Shrinkage porosity prediction empowered by physics-based and data-driven hybrid models

Several defects might affect a casting part and degrade its quality and the process efficiency. Porosity formation is one ofthe major defects that can appear in the resulting product. Thus, several research studies aimed at investigating methods thatminimize this anomaly. In the present work, a porosity prediction procedure is proposed to assist users at optimizing porositydistribution according to their application. This method is based on a supervised learning approach to predict shrinkageporosity from thermal history. Learning data are generated by a casting simulation software operating for different processparameters. Machine learning was coupled with a modal representation to interpolate thermal history time series for newparameters combinations. By comparing the predicted values of local porosity to the simulated results, it was demonstratedthat the proposed model is efficient and can open perspectives in the casting process optimization.