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Fuzzy neural network analysis on the compacted graphite iron with improved tensile and heat transport properties |
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| Authors: | G Wang X Chen D Xu Y Li Z Liu |
| Affiliation: | 1. School of Materials Science and Engineering, Tsinghua University, BEIJING, 100084 PEOPLE'S REPUBLIC OF CHINA;2. School of Materials Science and Engineering, Tsinghua University, BEIJING, 100084 PEOPLE'S REPUBLIC OF CHINA
Key Laboratory for Advanced Materials Processing Technology, Ministry of Education, BEIJING, 100084 PEOPLE'S REPUBLIC OF CHINA;3. School of Nuclear Equipment and Nuclear Engineering, Yantai University, YANTAI, 264005 PEOPLE'S REPUBLIC OF CHINA |
| Abstract: | In order to find out the most effective method for developing compacted graphite iron with a combination of high tensile strength, ductility and thermal conductivity, the superposed structural effects were investigated by experimental results and the relative significances were ranked on the basis of fuzzy neural network model. The concerned structural parameters consisted of graphite content, vermicularity and microhardness of the matrix. It was found that the relationships between properties and structural parameters become complex due to the mutual disturbances of various characteristics. Irregular and compossible corrections were both observed. The sensitivity level suggested that low microhardness of the matrix and low vermicularity are the optimal directions for improving simultaneously the tensile strength, thermal conductivity and elongation of compacted graphite iron. |
| Keywords: | Compacted graphite iron fuzzy neural network tensile strength elongation thermal conductivity Gusseisen mit Vermiculargraphit fuzzy neuronales Netzwerk Zugfestigkeit Dehnung Wärmeleitfähigkeit |