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