Data Models for Casting Processes – Performances, Validations and Challenges

Abstract Data-driven models with their associated data learning and training schemes can be utilised for the light metal casting processes. This paper presents the basis of data model building processes along with data training and learning exercises for vertical direct chill casting and high pressure die casting (HPDC) applications. The concepts of efficient database building, data translations and sampling, as well as real-time model building and validations are briefly discussed. Rigorous performance studies were additionally carried out for two real-world case studies. Different combinations of data solvers and interpolators are adapted for the model building techniques, while machine learning schemes are used for data trainings.