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This paper presents a databased approach for improving the precision of the moulding sand compressibility in the moulding sand mixer of a foundry. In this approach, the deviation between the measured and the target compressibility is reduced by controlling the water addition. The complex dynamic behaviour of the process variables and their influence on the water addition is modelled with a long short-term memory (LSTM) network. Another LSTM network as control path simulates the impact of the water addition on the compressibility. Simulation and experimental results with the applied model for water prediction in a feedforward control yield relevant improvements of the moulding sand compressibility.
