Klaus Dröder

As Lithium-ion batteries (LIBs) are forecasted to reach their technological optimization limits in the coming decade, next generation battery technologies, such as solid-state batteries (SSBs), are investigated to enable even higher energy and power densities. However, depending on the utilized solid electrolytes (SEs), the cell components exhibit low mechanical stability or an adhesive behavior with serious implications on handling operations during cell assembly. Additionally, mainly SSB cell components with one-sided coating are currently available, which limit the automated cell assembly to the production of bicells with challenges in securely changing the component’s orientation during stacking with established gripping technologies. In the present research, a drum gripping concept for a secured change of the cell components’ orientation along its pitch axis during handling is introduced. Especially relevant is the avoidance of undesirable folds in the materials which occur in dependence of the adjustable pressure difference and the gripper’s rotation direction. To validate the applicability of the presented concept, an experimental evaluation of the deposition accuracy in relation to the supplied pressure difference with polymer-based SEs and lithium metal anodes is carried out. As key results, adequate deposition accuracies are achieved with deposition with the original orientation and for pressure differences of 0.5 bar. Overall, the proposed gripping concept can be viewed as a valid solution for a flexible automated cell assembly of early-stage test cells which are required for further developments towards the establishment of industry-scale SSB production.

Digital twins are increasingly an integral part of engineering for special machinery and mechanical components in general, as they enable insights into machine operation, behavior and condition and improve system reliability, providing substantial benefit and information to the various stakeholders. However, the success and further utilization of digital twins depends on the perceived added value for these stakeholders within the manufacturing company, the customer or regulation authorities. A lack of integration of stakeholders can lead to a rejection of the digital twin at the user level. Therefore, this study aims to develop a methodology for the machine-specific identification and classification of stakeholders within the digital twin framework, ensuring an alignment of diverse interests and requirements of all relevant parties. Based on the developed method, the approach is applied to a production machine as a sample use case. A comprehensive network with 39 stakeholders is identified. These stakeholders can be categorized into four primary groups: system users, system developers, regulatory and legislative bodies, and decision-makers. For each stakeholder, the fundamental requirements are identified and a classification is carried out both thematically and in relation to the observation period. Consequently, the proposed stakeholder classification method enables a tailored implementation process of digital twins according to the unique requirements of each asset and its associated stakeholders connected to the production machine. This targeted approach enhances stakeholder engagement, maximizes the utility and effectiveness of digital twins, and contributes to the overall success of projects in the mechanical and special machine engineering sectors.

Traditional tool development processes in mechanical forming, especially for pressing tools, are often limited by time‐consuming trial‐and‐error cycles, which rely heavily on expert knowledge. These cycles can create bottlenecks and increase costs. Machine learning can be a useful tool to greatly speed up these processes by using advanced models. In this regard, previous research proposed and employed a denoising diffusion model (DDM) to inversely model effective tool surfaces from final product geometries. This model leverages spatial and temporal attention mechanisms to enhance its predictive capabilities. While DDM achieved commendable overall predictions, the accuracy in reconstructing individual denoised frames during later forming steps was lacking. To address this, the present research adopts a stepwise strategy focused on the late deformation state in forming simulations. The research utilizes pixel‐adaptive convolutional neural networks (PAC) to predict high‐resolution simulation output of various channels incorporated in DDM from coarse‐mesh results, using the final part geometry as high‐resolution guidance. This approach enables efficient and reliable estimation of these high‐resolution distributions for the late deformation stage, reducing reliance on computationally intensive full‐resolution simulations at this critical point. This work demonstrates the effectiveness of PAC for simulation refinement in late‐stage deformation and lays the foundation for future integration of PAC into the DDM framework, thereby contributing to ongoing efforts toward more efficient, data‐driven approaches in industrial tool design and manufacturing.

Laser-based powder bed fusion enables the additive manufacturing of high-quality metal components. Beyond extending its applicability to new materials, current research increasingly focuses on process control to ensure consistent and predictable mechanical performance. Precipitation-hardenable alloys such as 15-5PH are attracting growing interest owing to their high strength, stiffness, and corrosion resistance. Previous studies have predominantly addressed microscopic and macroscopic analyses, with particular emphasis on porosity, its impact on mechanical properties, and the effects of various post-processing heat treatments. However, thermal properties and dimensional accuracy, both dependent on the applied process parameters, are of considerable relevance for the subsequent applicability, as they exert a decisive influence on component design. In this context, the present work examines the mechanical and thermal properties obtained under varying laser powers and scanning speeds, and compares these with the achievable dimensional accuracy using a plate-based SC-BCC structure as an example. The objective is to identify a process window that achieves a compromise between high dimensional accuracy and desirable mechanical properties. The results demonstrate that low energy densities yield high dimensional accuracy but also high porosity, which degrades both mechanical and thermal performance. In contrast, high energy densities result in high mechanical properties but compromise dimensional accuracy due to increased distortion during processing. Within the investigated process window, a favorable compromise between dimensional accuracy and the resulting mechanical and thermal properties of 15-5PH was identified. These findings contribute to a deeper understanding of the process–property relationships and highlight limitations relevant to future applications.

With rising efforts to enable a circularity of valuable resources of lithium-ion batteries, a growing number of recycling companies in Europe are expanding their capacities and developing new recycling technologies. The European Union (EU) has set a benchmark for battery recycling by publishing recycling targets. These targets require precise mass determination of the individual battery components, making disassembly of the battery mandatory for characterization. The paper puts forth a semi-automated disassembly procedure for determining the composition of the components at the module and cell levels across a range of designs. Our analysis incorporates the introduction of TGA as a novel, direct method for determining the cathode active material with an accuracy above 99%. This approach is intended to define the recycling input for all extant recycling routes by providing quantitative experimental results with statistical significance. The results indicate a black mass proportion of 61.6% at the module level and 53–74% at the cell level. Additionally, there are significant differences in value creation, ranging from 0.80 to 1.81 USD kg⁻¹ black mass, depending on the cell chemistry. The procedure can be used for EoL and scrap material, and enables greater transparency and comparability in battery recycling, opening up new perspectives for the resource-efficient and targeted use of various recycling technologies.