  Effective thermal conductivity is an important property of composites for
different thermal management applications. Although physics-based methods, such
as effective medium theory and solving partial differential equation, dominate
the relevant research, there is significant interest to establish the
structure-property linkage through the machine learning method. The performance
of general machine learning methods is highly dependent on features selected to
represent the microstructures. 3D convolutional neural networks (CNNs) can
directly extract geometric features of composites, which have been demonstrated
to establish structure-property linkages with high accuracy. However, to obtain
the 3D microstructure in composite is generally challenging in reality. In this
work, we attempt to use 2D cross-section images which can be easier to obtain
in real applications as input of 2D CNNs to predict effective thermal
conductivity of 3D composites. The results show that by using multiple
cross-section images along or perpendicular to the preferred directionality of
the fillers, the prediction accuracy of 2D CNNs can be as good as 3D CNNs. Such
a result is demonstrated with the particle filled composite and a stochastic
complex composite. The prediction accuracy is dependent on the
representativeness of cross-section images used. Multiple cross-section images
can fully determine the shape and distribution of fillers. The average over
multiple images and the use of large-size images can reduce the uncertainty and
increase the prediction accuracy. Besides, since cross-section images along the
heat flow direction can distinguish between serial structures and parallel
structures, they are more representative than cross-section images
perpendicular to the heat flow direction.
