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Evaluation of the electrical resistivity of steels
Author(s) -
Bohnenkamp Ulrich,
Sandström Rolf
Publication year - 2000
Publication title -
steel research
Language(s) - English
Resource type - Journals
eISSN - 1869-344X
pISSN - 0177-4832
DOI - 10.1002/srin.200001337
Subject(s) - cementite , electrical resistivity and conductivity , austenite , metallurgy , materials science , carbide , ferrite (magnet) , microstructure , phase (matter) , residual resistivity , chemistry , composite material , electrical engineering , organic chemistry , engineering
Literature data on the physical properties of steels have been collected and put into a database. The resistivity of steels has been analyzed as a function of composition and microstructure. An overview over former studies is given. The steels have been investigated in two groups, ferritic steels and austenitic steels. A thermodynamic analysis with ThermoCalc has been performed. Regression analysis on the influence of composition on the resistivity was then carried out. The results for ferritic steels are: Si and Al have the highest elemental resistivity, followed by Mn, Cu, Ni, Mo, and Cr. C precipitated in cementite shows a high coefficient in the analysis when the amount of Fe bound in cementite is not considered separately. C in solution with ferrite shows no significant effect. Cr bound in cementite shows a significant effect but Mn, though present in cementite in comparable amounts, has no significant effect on the resistivity. N and C have the highest elemental resistivity in austenite, followed by the substitutional solutes Nb, Si, Ti, Cu, Ni, Mo, and Cr. The carbides NbC and TiC appear with a higher coefficient in the regression model than can be explained by phase‐mixture models providing upper and lower bounds for the resistivity of two‐phase alloys. Cr 23 C 6 shows no significant effect. The regression results can be used to predict the resistivity of steels with known composition. The model predicts the resistivity of ferritic steels with a maximum deviation between experimental and computed value of 12 nŌm and a standard deviation of 5.6 nŌm. For austenitic steels, the model prediction shows a maximum deviation of 52 μŌcm and a standard deviation of 20 nŌm.
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