This thesis deals with the mechanical and physical investigations on a newly discovered porous material based on fly ash cenospheres. The process used to fabricate the material along with the physical properties of the material is first described. Under uniaxial compression, it is observed that the material exhibits a long load plateau that is typical of energy absorbing materials such as thin-walled metallic honeycombs. In tension, the material fractures similar to most traditional brittle materials such as glass and ceramics. As a result, several uniaxial compression and tension tests are performed on different samples to evaluate the influence of the different chemicals, the curing time, and the mass density on the 'plateau' strength. However, in addition to its low processing costs, the new material presents important properties that are desirable for discrete materials such as homogeneity and isotropy. Although its insulating properties were not quantified, it appears that the material can be used as an excellent heat barrier. Finally, metallic tubes as well as bamboo poles reinforced with the new material are tested to investigate the effectiveness of the reinforcement, showing highly improved structural performance.

Environmental awareness and legislative pressures have made manufacturers responsible for the take-back and end-of-life treatment of their products. Therefore, manufacturers are struggling to find ways to recover maximum value from returned products. This goal can best be achieved by promoting multiple reuse programs as reuse is one of the most effective ways to enhance a sustainable engineering economy. Since the essential goal of the reuse strategy is to reuse parts, the reliability of used parts becomes a core issue. Research indicates that reuse is technologically feasible, associated with a significant manufacturing cost saving, and it does not compromise product quality. However, it is not easy to be applied in reality. There are several uncertainties associated with reuse, the most common is the uncertainty of the product???s quality after use. A widespread implementation of the reuse strategy could be triggered, subject to the availability of reliable methods to assess the useful remaining life of parts. The evolution of such a methodology would play a pivotal role in making decisions on the supply chain process and the recovery value of returned products. Reliability assessment by life cycle data analysis is the basis of this research. The proposed methodology addresses the problem of reliability assessment of used parts by considering two important aspects. It performs statistical as well as condition monitoring data analysis for decision-making on reuse. The analysis is carried out in two stages. Firstly, a wellknown reliability assessment procedure, the Weibull analysis, is applied to analyse time-tofailure data to assess the overall reuse potential of components. In the second stage, the used capacity (actual life) of components is determined by analysing their operating history (condition monitoring data). The linear and nonlinear regression analysis, Kriging procedures and artificial neural networks (ANN) are employed in this stage. Finally, the Weibull analysis and ANNs are integrated to estimate the remaining useful life of components/assemblies of a product at the end of its first life cycle. The model was validated by using life cycle data from consumer products.

Environmental awareness and legislative pressures have made manufacturers responsible for the take-back and end-of-life treatment of their products. Therefore, manufacturers are struggling to find ways to recover maximum value from returned products. This goal can best be achieved by promoting multiple reuse programs as reuse is one of the most effective ways to enhance a sustainable engineering economy. Since the essential goal of the reuse strategy is to reuse parts, the reliability of used parts becomes a core issue. Research indicates that reuse is technologically feasible, associated with a significant manufacturing cost saving, and it does not compromise product quality. However, it is not easy to be applied in reality. There are several uncertainties associated with reuse, the most common is the uncertainty of the product???s quality after use. A widespread implementation of the reuse strategy could be triggered, subject to the availability of reliable methods to assess the useful remaining life of parts. The evolution of such a methodology would play a pivotal role in making decisions on the supply chain process and the recovery value of returned products. Reliability assessment by life cycle data analysis is the basis of this research. The proposed methodology addresses the problem of reliability assessment of used parts by considering two important aspects. It performs statistical as well as condition monitoring data analysis for decision-making on reuse. The analysis is carried out in two stages. Firstly, a wellknown reliability assessment procedure, the Weibull analysis, is applied to analyse time-tofailure data to assess the overall reuse potential of components. In the second stage, the used capacity (actual life) of components is determined by analysing their operating history (condition monitoring data). The linear and nonlinear regression analysis, Kriging procedures and artificial neural networks (ANN) are employed in this stage. Finally, the Weibull analysis and ANNs are integrated to estimate the remaining useful life of components/assemblies of a product at the end of its first life cycle. The model was validated by using life cycle data from consumer products.

Environmental awareness and legislative pressures have made manufacturers responsible for the take-back and end-of-life treatment of their products. Therefore, manufacturers are struggling to find ways to recover maximum value from returned products. This goal can best be achieved by promoting multiple reuse programs as reuse is one of the most effective ways to enhance a sustainable engineering economy. Since the essential goal of the reuse strategy is to reuse parts, the reliability of used parts becomes a core issue. Research indicates that reuse is technologically feasible, associated with a significant manufacturing cost saving, and it does not compromise product quality. However, it is not easy to be applied in reality. There are several uncertainties associated with reuse, the most common is the uncertainty of the product???s quality after use. A widespread implementation of the reuse strategy could be triggered, subject to the availability of reliable methods to assess the useful remaining life of parts. The evolution of such a methodology would play a pivotal role in making decisions on the supply chain process and the recovery value of returned products. Reliability assessment by life cycle data analysis is the basis of this research. The proposed methodology addresses the problem of reliability assessment of used parts by considering two important aspects. It performs statistical as well as condition monitoring data analysis for decision-making on reuse. The analysis is carried out in two stages. Firstly, a wellknown reliability assessment procedure, the Weibull analysis, is applied to analyse time-tofailure data to assess the overall reuse potential of components. In the second stage, the used capacity (actual life) of components is determined by analysing their operating history (condition monitoring data). The linear and nonlinear regression analysis, Kriging procedures and artificial neural networks (ANN) are employed in this stage. Finally, the Weibull analysis and ANNs are integrated to estimate the remaining useful life of components/assemblies of a product at the end of its first life cycle. The model was validated by using life cycle data from consumer products.

Thesis (M.A.I.S.)--Oregon State University, 2009. / Printout. Includes bibliographical references (leaves 74-79). Also available on the World Wide Web.

Thesis (Ph. D.)--Industrial and Systems Engineering, Georgia Institute of Technology, 2008. / Committee Co-Chair: Ammons, Jane C.; Committee Co-Chair: Realff, Matthew J.; Committee Member: Griffin, Paul M.; Committee Member: Peoples, Robert; Committee Member: Thomas, Valerie.

Thesis (M. S.)--Mechanical Engineering, Georgia Institute of Technology, 2008. / Committee Chair: Bert Bras; Committee Member: Beril Toktay; Committee Member: Shreyes Melkote.

Thesis (M.Eng.). / Written for the Dept. of Mining, Metals and Materials Engineering. Title from title page of PDF (viewed 2009/06/15). Includes bibliographical references.

Thesis (M. Sc.)--University of Hong Kong, 2000. / Includes bibliographical references.

Thesis (M. Sc.)--University of Hong Kong, 2001. / Includes bibliographical references (leaves 60-62).