Predicting the Depth of Penetration of Molten Metal Into a Pore Network Using TensorFlow

Liquid metal infiltration consists of infusing liquid metal into a porous media or a packed bed of boron carbide powder to react and create ultimately a metal or ceramic matrix embedded with boride-carbide precipitates. The purpose of the study is to model the liquid flow into the capillaries of the packed bed by using machine learning algorithms from an open source available as TensorFlow library created by Google Brain. The library has a variety of algorithms including training and inference algorithms forming deep neural network models to predict the wetting dynamics, flow resistance, and the depth/rate of penetration into the capillaries of the packed bed. In the present work, the results from the machine-learning python code based on the TensorFlow library is compared against the experimental data obtained for molten Hf-Ti-Y-Zr alloys infiltrating into a packed bed of boron carbide at temperatures up to 2300°C. A summary of the techniques used to tweak the machine learning algorithms to predict the infusion behavior will be presented.