Composite materials have gained increasing attention in the past several years due to their superior mechanical properties and improved strength-to-weight ratio over traditional materials. With this focus on composite materials, a concentration on resin transfer molding (RTM) has followed. RTM is an attractive processing method due to its potential for providing consistently superior parts at a lower cost than other manufacturing techniques. / The resin transfer molding process involves a large number of variables that are linked to the design of the component, the selection and formulation of the constituent materials, such as resin and fiber, and the design of the mold and molding process. These variables are strongly related to the system performance, for example mold filling time, and RTM product quality. The need for understanding the impact of RTM product and process design variables on part quality and process performance is crucial. This is accomplished through an integrated product and process design (IPPD) approach. Genetic algorithms (GA), in conjunction with the cascade correlation neural network architecture (CCA-NN), are utilized for the following purposes: (1) to establish a working model that predicts performance and quality measures in RTM given a set of product and process design parameters, and (2) to determine the optimal settings of the product and process design parameters to enhance the RTM process and improve part quality. / Optimum design of RTM product and process design variables will result in high quality parts and enhance the efficiency and robustness of the RTM process. An intelligent, adaptive process control procedure yields consistently high quality parts in the presence of interactions and nonlinearities among RTM parameters. / The proposed research outlines two major tasks, (1) the integration of modeling and simulation technologies that support an integrated product and process design (IPPD) approach, and (2) the intelligent, adaptive control of the RTM process. The goal of the proposed research is to achieve optimum design of RTM parts through the development of a robust process and an intelligent, adaptive process control procedure. This is the vision underlying this research. / Source: Dissertation Abstracts International, Volume: 56-10, Section: B, page: 5703. / Major Professor: Ben Wang. / Thesis (Ph.D.)--The Florida State University, 1995.
| Identifer | oai:union.ndltd.org:fsu.edu/oai:fsu.digital.flvc.org:fsu_77575 |
| Contributors | Spoerre, Julie Kaye., Florida State University |
| Source Sets | Florida State University |
| Language | English |
| Detected Language | English |
| Type | Text |
| Format | 163 p. |
| Rights | On campus use only. |
| Relation | Dissertation Abstracts International |
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