Optimal Design of Functional Gradient Absorbing Materials by Taguchi-Genetic Algorithm

碩士 === 逢甲大學 === 航太與系統工程學系 === 106 === The study optimized the process and the composition of functionally gradient absorbing material of carbonyl iron powder by the Taguchi method and the genetic algorithm. In the process of fabrication, we choose carbonyl iron powder for the ball milling experiment...

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Bibliographic Details
Main Authors: Lee, Jia-Bao, 李佳保
Other Authors: Lee, Yung-Min
Format: Others
Language:zh-TW
Published: 2017
Online Access:http://ndltd.ncl.edu.tw/handle/27x766
Description
Summary:碩士 === 逢甲大學 === 航太與系統工程學系 === 106 === The study optimized the process and the composition of functionally gradient absorbing material of carbonyl iron powder by the Taguchi method and the genetic algorithm. In the process of fabrication, we choose carbonyl iron powder for the ball milling experiments. By using the Orthogonal Array, 1024 experiments were reduced to 16 experiments, found the optimal combination of five control factors which affect the milling result and did the confirmation experiment. Compare to the tranditional, the aspect ratio increase obviously. We made the 60wt% experiment chip with the combination of polyurethane resin and ball milling powder, measured the electromagnetic parameters by using the vector network analysis and coaxial waveguide method, and then computed the wave absorptivity with 2mm by ANSYS HFSS. However, we discovered that the higher aspect ratio is, the more possible the wave absorbing happen in low frequency at the same thickness. Afterwards, fabricate the chip, from 10wt% to 70wt% respectively with the original and the optimal ball milling process. The optimization is not only have the wave absorptivity in lower frequency but also increase -15dB wave absorptivity in identical concentration. Further, design the multi-layer functionally gradient material with the best bandwidth of absorbing by applying the Taguchi-Genetic Algorithm. At the same time, compared to the Genetic Algorithm and the Taguchi method. We proved that the Taguchi-Genetic Algorithm help a lot for this research area, moreover, the experimental efficiency can be increase dramatically.