Polylactide acid (PLA) is one of the most used plastics in extrusion-based additive manufacturing (AM). Although it is bio-based and in theory biodegradable, its recyclability for fused filament fabrication (FFF) is limited due to material degradation. To better understand the material’s recyclability, blends with different contents of recycled PLA (rPLA) are investigated alongside a coextruded filament comprised of a core layer with high rPLA content and a skin layer from virgin PLA. The goal was to determine whether this coextrusion approach is more efficient than blending rPLA with virgin PLA. Different filaments were extruded and subsequently used to manufacture samples using FFF. While the strength of the individual strands did not decrease significantly, layer adhesion decreased by up to 67%. The coextruded filament was found to be more brittle than its monoextruded counterparts. Additionally, no continuous weld line could be formed between the layers of coextruded material, leading to a decreased tensile strength. However, the coextruded filament proved to be able to save on master batch and colorants, as the outer layer of the filament has the most impact on the part’s coloring. Therefore, switching to a coextruded filament could provide economical savings on master batch material.

In the extrusion of plastics, the thermal and material homogeneity of the plastic melt at the die entry are of high importance for the extrudate quality. While static mixers are widely used to improve the melt homogeneity, previous attempts at optimisation for reduced pressure loss and improved mixing had to be performed by hand and human experience, limiting the degrees of freedom and efficiency. A new automatic optimisation method based on the open source software OpenFOAM was developed. Using immersed boundary methods, new target functions in the pre-existing routine adjointShapeOptimizationFoam and an additional algorithm checking the suitability for additive manufacturing and fixing the geometry during run-time is presented. The new algorithm is used to optimise an existing static mixer based on an X-type geometry with integrated oil channels, maximising the heat exchange between oil and melt. Based on the results of these simulative optimisations, the best candidates were manufactured using selective laser melting and experimental trials were run. Experimental validation shows that with our optimisation algorithm, a pressure loss reduction of 10% could be achieved. The core melt temperature was reduced by 6 ${}^{\circ }$C, improving the thermal homogenisation as well. While the main advantage of this method is the rapid optimisation taking the operating point into account, the trials also showed positive results in off-design operating points. This allows the low-cost design and manufacture of individualised static mixers.

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.

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.

One major challenge in profile extrusion is the prediction of shrinking and warpage, leading to high amounts of off-specification goods, especially during start One major challenge in profile extrusion is the prediction of shrinking and warpage, leading to high amounts of off-specification goods, especially during start