| Summary: | 碩士 === 國立成功大學 === 機械工程學系碩博士班 === 90 === Abstract
Concerning the limits of natural resource and the effect on environmental impact, products had better consider the whole life cycle from the design, manufacture, sale, use and recycle in order to reduce the cost of products and reduce impact in the environment. In all kinds of ways, the remanufacture of the products is the most practicable strategy to arrive above goals. The main structure of this thesis includes two parts. First, it analyzes the end of life strategies that includes the reuse, service, remanufacture, recycle with disassembly, recycle without disassembly and disposal of products by the back propagation neural network. Another part is planning sub-modular of products with the way of product modularity to enhance the remanufactureability.
Neural network has the advantages of easily to use and feasible to non-linear problems with learning capability. The theory of End-of-Life Design Advisor (ELDA) is selected as the basic structure of back-propagation neural network to determine the useful strategy. Furthermore, self organize map neural network was selected to analyze the relation between each strategies. Hence, the trained neural networks can simulate the analysis mode of ELDA rapidly and offer the designer with an easy operation method in the relative research domain.
Product modularity plays a quite important role in designing procedure, especially when the products have to be up-graded. General speaking, product modularity is divided from the functions. In order to enhance the possibilities of remanufacture and decrease the quantity of parts, we divide the sub-modular of products based on the point of view of material, life and up-grade. Finally, this thesis provides the designer the design principles and suggestions in product remanufacture design tasks.
|
|---|
