Control of the homogenization process is important in obtaining high extrudability and desirable properties in 6xxx aluminum alloys. Three consecutive steps of the process chain were modeled. Microsegregation arising from solidification was described with the Scheil–Gulliver model. Dissolution of Mg2Si, Si (diamond) and β-AlFeSi (β-Al5FeSi) to α-AlFeSi (α-Al12(FeMn)3Si) transformation during homogenization have been described with a CALPHAD-based multicomponent diffusion Dual-Grain Model (DGM), accounting for grain size inhomogeneity. Mg2Si precipitation and associated strengthening during homogenization cooling were modeled with the Kampmann–Wagner Numerical (KWN) precipitation framework. The DGM model indicated that the fractions of β-AlFeSi and α-AlFeSi exhibit an exact spatial and temporal correspondence during transformation. The predictions are in good agreement with experimental data. The KWN model indicated the development of a bimodal particle size distribution during homogenization cooling, arising from corresponding nucleation events. The associated strengthening, arising from solid solution and precipitation strengthening, was in good agreement with experimental results. The proposed modeling approach is a valuable tool for the prediction of microstructure evolution during the homogenization of 6xxx aluminum alloys, including the often-neglected part of homogenization cooling.

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Decision support systems can provide real-time process information and correlations, which in turn assists process experts in making decisions and thus further increase productivity. This also applies to well-established and already highly automated processes in continuous production employed in various industrial sectors. Continuous production refers to processes in which the produced material, either fluid or solid form, is continuously in motion and processed. As a result, the process can usually not be stopped. It is only possible to influence the running process. However, the highly nonlinear interactions between process parameters and product quality are not always known in their entirety which led to inferior product quality in terms of mechanical properties and surface quality. This requires accurate representations of the processes and the products in real-time, so-called digital shadows.Therefore, this contribution shows the necessary steps to provide a digital shadow based on numerical, physical models and process data and to couple the digital shadow with data analysis and machine learning to enable automatic decision support. This is exemplified at various stages throughout two different process chains with continuous processes: first, by using a thermoplastic production process called profile extrusion, and second, on the example of a metal processing process chain, from which three processes are described in more detail, namely, hot rolling, tempering, and fine blanking. Finally, the presented approaches and results are summarized.