Cast Porosity Prediction by Means of Thercast Finite Element Analysis.

This research aims to investigate and improve the accuracy of porosity prediction in steel ingot casting by leveraging Thercast finite element simulations. In particular, the study refines the Niyama criterion through additional physical parameters and solidification modeling, aiming to reduce shrinkage porosity and enhance the overall mechanical reliability of cast components. Simulation results reveal that a lower thermal conductivity and faster cooling rates exacerbate shrinkage porosity, while a refined Niyama indicator using explicit solid-fraction weighting, with viscosity, alloy composition, and shrinkage accounted for through the underlying THERCAST material model, improves spatial localization of porosity-prone regions in the investigated case. For the investigated configuration, reducing the cooling rate to around 1.25 K/s decreased the extent of the simulated region classified as porosity-prone relative to the reference case. Furthermore, the analytical porosity-strength relation indicates a material-dependent reduction in strength when the porosity fraction exceeds 2%, underscoring the structural significance of internal voids. This study extends the practical interpretation of the classical Niyama criterion by combining solid-fraction weighting with material-dependent thermophysical inputs, addressing gaps in existing shrinkage porosity models. The approach integrates simulation findings with actual casting defects identified through ultrasonic scanning and metallographic analysis. By merging experimental insights with advanced finite element simulations, foundries can better regulate casting conditions, particularly cooling rates and thermal gradients, to minimize porosity. The refined porosity prediction framework aids in process optimization, improved material utilization, and superior quality assurance of steel ingots.