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Download fileStudy of the Freeze Casting Process by Artificial Neural Networks
journal contribution
posted on 2020-08-27, 15:10 authored by Yue Liu, Wei Zhai, Kaiyang ZengFreeze casting technology
has experienced vast development since
the early 2000s due to its versatility and simplicity for producing
porous materials. A linear relationship between the final porosity
and the initial solid material fraction in the suspension was reported
by many researchers. However, the linear relationship cannot well
describe the freeze casting for various samples. Here, we proposed
an artificial neural network (ANN) to analyze the influence of critical
parameters on freeze-cast porous materials. After well training the
ANN model on experimental data, a porosity value can be predicted
from four inputs, which describe the most influential process conditions.
Based on the constructed model, two improvements are also successfully
added on to infer more information. By involving big data from real
experiments, this method effectively summarizes a general rule for
diverse materials in one model, which gives a new insight into the
freeze casting process. The good convergence and accuracy prove that
our ANN model has the potential to be developed for solving more complicated
issues of freeze casting. Finally, a user-friendly mini-program based
on a well-trained ANN model is also provided to predict the porosity
for customized freeze-casting experiments.