Bubble Size Determination in a Half-Scale Curved Water Model Mold for Various Casting Conditions Using Imaging and Machine Learning

Abstract

Parametric studies were performed in a 1:2 scaled, curved water model using shadowgraphy to estimate bubble sizes for different casting parameters such as gas flow rate, liquid flow rate and mold width. Bubble diameter calculations were based on a machine learning algorithm using ImageJ software. Bubble diameters were correlated with input parameters using a deep-learning algorithm. The model performance was determined based on the coefficient of determination (R 2 ). The model showed significant promise with bootstrapping aggregation, validated with five-fold cross-validation and improved accuracy.