Magnesium alloys due to their low density and high strength-to-weight ratio are promising material for the automotive and aerospace industries. Many elements made from magnesium alloys are produced by means of sand casting. It is essential to investigate impact of the applied mould components on the microstructure and the quality of the castings. For the research, six identical, 100x50x20mm plates has been sand cast from the Elektron 21 magnesium casting alloy. Each casting was fed and cooled in a different way: one, surrounded by mould sand, two with cast iron chills 20mm and 40mm thick applied, another two with the same chills as well as feeders applied and one with only the feeder applied. Solid solution grain size and eutectics volume fraction were evaluated quantitatively in Met-Ilo program, casting defects were observed on the scanning electron microscope Hitachi S3400N. The finest solid solution grain was observed in the castings with only the chills applied. Non metallic inclusions were observed in each plate. The smallest shrinkage porosity was observed in the castings with the feeders applied.

The paper presents some aspects of a development project related to Industry 4.0 that was executed at Nemak, a leading manufacturer of the aluminium castings for the automotive industry, in its high pressure die casting foundry in Poland. The developed data analytics system aims at predicting the casting quality basing on the production data. The objective is to use these data for optimizing process parameters to raise the products’ quality as well as to improve the productivity. Characterization of the production data including the recorded process parameters and the role of mechanical properties of the castings as the process outputs is presented. The system incorporates advanced data analytics and computation tools based on the analysis of variance (ANOVA) and applying an MS Excel platform. It enables the foundry engineers and operators finding the most efficient process variables to ensure high mechanical properties of the aluminium engine block castings. The main features of the system are explained and illustrated by appropriate graphs. Chances and threats connected with applications of the data-driven modelling in die casting are discussed.