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Freeze casting has been increasingly applied to process various porousmaterials. A linear relationship between the final porosity and the initialsolid material fraction in the suspension was reported by other researchers.However, the relationship of the volume fraction between the porosity and thesolid material shows high divergence among different materials or frozensolvents, as the nature of materials significantly affects the pores formed infreeze casting. Here, we proposed an artificial neural network (ANN) toevaluate the porosity in freeze casting process. After well training the ANNmodel on experimental data, a porosity value can be predicted from four inputswhich describe the most influential process conditions. The error range,reliability and optimization of the model were also analyzed and discussed inthis study. The results proved that this method effectively summarizes ageneral rule for diverse materials in one model, which is difficult to berealized by linear fitting. Finally, a user-friendly mini program based on awell-trained ANN model is also provided to predict the porosity level forcustomized freeze-casting experiments.

Predicting the Porosity Formed in Freeze Casting by Artificial Neural Network