Accelerated degradation testing is widely accepted in competitive industries. As there is no longer the need to test till failures, there are tremendous cost and time benefits on fully capitalizing on such a testing regime. Consequently, this research has aimed for better understanding of the relationship between design and degradation using the degradation data. Existing work in the literature uses the degradation data to improve the reliability of products. The majority of techniques, however, are centred on statistical experimental methods. For problems with increasing complexities such as large multivariable data set, non-linear interactions and dynamic varying processes, conventional methods cannot resolve the problem efficiently. Furthermore, it can not provide the adequate modelling mechanism to learn from the degradation data autonomously for describing the relationship between the design parameters and degradation. Artificial neural network is widely used for complex problems in the literature. This thesis proposes and demonstrates the neural network modelling methodology into capturing the non parametric relationship between design and degradation. / The development of a neural network consists of data preparation, network design and training and testing. This thesis presents a comprehensive description on the data generation and acquisition process. More specifically, the physical tests, experimental designs, equipment configurations, data acquisitions systems and algorithms are elaborated. Single hidden layer multilayered perceptrons are found to be the most suitable network architectures for the problem domains. Detailed descriptions of the training and testing process in determining the suitable number of hidden neurons sufficient for the problem are provided. / In summary, the neural network modelling methodology is demonstrated for the particular problem domain. As a result of the work in this thesis, two models of different practical significance are developed and compiled as Windows executables for predicting material performances. / Thesis (MEng(ManufacturingEngineering)--University of South Australia, 2006.
| Identifer | oai:union.ndltd.org:ADTP/267272 |
| Creators | Lin, Hungyen. |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
| Rights | copyright under review |
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