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

Martin Selin

doi:10.4028/www.scientific.net/KEM.457.337

Paper Titles

Researches about the Determination of the Thermal Conductivity Coefficient for Silica Sand Moulds Used in Romanian Foundries
p.312

Modelling the Thermal Conductivity of Various Cast Irons
p.318

Modeling of the Divorced Eutectic Solidification of Spheroidal Graphite Cast Iron
p.324

Simulation of the Ductile Iron Solidification Using a Cellular Automaton
p.330

Using Regression Analysis to Optimize the Combination of Thermal Conductivity and Hardness in Compacted Graphite Iron
p.337

Development of Ductile Iron Material, Simulation and Production Technology to Locally Strengthen Castings
p.343

Prediction of Microstructure and Porosity Formation of Practical Spheroidal Graphite Iron Castings
p.349

Some Key Points on High Strength Grey Iron Cylinder Block Production
p.357

Effect of Selected Alloying Elements on Mechanical Properties of Pearlitic Nodular Cast Irons
p.361

HomeKey Engineering MaterialsKey Engineering Materials Vol. 457Using Regression Analysis to Optimize the...

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.

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Periodical:

Key Engineering Materials (Volume 457)

Pages:

337-342

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.457.337

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Online since:

December 2010

Authors:

Martin Selin

Keywords:

Compacted Graphite Iron (CGI), Regression Analysis, Thermal Conductivity (TC), Vickers Hardness

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