Prediction of electrical resistivity of steel using artificial neural network

ABSTRACT Electrical resistivity of commercially produced plain carbon manganese steel has been experimentally measured at room temperature (28–30°C) using four-probe method. Resulting data were used to generate both regression based and artificial neural network-based models for prediction of electrical resistivity from the chemical composition of steel. It was found that both models were capable of predicting the resistivity within ±5% error band. Analysis of data also indicated carbon to be the most influential element to increase resistivity followed by manganese and silicon. A comprehensive literature review indicates no such advanced resistivity prediction model is available in the public literature for commercially produced steel with wide variation in carbon content (0.03 0.85 wt-%), manganese content (0.35–1.50 wt-%) and silicon content (0.015–0.90 wt-%).

[1]  E. Campbell.  Do equiatomic solutions in iron possess equal resistances , 1917 .

[2]  E. A.,et al.  A Comprehensive Treatise on Inorganic and Theoretical Chemistry , 1938, Nature.

[3]  F. Schwerer,et al.  Residual Resistivities of Binary Iron Alloys , 1969 .

[4]  F. Schwerer,et al.  The effect of composition on the resistivity of carbon steel sheet , 1971 .

[5]  H. Aaronson,et al.  Lectures on the theory of phase transformations , 1975 .

[6]  R. D. Shannon.  Revised effective ionic radii and systematic studies of interatomic distances in halides and chalcogenides , 1976 .

[7]  R. A. Matula,et al.  Electrical Resistivity of Ten Selected Binary Alloy Systems , 1983 .

[8]  Ming-Chuan Lee,et al.  Electrical conduction behavior of cementite, Fe3C , 1987 .

[9]  Paul L. Rossiter,et al.  The Electrical Resistivity of Metals and Alloys , 1987 .

[10]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[11]  N. K. Bose,et al.  Neural Network Fundamentals with Graphs, Algorithms and Applications , 1995 .

[12]  H. K. D. H. Bhadeshia,et al.  Neural Networks in Materials Science , 1999 .

[13]  K. Tsuchiya,et al.  Production of bulk cementite and its characterization , 2001 .

[14]  R. Sandström,et al.  Electrical resistivity of steels and face-centered-cubic iron , 2002 .

[15]  O. N. Mohanty,et al.  Electrical resistivity studies in low carbon and HSLA-100 steels , 2003 .

[16]  Shubhabrata Datta,et al.  Composition–Processing–Property Correlation of Cold-Rolled IF Steel Sheets Using Neural Network , 2008 .

[17]  Shubhabrata Datta,et al.  Developing rules bases on the influence of welding parameters in FCAW process from ANN model , 2010 .

[18]  B. Hanumantha Rao,et al.  Artificial neural network models for predicting electrical resistivity of soils from their thermal resistivity , 2010 .

[19]  Shubhabrata Datta,et al.  Designing cold rolled IF steel sheets with optimized tensile properties using ANN and GA , 2011 .

[20]  E. Cottrell,et al.  Electrical and thermal transport properties of iron and iron‐silicon alloy at high pressure , 2012 .

[21]  Yadunath Singh,et al.  ELECTRICAL RESISTIVITY MEASUREMENTS: A REVIEW , 2013 .

[22]  I. Miccoli,et al.  The 100th anniversary of the four-point probe technique: the role of probe geometries in isotropic and anisotropic systems , 2015, Journal of physics. Condensed matter : an Institute of Physics journal.

[23]  E. R. Shepard.  Pipe-Line Currents and Soil Resistivity as Indicators of Local Corrosive Soil Areas , 2017 .