Microwave Absorption Of Polymer Matrix Composites And The Study Of Artificial Neural Network

The microwave absorption properties of composites formulated by mixing butadiene-acrylonitrile rubber (NBR) or polyvinyl chloride (PVC) with the carbon Black(CB) and Polyaniline (PAn) were investigated in this paper. The reflection loss, the complex permittivity and permeability and the electric conductivity of the materials were measured. The absorption properties depended on the category and content levels of absorbers. The absorption properties of samples with single layer structure were compared with these of the two-layer or more layer structure samples. In addition, the effect of the thickness and matrix polymer of the composites on the absorption property was discussed. Moreover, with application the MATLAB software to establish three-layer BP network, the absorption properties were imitated and predicted. Experiment and results mainly included:1. The microwave absorption properties of different composites with different CB and PAn levels were determined. The results showed that the electric conductivity of the composites had an influence on the absorption properties. With increasing of the electric conductivity of materials, the absorption peak moved to lower frequency.2. The samples with two and four layers structure were produced. They showed that thu absorption properties of multi-layer structure samples were better than these of the single layer samples if the structure was designed principally.3. The absorption property of a given composites was related to the samples thickness. In the case of the composites with composition ofNBR/CB/PAn (100/25/25) , the best absorption property was obtained from the 2mm thick sample among the 4mm, 2mm and 1mm thick samples, exhibiting the biggest reflection losses 22. 99dB4. By inputting the complex permittivity and permeability ( ) as the vector and the reflection loss as the output vector, the three-layer BP network was established . It can be used to imitate and predict the reflection loss. The prediction result was good, and the error did not exceed 10%.