Due to the nonlinear material behavior and contradicting application requirements, the selection of a specific electrical steel grade for a highly efficient electrical machine during its design stage is challenging. With sufficient knowledge of the correlations between material and magnetic properties and capable material models, a material design for specific requirements can be enabled. In this work, the correlations between magnetization behavior, iron loss and the most relevant material parameters for non-oriented electrical steels, i.e., alloying, sheet thickness and grain size, are studied on laboratory-produced iron-based electrical steels of 2.4 and 3.2 wt % silicon. Different final thicknesses and grain sizes for both alloys are obtained by different production parameters to produce a total of 21 final material states, which are characterized by state-of-the-art material characterization methods. The magnetic properties are measured on a single sheet tester, quantified up to 5 kHz and used to parametrize the semi-physical IEM loss model. From the loss parameters, a tailor-made material, marked by its thickness and grain size is deduced. The influence of different steel grades and the chance of tailor-made material design is discussed in the context of an exemplary e-mobility application by performing finite-element electrical machine simulations and post-processing on four of the twenty-one materials and the tailor-made material. It is shown that thicker materials can lead to fewer iron losses if the alloying and grain size are adapted and that the three studied parameters are in fact levers for material design where resources can be saved by a targeted optimization.

In this work, a Fe-23Mn-0.3C-1Al high manganese twinning-induced plasticity (TWIP) steel is subjected to varying warm rolling procedures in order to increase the yield strength and maintain a notable ductility. A comprehensive material characterization allows for the understanding of the activated deformation mechanisms and their impact on the resulting microstructure, texture, and mechanical properties. The results show a significant enhancement of the yield strength compared to a fully recrystallized Fe-23Mn-0.3C-1Al steel. This behavior is mainly dominated by the change of the active deformation mechanisms during rolling. Deformation twinning is very pronounced at lower temperatures, whereas this mechanism is suppressed at 500 °C and a thickness reduction of up to 50%. The mechanical properties can be tailored by adjusting rolling temperature and thickness reduction to desired applications.

Non-oriented electrical steel sheets are applied as a core material in rotors and stators of electric machines in order to guide and magnify their magnetic flux density. Their contouring is often realized in a blanking process step, which results in plastic deformation of the cut edges and thus deteriorates the magnetic properties of the base material. This work evaluates the influence of the material’s grain size on its iron losses after the blanking process. Samples for the single sheet test were blanked at different cutting clearances (15 µm–70 µm) from sheets with identical chemical composition (3.2 wt.% Si) but varying average grain size (28 µm–210 µm) and thickness (0.25 mm and 0.5 mm). Additionally, in situ measurements of blanking force and punch travel were carried out. Results show that blanking-related iron losses either increase for 0.25 mm thick sheets or decrease for 0.5 mm thick sheets with increasing grain size. Although this is partly in contradiction to previous research, it can be explained by the interplay of dislocation annihilation and transgranular fracturing. The paper thus contributes to a deeper understanding of the blanking process of coarse-grained, thin electrical steel sheets.

In order to not only produce an open-die forged part with the desired final geometry but to also maintain economic production, precise process planning is necessary. However, due to the incremental forming of the billet, often with several hundred strokes, the process design is arbitrarily complicated and, even today, often only based on experience or simple mathematical models describing the geometry development. Hence, in this paper, fast process models were merged with a double deep Q-learning algorithm to enable a pass schedule design including multi-objective optimization. The presented implementation of a double deep Q-learning algorithm was successfully trained on an industrial-scale forging process and converged stably against high reward values. The generated pass schedules reliably produced the desired final ingot geometry, utilized the available press force well without exceeding plant limits, and, at the same time, minimized the number of passes. Finally, a forging experiment was performed at the institute of metal forming to validate the generated results. Overall, a proof of concept for the pass schedule design in open-die forging via double deep Q-learning was achieved which opens various starting points for future work.

The magnetic properties of non-oriented electrical steel, widely used in electric machines, are closely related to the grain size and texture of the material. How to control the evolution of grain size and texture through processing in order to improve the magnetic properties is the research focus of this article. Therefore, the complete process chain of a non-oriented electrical steel with 3.2 wt.-% Si was studied with regard to hot rolling, cold rolling, and final annealing on laboratory scale. Through a comprehensive analysis of the process chain, the influence of important process parameters on the grain size and texture evolution as well as the magnetic properties was determined. It was found that furnace cooling after the last hot rolling pass led to a fully recrystallized grain structure with the favorable ND-rotated-cube component, and a large portion of this component was retained in the thin strip after cold rolling, resulting in a texture with a low γ-fiber and a high ND-cube component after final annealing at moderate to high temperatures. These promising results on a laboratory scale can be regarded as an effective way to control the processing on an industrial scale, to finally tailor the magnetic properties of non-oriented electrical steel according to their final application.

In modern process design of metallic components, the influence of the metal forming process on the component properties can be taken into account. However, damage occurring concurrent to forming cannot be accounted for yet. Due to the complex multi-scale, multi-mechanism nature of damage, it is very challenging to predict its evolution through any metal forming process chain. In order to enable such damage controlled forming processes in the future three research questions need to be addressed in detail: How are the mechanisms governing the damage initiation and evolution in metals best characterized? How can the damage mechanisms be described and the damage evolution be predicted using models? How do forming processes influence the damage evolution? Answering these questions and considering damage during process design will in the long term lead to improved lightweight components that do not require conventional safety factors, as their performance is already fully known.