Ductile cast iron, also known as nodular cast iron, is a graphite-rich cast iron with high impact and fatigue resistance, due to its nodular graphite inclusions. Ductile cast iron is produced by incorporating additives (often FeSi alloys) to the iron base metal at different production steps to obtain the desired graphite shape. A crucial step is the addition of Magnesium to promote the spheroidization of the graphite. The most common method is by adding crushed and sized Ferro-Silicon-Magnesium (FSM). The alloy composition, microstructure, and sizing are assumed to affect the key parameters of this reaction, namely, reactivity, recovery, and slag formation. Therefore, the study of the solidification of FSM is important to understand and predict its performance at the foundries. The present work aims at understanding and predicting numerically the formation of the major phases during the solidification process. Two approaches have been used: thermodynamic calculations through Thermo-Calc solver and phase field modelling using MICRESS. The models have been calibrated by comparison with advanced statistical characterization of the microstructure. The results indicate a competitive growth of the major phases and transformation of phases in solid state that can be emulated by the model.

A digital twin for Aluminium billet DC-casting, a digital replica of the physical casting process, creates a living simulation platform that can analyse, update, and change the process to achieve the multi-objectives process optimization. In this work, we assess the possibility of integrating high throughput micro-macro scale computation, SQL database and an artificial neural network (ANN) to establish an analytical twin for the prediction of a typical Direct Chill casting defect of Al alloys, i.e., hot tearing. The high throughput computation consists of solidification path computation at the microscopic scale (dendrite arm scale with software Alstruc) and heat transfer, fluid flow and stress/strain computation at the macroscopic scale (the scale of billet/ingot dimensions with software Alsim). The two-scale computations are coupled via sharing with Alsim the compositional dependent solidification path (Solid fraction-Temperature curve), thermo-physical properties such as densities, thermal conductivities calculated by Alstruc. Then Alsim calculates all the field variables including thermal stress, volumetric strain, and predicts the locations of the most vulnerable position and its hot tearing susceptibility. We demonstrate that the proposed framework can efficiently predict sump depth and hot-tearing tendency in the center of billets for a range of industrial AA6xxx alloy composition, casting parameters including casting speed and casting temperature. The data generated by the multi-scale computation are used to build a SQL database for training and testing the neural network. The utilities of the trained neural network and established SQL database are discussed for their application to optimize DC casting recipes of 6xxx extrusion billets. Our conclusion is that the proposed high throughput multi-scale simulation, SQLite database and ANN parameterization are three essential pillars supporting the establishment of a digital casting twin, and such a twin can provide a quick screening and selection/adjustment of process parameters before casting or during casting to avoid hot-tears.

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