| Summary: | 碩士 === 明新科技大學 === 精密機電工程研究所 === 100 === This paper applies Design-Expert to generate the technology of D-optimal mixture design which integrating response surface methodology(RSM), and back-propagation neural network integrae genetic algorithm (BPNN/GA) method separately to discuss variation of the permittivity, dielectric strength, and tensile strength depended on injection molding mixture ratio of 10-20% short glass fiber (SGF) 4-12% polytetrafluoroethylene (PTFE) and 68-86% reinforced polycarbonate (PC) composites.
The analysis of variance (ANOVA) was applied to identify the effect of mixture ratio of SGF and PTFE reinforced PC composites for the permittivity and dielectric strength and tensile strength.By regression analysis, a mathematical predictive model ofthe permittivity and dielectric strength and tensile strength were developed in terms of the mixture ratio setting. The combining BPNN/GA optimization method can be obtained for the appropriate combinations of the optimal mixture ratio setting. In addition, the result of BPNN integrating GA was also predictive with BPNN approach.
The results show that the optimal mixture ratio setting gives appropriate combinations with a PC of 0.72, a SGF of 0.20, and a PTFE of 0.08 by RSM approach. Additionally, BPNN/GA approaches are gives appropriate combinations with a PC of 0.73, a SGF of 0.20, and a PTFE of 0.07. By verification results show the proposed algorithm of GA approach has better prediction result than the RSM method.
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