Continuous casting (cc) is a well-established process in steelmaking, looking back on a history of more than 60 years. Today, casting machines meet all the challenges in process safety, process control and quality control. The lecture focuses on the status of cc by exemplifying new machine concepts, technical solutions, sensors, and process control.
A central part of the lecture deals with cc product quality and quality prediction: Significant progress in developing new sensors and control systems has been evident in the last few years, and, in addition, artificial intelligence might contribute to the correlation between process and quality data. The classical understanding of steel's high-temperature deformation and defect formation phenomena seems, in contrast, unchanged over the decades. That also applies to the numerical simulation of solidification, where even thermodynamic data for some Fe-based multicomponent systems at high temperatures show white areas.
Two examples will finally demonstrate the demand for future fundamental research in solidification and high-temperature defect formation: The possibly increasing load of tramp elements in steel due to the ongoing decarbonization of steel production and its consequence for the surface quality of cc products and the trend to the cc of high alloyed steels.
Advances and developments in aluminium-based materials make it possible to reduce the weight of products in the automotive, aerospace, pharmaceutical, construction, electrical and food industries. Full recyclability reduces the consumption of energy sources and thus emissions and environmental impact, which is a competitive advantage for aluminium. The energy consumption in recycling secondary aluminium is low, only 5% of that in primary aluminium production, making secondary aluminium an excellent energy store compared to primary aluminium. Recycling ensures better sustainable development based on a structured and comprehensive approach such as Life Cycle Thinking (LCT). The high value of aluminium waste has always been the biggest incentive for recycling. If the waste is properly treated, processed and sorted into narrow quality groups, it is possible to produce the highest quality aluminium alloys with high mechanical properties using appropriate additions of primary aluminium and alloying additives, while conserving natural raw materials and saving energy.
Various models could be used to describe the process of producing aluminium alloys from secondary materials. The complexity of the industrial processes should be taken into account by coupling the models with neural networks (NN). The developed software tool will specify different compositions based on large databases and suggest how much of which secondary material should be used to produce the desired aluminium alloy. The optimal recycling is based on several parameters, such as the optimal composition of the input to achieve the desired chemical composition without adding primary aluminium, and the energy-optimal composition of the input to achieve the desired chemical composition.