This paper presents the application of machine learning in the control of the metal melting process. Metal melting is a dynamic production process characterized by nonlinear relations between process parameters. In this particular case, the subject of research is the production of white cast iron. Two supervised machine learning algorithms have been applied: the neural network and the support vector regression. The goal of their application is the prediction of the amount of alloying additives in order to obtain the desired chemical composition of white cast iron. The neural network model provided better results than the support vector regression model in the training and testing phases, which qualifies it to be used in the control of the white cast iron production.

On the way to building smart factories as the vision of Industry 4.0, the casting process stands out as a specific manufacturing process due to its diversity and complexity. One of the segments of smart foundry design is the application of artificial intelligence in the improvement of the casting process. This paper presents an overview of the conducted research studies, which deal with the application of artificial intelligence in the improvement of the casting process. In the review, 37 studies were analyzed over the last 15 years, with a clear indication of the type of casting process, the field of application of artificial intelligence techniques, and the benefits that artificial intelligence brought. The goals of this paper are to bring to attention the great possibilities of the application of artificial intelligence for the improvement of manufacturing processes in foundries, and to encourage new ideas among researchers and engineers.

The paper describes the innovative solution for revitalization of hydro turbines by using the techniques of reverse engineering and virtual production. The application of modern digital tools in developing, designing and producing shows substantial benefits in improving the existing hydro turbine. Model testing in these cases is not necessary to implement because as the basis is used the known geometry of existing hydro turbine runners and buckets. Digital tools allow a relatively easy way to develop new geometry, improve hydraulic performances of turbines and reach a greater level of usefulness with minimal costs. This paper aims to show optimized geometry and improvement of the Pelton turbine performance using the presented approach