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This contribution covers the variational‐based modeling of non‐dissipative magneto‐mechanical systems using a vector potential approach and the thorough analysis and discussion of corresponding conforming finite element methods. Since the construction of divergence‐free finite element spaces explicitly enforcing the Coulomb gauge poses some major challenges, we propose some primal and mixed variational principles that ensure well posedness of the problem and allow to seek the vector potential in unconstrained function spaces. The performance of these methods is assessed in two comparative benchmark studies. The focus of both studies lies on the accurate approximation of field quantities in systems with material discontinuities and re‐entrant corners. [ABSTRACT FROM AUTHOR]

Mechanical clinching is a frequently used joining method for technical components. These joints are usually weak spots. Here, corrosion and fatigue are decisive influencing factors for the assessment of the service life of such joints. Corrosion generally leads to material deterioration and thus to premature failure of the joints. Under certain circumstances, however, corrosion can lead to an increased fatigue life. While this effect has not yet been fully understood, the present work provides a possible explanation and a modeling approach to predict the fatigue life of precorroded clinched joints. The increased fatigue life is observed when the clinched components are briefly (up to 3 weeks) exposed to a salt spray environment. During this time, a small layer of corrosion products protrudes from the metal surface and fills the gaps between the joined sheets. Due to the increased contact area, the mechanical stress in the joint decreases, resulting in an improved fatigue performance. Although there are a variety of corrosion phenomena, for example, pitting, intergranular, and transgranular corrosion as well as galvanic corrosion, experimental studies indicate that galvanic corrosion is the main contributor of this effect. In the present work, a coupled electro‐chemo‐mechanical corrosion model is presented and applied to two test cases. Case I: corrosion products growth, and Case II: corrosion products growth and mechanical loading. [ABSTRACT FROM AUTHOR]

Due to climate change, sustainable and energy‐efficient power supply is urgently required. To increase the performance of solid oxide fuel cells, the effects of the microstructure of their porous electrodes need to be studied. Therefore, real tomography images are used to (i) characterize specific geometrical features such as the two‐point correlation function or the tortuosity and (ii) determine the effective conductivities (thermal, ionic, electronic) and the effective permeability. The temperature‐dependent and anisotropic physical properties based on the first‐order homogenization method are described by the dissipation potential. The results can be used in macroscopic fuel cell simulations to bridge the gap between the micro and macro scale and to gain a better understanding of which characteristics of electrode microstructures are favorable for higher efficiency. [ABSTRACT FROM AUTHOR]

In the field of material informatics, artificial neural networks (ANNs) contribute to the investigation of the processing‐structure‐properties‐performance relationship of materials. This inspires us to leverage the capabilities of ANNs to decode properties of hydrogels, thereby customizing these active materials for sensors or actuators. In the current work, we introduce an approach to predict discrete swelling states of temperature‐responsive hydrogels, especially PNIPAAm, based on their synthesis parameters, utilizing ANN models. To build the database, we analyze literature on temperature‐responsive hydrogels and compile essential synthesis parameters. The corresponding data points related to these synthesis parameters are then extracted. We propose different variants of ANN models and compare their accuracy on the acquired dataset. The selected model can predict the swelling states of hydrogel samples within the test dataset with relative prediction error of 0.11. This approach is applied to predict the expected properties. Subsequently, the hydrogels can be synthesized, and their properties can be experimentally verified. Our approach can be extended to other types of hydrogels and in the prediction of additional properties. The identified synthesis parameters serve as a valuable foundation for the expansion of the database with further literature resources. An enriched database will enhance the performance of the data‐driven model, thereby improving its predictive capabilities. [ABSTRACT FROM AUTHOR]

Stimuli‐responsive hydrogels are representatives of smart materials with enormous swelling capability. The prediction of discrete hydrogel swelling states by artificial neural network based on the processing parameters has been realized in our previous work. In the current study, we explore ways to enhance the prediction capabilities by integrating physical information of the swelling curves to the model: Instead of predicting discrete swelling states, we predict the mathematical parameters of the continuous swelling curve. We therefore assume that the swelling behavior of hydrogels is consistent with a sigmoidal tanh function, based on their physical properties. After predicting the parameters of the sigmoidal function with the trained model, we analyze the prediction accuracy in comparison to the initial discrete prediction. Moreover, the new approach then allows us to physically interpret the different material properties, which denote sensitivity, maximum achievable differential swelling, and the position of the reference point for derivation of a material model. [ABSTRACT FROM AUTHOR]

Shaft misalignments are one of the main causes of damage to couplings in machines. If they are not detected in time, they can lead to machine downtimes. When assembling a machine, however, it is not always possible to align the shafts perfectly. Elastic couplings are able to compensate for such misalignments up to a certain degree. To detect the present radial misalignment, the concept of a sensor‐integrating jaw coupling, developed to determine the transmitted torque, is used. In the teeth of the gear rim, boreholes are drilled and dielectric elastomer sensors (DES) are inserted. If the teeth deform during use of the coupling the DES will deform as well, leading to a change in capacitance, which can be detected. If the two shafts, which the coupling connects, are perfectly aligned to each other, each of the loaded teeth will deform uniformly. However, if there is a radial or angular misalignment, the deformation is different for each tooth, which can be determined with the help of the DES. To demonstrate this effect, a finite element model of the sensor‐integrating jaw coupling with misalignments is created, and numerical simulations are conducted. In this model, the hyper‐viscoelastic material behavior of (i) the gear rim of the jaw coupling and (ii) the DES is taken into account. From the mechanical simulation, the deformed state of the DES is obtained, which is used to compute the capacitance of the sensor. With the results of the numerical simulations, a symbolic regression is performed. The result is an analytical formula that makes the accurate detection of the present radial misalignment possible. The developed framework can later be easily programmed on a microcontroller which is integrated in the sensor‐integrating jaw coupling. [ABSTRACT FROM AUTHOR]

In the paper, the spark plasma sintering (SPS) process for metal particles has been simulated based on experimental data and using the finite element method in the thermo-electro-mechanical formulation with taking into account the temperature influence on parameters of materials (nickel and copper). A comparison of obtained results with experimental data made it possible to create a computational model of the SPS process, the model being convenient to evaluate the influence of creep and plasticity parameters on the size of the interparticle neck forming in SPS. It was found that the creep effect significantly dominated over the plasticity influence on the process of forming the sintering neck at high temperatures. In this case, the variation of creep parameters in simulation also actively affects the formation of the neck.