The foundry industry arguably represents one the most important primary shaping processes in use today [1], and just like all manufacturing industry sectors, it faces a new digitalization challenge [2,3]. While automation as well as sophisticated, physics-based process simulation are well established, the transition to true industry 4.0 applications characterized by aspects like increased autonomy of production systems, the realization of digital twins via data-driven approaches etc. is ongoing. The present study aims at providing an overview of the current state of technology and of research in this field in as far as it is practically applied to metal casting processes. The focus is on high pressure die casting (HPDC), as high productivity and the ensuing extremely short cycle times of this process constitute a special challenge when it comes to process monitoring and control. Major aspects like data acquisition, data management and data evaluation using conventional and AI approaches are covered and illustrated by summarizing case studies from both academia and industry. In addition to process monitoring and control, quality evaluation and prediction, use of AI techniques in alloy development for casting processes is briefly be discussed as side aspect in as far as it is linked to casting processes in general and HPDC specifically.

[1] D. Lehmhus Metals, 2022, 12, 1959.
[2] P. Saxena, M. Papanikolaou, E. Pagone, K. Salonitis, M. R. Jolly In: Tomsett, A. (eds) Light Metals 2020. The Minerals, Metals & Materials Series. Springer, Cham. 
[3] D. Lehmhus Metals, 2024, 14, 334.