Paper Title:
Using Regression Analysis to Optimize the Combination of Thermal Conductivity and Hardness in Compacted Graphite Iron
  Abstract

In cast iron there is a contradictory relationship between thermal conductivity and strength. In many applications it is desirable to optimize the material properties to obtain both sufficiently high thermal conductivity and sufficiently high strength. The aim of this paper is to investigate how various microstructure parameters and alloying elements affect thermal conductivity and hardness in compacted graphite irons. It was found that the fraction of ferrite, the fraction of cementite, nodularity and content of carbon and silicon are parameters that influence the thermal conductivity and hardness the most. Based on these five key parameters linear regression equations were created for calculation of thermal conductivity and hardness. Ferrite and carbon have a positive influence on the thermal conductivity, while silicon, cementite and nodularity have a deleterious effect. All parameters except ferrite have a positive influence on the hardness. This is because the thermal conductivity is dependent on the movement of free electrons, and therefore unfavourable growth directions and grain boundaries which impede the electron movement will reduce the thermal conductivity. Ferrite has quite high thermal conductivity, while cementite has poor thermal conductivity, due to an unfavourable crystal structure. Nodular shaped graphite has a lower thermal conductivity than compacted graphite which explains the deleterious influence of nodularity. The soft ferrite phase will reduce the hardness value, while increasing the fraction of harder graphite nodules and harder cementite phase will increase the hardness. To investigate how these five parameters affect the combination of hardness and thermal conductivity, values for hardness and thermal conductivity were calculated for all combinations of key parameters in given intervals, using two linear regression equations. From these it is possible to predict the combination of parameters which gives a particular combination of hardness and thermal conductivity in compacted graphite iron.

  Info
Periodical
Edited by
Adel Nofal and Mohamed Waly
Pages
337-342
DOI
10.4028/www.scientific.net/KEM.457.337
Citation
M. Selin, "Using Regression Analysis to Optimize the Combination of Thermal Conductivity and Hardness in Compacted Graphite Iron", Key Engineering Materials, Vol. 457, pp. 337-342, 2011
Online since
December 2010
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