Universität Stuttgart
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Item Open Access Anti-adhesive surfaces inspired by bee mandible surfaces(2023) Saccardi, Leonie; Schiebl, Jonas; Balluff, Franz; Christ, Ulrich; Gorb, Stanislav N.; Kovalev, Alexander; Schwarz, OliverPropolis, a naturally sticky substance used by bees to secure their hives and protect the colony from pathogens, presents a fascinating challenge. Despite its adhesive nature, honeybees adeptly handle propolis with their mandibles. Previous research has shown a combination of an anti-adhesive fluid layer and scale-like microstructures on the inner surface of bee mandibles. Our aim was to deepen our understanding of how surface energy and microstructure influence the reduction in adhesion for challenging substances like propolis. To achieve this, we devised surfaces inspired by the intricate microstructure of bee mandibles, employing diverse techniques including roughening steel surfaces, creating lacquer structures using Bénard cells, and moulding resin surfaces with hexagonal patterns. These approaches generated patterns that mimicked the bee mandible structure to varying degrees. Subsequently, we assessed the adhesion of propolis on these bioinspired structured substrates. Our findings revealed that on rough steel and resin surfaces structured with hexagonal dimples, propolis adhesion was significantly reduced by over 40% compared to unstructured control surfaces. However, in the case of the lacquer surface patterned with Bénard cells, we did not observe a significant reduction in adhesion.Item Open Access Analytic free-energy expression for the 2D-Ising model and perspectives for battery modeling(2023) Markthaler, Daniel; Birke, Kai PeterAlthough originally developed to describe the magnetic behavior of matter, the Ising model represents one of the most widely used physical models, with applications in almost all scientific areas. Even after 100 years, the model still poses challenges and is the subject of active research. In this work, we address the question of whether it is possible to describe the free energy A of a finite-size 2D-Ising model of arbitrary size, based on a couple of analytically solvable 1D-Ising chains. The presented novel approach is based on rigorous statistical-thermodynamic principles and involves modeling the free energy contribution of an added inter-chain bond DAbond(b, N) as function of inverse temperature b and lattice size N. The identified simple analytic expression for DAbond is fitted to exact results of a series of finite-size quadratic N N-systems and enables straightforward and instantaneous calculation of thermodynamic quantities of interest, such as free energy and heat capacity for systems of an arbitrary size. This approach is not only interesting from a fundamental perspective with respect to the possible transfer to a 3D-Ising model, but also from an application-driven viewpoint in the context of (Li-ion) batteries where it could be applied to describe intercalation mechanisms.Item Open Access CHEMampere : technologies for sustainable chemical production with renewable electricity and CO2, N2, O2, and H2O(2022) Klemm, Elias; Lobo, Carlos M. S.; Löwe, Armin; Schallhart, Verena; Renninger, Stephan; Waltersmann, Lara; Costa, Rémi; Schulz, Andreas; Dietrich, Ralph‐Uwe; Möltner, Lukas; Meynen, Vera; Sauer, Alexander; Friedrich, K. AndreasThe chemical industry must become carbon neutral by 2050, meaning that process‐, energy‐, and product‐related CO2 emissions from fossil sources are completely suppressed. This goal can only be reached by using renewable energy, secondary raw materials, or CO2 as a carbon source. The latter can be done indirectly through the bioeconomy or directly by utilizing CO2 from air or biogenic sources (integrated biorefinery). Until 2030, CO2 waste from fossil‐based processes can be utilized to curb fossil CO2 emissions and reach the turning point of global fossil CO2 emissions. A technology mix consisting of recycling technologies, white biotechnology, and carbon capture and utilization (CCU) technologies is needed to achieve the goal of carbon neutrality. In this context, CHEMampere contributes to the goal of carbon neutrality with electricity‐based CCU technologies producing green chemicals from CO2, N2, O2, and H2O in a decentralized manner. This is an alternative to the e‐Refinery concept, which needs huge capacities of water electrolysis for a centralized CO2 conversion with green hydrogen, whose demand is expected to rise dramatically due to the decarbonization of the energy sector, which would cause a conflict of use between chemistry and energy. Here, CHEMampere's core reactor technologies, that is, electrolyzers, plasma reactors, and ohmic resistance heating of catalysts, are described, and their technical maturity is evaluated for the CHEMampere platform chemicals NH3, NOx, O3, H2O2, H2, CO, and CxHyOz products such as formic acid or methanol. Downstream processing of these chemicals is also addressed by CHEMampere, but it is not discussed here.Item Open Access Mechatronic control system for a compliant and precise pneumatic rotary drive unit(2019) Stoll, Johannes T.; Schanz, Kevin; Pott, AndreasItem Open Access Influence of temperature and pressure on the wetting progress in 21700 lithium‐ion battery cells : experiment, model, and lattice Boltzmann simulation(2024) Wanner, Johannes; Burgard, Matthias; Othman, Nabih; Singh, Soumya; Birke, Kai PeterThe electrolyte filling and subsequent wetting of the active material is a time‐critical process in the manufacturing of lithium‐ion batteries. Due to the metallic cell housing, the process phenomena are insufficiently accessible, preventing the replication of the wetting processes by mathematical or simulative methods and hindering efforts to accelerate the wetting process. Therefore, this publication employs a glass cell housing for electrolyte filling of a 21700 cylindrical cell to investigate the wetting at different temperatures and process pressures. In parallel, a mathematical replication of the wetting, as well as a lattice Boltzmann pore‐scale simulation, is used to evaluate the influence of these varying process boundary conditions. The results show a strong temperature dependence on electrolyte wetting and the positive effect of pressure changes in the wetting process. These findings are particularly relevant to the process design of large‐scale cylindrical cell manufacturing.Item Open Access Enzyme-assisted circular additive manufacturing as an enabling technology for a circular bioeconomy : a conceptual review(2024) Protte-Freitag, Kristin; Gotzig, Sophia; Rothe, Hannah; Schwarz, Oliver; Silber, Nadine; Miehe, RobertAdditive manufacturing (AM) is a decisive element in the sustainable transformation of technologies. And yet its inherent potential has not been fully utilized. In particular, the use of biological materials represents a comparatively new dimension that is still in the early stages of deployment. In order to be considered sustainable and contribute to the circular economy, various challenges need to be overcome. Here, the literature focusing on sustainable, circular approaches is reviewed. It appears that existing processes are not yet capable of being used as circular economy technologies as they are neither able to process residual and waste materials, nor are the produced products easily biodegradable. Enzymatic approaches, however, appear promising. Based on this, a novel concept called enzyme-assisted circular additive manufacturing was developed. Various process combinations using enzymes along the process chain, starting with the preparation of side streams, through the functionalization of biopolymers to the actual printing process and post-processing, are outlined. Future aspects are discussed, stressing the necessity for AM processes to minimize or avoid the use of chemicals such as solvents or binding agents, the need to save energy through lower process temperatures and thereby reduce CO2 consumption, and the necessity for complete biodegradability of the materials used.Item Open Access Multi-method model for the investigation of disassembly scenarios for electric vehicle batteries(2023) Baazouzi, Sabri; Grimm, Julian; Birke, Kai PeterDisassembly is a pivotal technology to enable the circularity of electric vehicle batteries through the application of circular economy strategies to extend the life cycle of battery components through solutions such as remanufacturng, repurposing, and efficient recycling, ultimately reintegrating gained materials into the production of new battery systems. This paper aims to develop a multi-method self-configuring simulation model to investigate disassembly scenarios, taking into account battery design as well as the configuration and layout of the disassembly station. We demonstrate the developed model in a case study using a Mercedes-Benz battery and the automated disassembly station of the DeMoBat project at Fraunhofer IPA. Furthermore, we introduce two disassembly scenarios: component-oriented and accessibility-oriented disassembly. These scenarios are compared using the simulation model to determine several indicators, including the frequency of tool change, the number and distribution of robot routes, tool utilization, and disassembly time.Item Open Access Machine learning-based shear force quality prediction of ultrasonic wire bonds : utilizing process data and machine data without additional sensors(2024) Buchner, Christoph; Seidler, Christian T.; Huber, Marco F.; Eigenbrod, Hartmut; Ribbeck, Hans-Georg von; Schlicht, FranzUltrasonic wire bonding is a highly automated production process that is used billions of times a year in the electronics and electromobility industries. Due to the complexity of the process and the large number of influencing parameters, there are currently no automated methods that can be used without additional sensors to evaluate the shear force bond quality quantitatively and non-destructively with sufficiently high precision. For this reason, this paper presents a new methodology that uses machine learning to enable quantitative, non-destructive prediction of bond quality without additional sensors. For this purpose, a machine learning algorithm was developed that uses various machine data and process data from existing sensors to quantitatively predict the shear force of the bonded joint. In addition, features are extracted from process time series, such as current, power, and frequency of the ultrasonic generator as well as deformation during bonding. Of the total of 2,090 features considered, the number of features could be reduced to 26 by recursive feature elimination, while maintaining the prediction accuracy. By using optimized deep neural networks, on average, a prediction precision of the regression on the shear force of the source bond of over 89.6% R 2 -score and a mean absolute error of 241 cN can be achieved.Item Open Access Numerical investigation and simulation of hydrogen blending into natural gas combustion(2024) Jung, Laura; Mages, Alexander; Sauer, AlexanderThis study reviews existing simulation models and describes a selected model for analysing combustion dynamics in hydrogen and natural gas mixtures, specifically within non-ferrous melting furnaces. The primary objectives are to compare the combustion characteristics of these two energy carriers and assess the impact of hydrogen integration on furnace operation and efficiency. Using computational fluid dynamics (CFD) simulations, incorporating actual furnace geometries and a detailed combustion and NOx emission prediction model, this research aims to accurately quantify the effects of hydrogen blending. Experimental tests on furnaces using only natural gas confirmed the validity of these simulations. By providing precise predictions for temperature distribution and NOx emissions, this approach reduces the need for extensive laboratory testing, facilitates broader exploration of design modifications, accelerates the design process, and ultimately lowers product development costs.Item Open Access Energy density optimization in laser-based powder bed fusion of nano-modified PA12 powder feedstocks(2025) Grünewald, Moritz; Ziefuss, Anna R.; Schlör, Christian; Popp, Kevin; Gann, Stan; Kusoglu, Ihsan Murat; Barcikowski, Stephan; Greiner, Joachim; Middendorf, Peter; Gruber, Piotr; Olejarczyk, Michał; Kurzynowski, Tomasz; Wang, Zhengze; Huang, Yajiang; Coskun, Mert; Kısasöz, Burçin Özbay; Koc, Ebubekir; Rudloff, JohannesAdditive manufacturing (AM) by powder bed fusion using a laser beam (PBF-LB) is often considered as the process of choice for industrial applications. Small changes in process parameters and intrinsic or extrinsic material parameters can significantly affect the final as-built part properties in this highly complex AM process. Choosing a machine configuration with optimal process parameters can be time-consuming, especially when new powders, i.e., nano-modified feedstocks or batches, are used. Even if a set of parameters works for one PBF-LB machine, the same parameters may produce unsatisfactory results on another machine model. Dimensionless parameters can be beneficial in simplifying complex phenomena. In this study, a semi-analytical approach based on printing monolayers was semi-blind tested by five laboratories on different PBF-LB machines. Virgin polyamide 12 (PA12), silver, and carbon black nano-particle-modified PA12 powders were tested. Two dimensionless numbers were used to describe the process. The first number describes the energy conversion dimensionless. The second dimensionless number proposes a minimum energy input demand for full densification. Both numbers combine process parameters (e.g., laser power, scan speed) and material properties (latent heat and solid density), while energy conversion number is considered by experimental results (monolayer thickness). Results indicate how nanoparticles influence thermal conductivity and energy absorption. Suggested surface energy densities based on the dimensionless numbers were compared with mechanical properties. The prediction of the highest overall mechanical values (tensile strength and elongation at break) matched the best mechanical properties. The monolayer approach presents an experimental simple method for predicting suitable machine settings and narrowing the process window in an efficient and material-conserving way.