Universität Stuttgart

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Open Access
Residual stresses in deep-drawn cups made of duplex stainless steel X2CrNiN23-4 : influence of the drawing depth
(2021) Simon, Nicola; Erdle, Hannes; Walzer, Stefan; Gibmeier, Jens; Böhlke, Thomas; Liewald, Mathias
Residual stress development in deep drawing processes is investigated based on cylindrical cups made of duplex stainless steel sheet. Using a two-scale approach combining finite element modelling with a mean field homogenization scheme the macro residual stresses as well as the phase-specific micro residual stresses regarding the phases ferrite and austenite are calculated for steel X2CrNiN23-4 for various drawing depths. The simulation approach allows for the numerical efficient prediction of the macro and phase-specific micro residual stress in every integration point of the entire component. The simulation results are validated by means of X‑ray diffraction residual stress analysis applied to a deep-drawn cup manufactured using corresponding process parameters. The results clearly indicate that the fast simulation approach is well suited for the numerical prediction of residual stresses induced by deep drawing for the two-phase duplex steel; the numerical results are in good agreement with the experimental data. Regarding the investigated process, a significant influence of the drawing depth, in particular on the evolution of the residual stress distribution in drawing direction, is observed. Considering the appropriate phase-specific strain hardening, the two-scale approach is also well suited for the prediction of phase specific residual stresses on the component level.
Open Access
Perspectives on data-driven models and its potentials in metal forming and blanking technologies
(2022) Liewald, Mathias; Bergs, Thomas; Groche, Peter; Behrens, Bernd-Arno; Briesenick, David; Müller, Martina; Niemietz, Philipp; Kubik, Christian; Müller, Felix
Today, design and operation of manufacturing processes heavily rely on the use of models, some analytical, empirical or numerical i.e. finite element simulations. Models do reflect reality as best as their design and structure may appear, but in many cases, they are based on simplifying assumptions and abstractions. Reality in production, i.e. reflected by measures such as forces, deflections, travels, vibrations etc. during the process execution, is tremendously characterised by noise and fluctuations revealing a stochastic nature. In metal forming such kind of impact on produced product today in detail is neither explainable nor supported by the aforementioned models. In industrial manufacturing the game to deal with process data changed completely and engineers learned to value the high significance of information included in such digital signals. It should be acknowledged that process data gained from real process environments in many cases contain plenty of technological information, which may lead to increase efficiency of production, to reduce downtime or to avoid scrap. For this reason, authors started to focus on process data gained from numerous metal forming technologies and sheet metal blanking in order to use them for process design objectives. The supporting idea was found in a potential combination of conventional process design strategies with new models purely based on digital signals captured by sensors, actuators and production equipment in general. To utilise established models combined with process data, the following obstacles have to be addressed: (1) acquired process data is biased by sensor artifacts and often lacks data quality requirements; (2) mathematical models such as neural networks heavily rely on high quantities of training data with good quality and sufficient context, but such quantities often are not available or impossible to gain; (3) data-driven black-box models often lack interpretability of containing results, further opposing difficulties to assess their plausibility and extract new knowledge. In this paper, an insight on usage of available data science methods like feature-engineering and clustering on metal forming and blanking process data is presented. Therefore, the paper is complemented with recent approaches of data-driven models and methods for capturing, revealing and explaining previously invisible process interactions. In addition, authors follow with descriptions about recent findings and current challenges of four practical use cases taken from different domains in metal forming and blanking. Finally, authors present and discuss a structure for data-driven process modelling as an approach to extent existing data-driven models and derive process knowledge from process data objecting a robust metal forming system design. The paper also aims to figure out future demands in research in this challenging field of increasing robustness for such kind of manufacturing processes.
Open Access
The “third body” approach to joining of metals by simple shear under pressure
(2024) Beygelzimer, Yan; Grötzinger, Karl C.; Liewald, Mathias; Estrin, Yuri; Kulagin, Roman
A continuum mechanics approach to cold welding (CW) of metals under shear is considered. The main idea is to treat a weld joint as an extra material-a “third body” in its own right. Its properties stem from plastic co‐deformation of the two contacting alloys. The mechanical characteristics of the weld joint, i.e., its strength and plasticity in the complex stress state, are determined by the deformation history of the “third body.” The proposed approach enables a unified description of the CW process itself, as well as the subsequent variation of shape of the composite material with the weld joint.
Open Access
Kontrolle und Identifizierung von Pressteilen im freien Fall : Digitalisierung beim Kaltfließpressen durch Freifallinspektion und -kontrolle
(2023) Deliktas, Tahsin; Liewald, Mathias; Clauß, Philipp; Schmid-Schirling, Tobias; Kuntz, Iris; Feurer, Matthias; Dimitropoulos, Georgios; Wientapper, Folker; Räuchle, Friedrich

Universität Stuttgart

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