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Fuzzy neuronale Netzwerkanalyse von Gusseisen mit Vermiculargraphit mit verbesserten Zug‐ und Wärmetransporteigenschaften
Author(s) -
Wang G.,
Chen X.,
Xu D.,
Li Y.,
Liu Z.
Publication year - 2020
Publication title -
materialwissenschaft und werkstofftechnik
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.285
H-Index - 38
eISSN - 1521-4052
pISSN - 0933-5137
DOI - 10.1002/mawe.201900047
Subject(s) - graphite , ultimate tensile strength , materials science , indentation hardness , ductility (earth science) , elongation , thermal conductivity , matrix (chemical analysis) , composite material , conductivity , artificial neural network , microstructure , computer science , chemistry , machine learning , creep
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.