Study of the Freeze Casting Process by Artificial Neural Networks
Yue Liu, Wei Zhai, Kaiyang Zeng
Abstract
Freeze 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.