selection for new
component design. Most of these components experience failure due to this form of loading. It has been well established that there are several conditions or parameters that may influence the tribological performance of a material. Good correlations with experimental results are not clearly obtained or achieved from mathematical models.
Artificial neural network (ANN) technology is recognised as an effective tool to accurately predict material tribological performance in relation to these influencing
parameters. The benefit and importance is the ANN models capability to predict solutions by being trained with experimental data. They essentially catalogue the performance characteristics eliminating the need to refer to tables and the requirement for additional time consuming testing. This will aid in continuing research, development
and implementation of fibre composites.
The aim of the project was to investigate artificial neural network (ANN) modelling for the accurate prediction of friction coefficient and surface temperature of a kenaf fibre reinforced epoxy composite for specific tribological loading conditions.
This study has verified the ability of an artificial neural network to make closely accurate generalised predictions within the given domain of the supplied training data. Improvements to the generalised predictability of the neural network was realised through the selection of an optimal network configuration and training method suited to
the supplied training data set.
Hence, the trained network model can be utilised to catalogue the friction coefficient and surface temperature variables in relation to the sliding distance, speed and load parameters. This is limited to the domain of the training data. This will ultimately save time and money otherwise used in conducting further testing.
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