Development of an artificial neural network (ANN) for predicting tribological properties of kenaf fibre reinforced epoxy composites (KFRE)

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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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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Item Type: USQ Project
Item Status: Live Archive
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Mechanical and Electrical Engineering
Supervisors: Yousif, Belal
Date Deposited: 05 Mar 2014 20:36
Last Modified: 06 Mar 2014 02:00
Uncontrolled Keywords: development; artificial neural network; tribological properties; kenaf; fibre composites
Fields of Research : 09 Engineering > 0912 Materials Engineering > 091202 Composite and Hybrid Materials
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
Socio-Economic Objective: B Economic Development > 87 Construction > 8703 Construction Materials Performance and Processes > 870302 Metals (e.g. Composites, Coatings, Bonding)
B Economic Development > 86 Manufacturing > 8604 Leather Products, Fibre Processing and Textiles > 860499 Leather Products, Fibre Processing and Textiles not elsewhere classified

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