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This paper proposes a machine learning-based position prediction approach to determine the position of a light-emitting diode (LED) target using a new measuring system called the multi-aperture positioning system (MAPS). The measurement system is based on a photogrammetric approach using an aperture mask and a single camera sensor. To achieve high accuracy in position calculation, several complex algorithms with high computational complexity are used. The accuracy of the system is equal to or better than that of existing photogrammetric devices. We investigate whether a neural network (NN) can replace the algorithms currently used in the system software to increase the measurement frequency with similar accuracy. Simulated images are used to train the NN, while real images are used to measure performance. Previously, various algorithms were used to calculate the position of the target from the captured images. Our approach is to train an NN, using thousands of labeled images, to predict the position of the target from these images. We investigate whether systematic measurement errors can be avoided; not all factors affecting the measurement precision are yet known, can always be accurately determined, or change over time. When NNs are used, all information contained in the images is learned by the model, considering all influences present at the time of training. Results show that the trained NN can achieve similar performance to the previously used Gaussian algorithm in less time since no filters or other pre-processing of images are required. This factor directly affects the measurement frequency of the MAPS. The light spot center was detected with sub-pixel accuracy without systematic errors in contrast to some of the previously used algorithms. The simulation of the sensor images needs to be improved to investigate the full potential of the NN.

In the past several decades, refuse-derived fuels (RDFs) have been widely applied in industrial combustion processes, for instance, in cement production. Since RDF is composed of various waste fractions with complex shapes, its flight and combustion behaviors can be relatively complicated. In this paper, we present a novel plenoptic camera-based spatial measurement system that uses image processing approaches to determine the dwell time, the space-sliced velocity in the depth direction, and the ignition time of various applied RDF fractions based on the obtained images. The image processing approach follows the concept of tracking-by-detection and includes a novel combined detection method, a 2.5D multiple particle tracking algorithm, and a postprocessing framework to tackle the issues in the initial tracking results. The thereby obtained complete spatial fuel trajectories enable the analysis of the flight behaviors elaborated in the paper. The acquired particles’ properties (duration, velocity, and ignition time) reversely prove the availability and applicability of the developed measurement system. The adequacy and accuracy of the proposed novel measurement system are validated by the experiments of detecting and tracking burning and nonburning fuel particles in a rotary kiln. This new measurement system and the provided experimental results can benefit a better understanding of the RDF’s combustion for future research.

Nanosized small-pore zeolites RHO and MMU-1 (a new Cs-Na modification of an EDI-type zeolite structure) were prepared in the presence of cesium hydroxide by changing the molar composition of the precursor zeolite solution. XRD, Raman, and FTIR results indicated that Cs had a structure-directing effect and controlled the formation of the respective zeolite phases. The presence of water played a crucial role, and when the amount of water was under a certain level, the zeolite cancrinite was formed. Solvent-free syntheses were attempted, and the results confirmed the conclusions drawn based on the water-mediated syntheses, namely that both Cs and water determine the nature of the product formed. The structure of the new zeolite MMU-1 was resolved by Rietveld refinement. MMU-1 was found to exhibit tetragonal symmetry, space group P42$_{1}$c (No. 114), and its framework is composed of four-membered and eight-membered TO$_{4}$-rings. The results reported suggest that there is the potential to synthesize other novel small-pore zeolite structures in the presence of cesium hydroxide by modifications of the chemical composition of the zeolite precursor solution.

During thermal annealing at 1425 °C nominal electric field strengths of 50 V/mm and 150 V/mm were applied along the grain boundary planes of a near 45° (100) twist grain boundary in SrTiO$_3$. Electron microscopy characterization revealed interface expansions near the positive electrode around 0.8 nm for either field strength. While the interface width decreased to roughly 0.4 nm after annealing at 50 V/mm, the higher field strength caused decomposition of the boundary structure close to the negative electrode. Electron energy-loss and X-ray photoelectron spectroscopies demonstrated an increased degree of oxygen sublattice distortion at the negative electrode side, and enhanced concentrations of Ti$^{3+}$ and Ti$^{2+}$ compared to bulk for both single crystals and bicrystals annealed with an external electric field, respectively. Oxygen migration due to the applied electric field causes the observed alteration of grain boundary structures. At sufficiently high field strength the agglomeration of anion vacancies may lead to the decomposition of the grain boundary.

We present an extension of the Livens variational principle (sometimes also referred to as Hamilton-Pontryagin principle) to mechanical systems subject to holonomic constraints. The newly proposed principle embodies an index reduction in the spirit of the often-applied GGL stabilization and thus may be termed “GGL principle”. The Euler-Lagrange equations of the GGL principle assume the form of differential-algebraic equations (DAEs) with differentiation index two. In contrast to the original GGL-DAEs, the present formulation fits into the Hamiltonian framework of mechanics. Therefore, the GGL principle facilitates the design of structure-preserving integrators. In particular, it offers the possibility to construct variational integrators. This is illustrated with the development of a new first-order scheme which is symplectic by design. The numerical properties of the newly devised scheme are investigated for representative examples of constrained mechanical systems.

A novel mixed framework and energy-momentum consistent integration scheme in the field of coupled nonlinear thermo-electro-elastodynamics is proposed. The mixed environment is primarily based on a framework for elastodynamics in the case of polyconvex strain energy functions. For this elastodynamic framework, the properties of the so-called tensor cross product are exploited to derive a mixed formulation via a Hu-Washizu type extension of the strain energy function. Afterwards, a general path to incorporate nonpotential problems for mixed formulations is demonstrated. To this end, the strong form of the mixed framework is derived and supplemented with the energy balance as well as Maxwell's equations neglecting magnetic and time dependent effects. By additionally choosing an appropriate energy function, this procedure leads to a fully coupled thermo-electro-elastodynamic formulation which benefits from the properties of the underlying mixed framework. In addition, the proposed mixed framework facilitates the design of a new energy-momentum consistent time integration scheme by employing discrete derivatives in the sense of Gonzalez. A one-step integration scheme of second-order accuracy is obtained which is shown to be stable even for large time steps. Eventually, the performance of the novel formulation is demonstrated in several numerical examples.

We use the surface detector of the Pierre Auger Observatory to search for air showers initiated by photons with an energy above 1019 eV. Photons in the zenith angle range from 30∘ to 60∘ can be identified in the overwhelming background of showers initiated by charged cosmic rays through the broader time structure of the signals induced in the water-Cherenkov detectors of the array and the steeper lateral distribution of shower particles reaching ground. Applying the search method to data collected between January 2004 and June 2020, upper limits at 95% CL are set to an E-2 diffuse flux of ultra-high energy photons above 1019 eV, 2 × 1019 eV and 4 × 1019 eV amounting to 2.11 × 10-3, 3.12 × 10-4 and 1.72 × 10-4 km-2 sr-1 yr-1, respectively. While the sensitivity of the present search around 2 × 1019 eV approaches expectations of cosmogenic photon fluxes in the case of a pure-proton composition, it is one order of magnitude above those from more realistic mixed-composition models. The inferred limits have also implications for the search of super-heavy dark matter that are discussed and illustrated.

The separation of music signals is a very challenging task, especially in case of polyphonic chamber music signals because of the similar frequency ranges and sound characteristics of the different instruments to separate. In this work, a joint separation approach in the time domain with a U-Net architecture is extended to incorporate additional time-dependent instrument activity information for improved instrument track extractions. Different stages are investigated to integrate the additional information, but an input before the deepest encoder block achieves best separation results as well as highest robustness against randomly wrong labels. This approach outperforms a label integration by multiplication and the input of a static instrument label. Targeted data augmentation by incoherent mixtures is used for a trio example of violin, trumpet, and flute to improve separation results. Moreover, an alternative separation approach with one independent separation model for each instrument is investigated, which enables a more flexible architecture. In this case, an input after the deepest encoder block achieves best separation results, but the robustness is slightly reduced compared to the joint model. The improvements by additional information on active instruments are verified by using real instrument activity predictions for both the joint and the independent separation approaches.

In articulated vehicles steering is accomplished by adjusting the articulation angle. Commonly a steering actuator is used to pivot the two vehicle sections. This actuator can be dispensed with if the pivoting is controlled by selective distribution of the drive torques to the individually driven wheels instead. Since steering is a safety-critical function, it must be ensured even if one of the drive motors fails. For this purpose, we propose a control method for a fault-tolerant four wheel independently driven articulated vehicle. The control method was developed and tested in a simulation environment and validated on a 1:5 scale demonstrator vehicle. The proposed method with constrained control allocation maintains the desired velocity and articulation angle by distributing the driving torques to the four wheels considering the current actuator limits. Both the simulation and the vehicle tests show that the control method meets the control objectives even when a sudden actuator limit occurs during critical driving scenarios like cornering. Thus, the vehicle can keep its maneuverability in the event of a detected failure of a drive motor. Together with reliable failure detection, the proposed approach provides a basis for further development towards innovative fault-tolerant electric articulated vehicles that meet the requirements of functional safety.

We provide theoretical investigations and empirical evidence that the effective stresses in computational homogenization of inelastic materials converge with a higher rate than the local solution fields. Due to the complexity of industrial-scale microstructures, computational homogenization methods often utilize a rather crude approximation of the microstructure, favoring regular grids over accurate boundary representations. As the accuracy of such an approach has been under continuous verification for decades, it appears astonishing that this strategy is successful in homogenization, but is seldom used on component scale. A part of the puzzle has been solved recently, as it was shown that the effective elastic properties converge with twice the rate of the local strain and stress fields. Thus, although the local mechanical fields may be inaccurate, the averaging process leads to a cancellation of errors and improves the accuracy of the effective properties significantly. Unfortunately, the original argument is based on energetic considerations. The straightforward extension to the inelastic setting provides superconvergence of (pseudoelastic) potentials, but does not cover the primary quantity of interest: the effective stress tensor. The purpose of the work at hand is twofold. On the one hand, we provide extensive numerical experiments on the convergence rate of local and effective quantities for computational homogenization methods based on the fast Fourier transform. These indicate the superconvergence effect to be valid for effective stresses, as well. Moreover, we provide theoretical justification for such a superconvergence based on an argument that avoids energetic reasoning.

Digital factories and smart manufacturing systems have been increasingly researched and multiple concepts were developed to cope with prevailing ever-shortening life-cycles. The ubiquitous digital twin, despite many definitions, is often praised for accurate virtual models. One key idea to improve manufacturing through such virtual models is virtual commissioning (VC), aiming at early machine code validation. VC and its virtual models are still lacking behind their real counterparts. This gap between reality and its virtual model, commonly termed reality gap, increases the complexity of creating cyber-physical systems. An especially stark contrast is visible between the idealized virtual model and a real machine encountering errors. While error simulations exist in other fields of research, a thorough investigation in VC is missing. Thus, this paper addresses the task of narrowing the reality gap in VC based on two steps. First, a comprehensive body of research of possible errors encountered in virtual commissioning is analyzed. Secondly, the feasibility of error implementation is discussed. This paper lays the foundation for narrowing the reality gap and enabling test automation and digital twin-based control.

The power of fast Fourier transform (FFT)-based methods in computational micromechanics critically depends on a seamless integration of discretization scheme and solution method. In contrast to solution methods, where options are available that are fast, robust and memory-efficient at the same time, choosing the underlying discretization scheme still requires the user to make compromises. Discretizations with trigonometric polynomials suffer from spurious oscillations in the solution fields and lead to ill-conditioned systems for complex porous materials, but come with rather accurate effective properties for finitely contrasted materials. The staggered grid discretization, a finite-volume scheme, is devoid of bulk artifacts in the solution fields and works robustly for porous materials, but does not handle anisotropic materials in a natural way. Fully integrated finite-element discretizations share the advantages of the staggered grid, but involve a higher memory footprint, require a higher computational effort due to the increased number of integration points and typically overestimate the effective properties. Most widely used is the rotated staggered grid discretization, which may also be viewed as an underintegrated trilinear finite element discretization, which does not impose restrictions on the constitutive law, has fewer artifacts than Fourier-type discretizations and leads to rather accurate effective properties. However, this discretization comes with two downsides. For a start, checkerboard artifacts are still present. Second, convergence problems occur for complex porous microstructures. The work at hand introduces FFT-based solution techniques for underintegrated trilinear finite elements with hourglass control. The latter approach permits to suppress local hourglass modes, which stabilizes the convergence behavior of the solvers for porous materials and removes the checkerboards from the local solution field. Moreover, the hourglass-control parameter can be adjusted to “soften” the material response compared to fully integrated elements, using only a single integration point for nonlinear analyses at the same time. To be effective, the introduced technology requires a displacement-based implementation. The article exposes an efficient way for doing so, providing minimal interfaces to the most commonly used solution techniques and the appropriate convergence criterion.

In this paper, the reduced strain gradient elasticity model with two characteristic lengths is proposed and presented. The reduced strain gradient elasticity model is a particular case of Mindlin’s first strain gradient elasticity theory with a reduced number of material parameters and is a generalization of the simplified first strain gradient elasticity model to include two different characteristic length scale parameters. The two characteristic lengths have the physical meaning of longitudinal and transverse length scales. The reduced strain gradient elasticity model is used to study screw and edge dislocations and to derive analytical solutions of the dislocation fields. The displacement, elastic distortion, plastic distortion and Cauchy stress fields of screw and edge dislocations are non-singular, finite and smooth. The dislocation fields of a screw dislocation depend on one characteristic length, whereas the dislocation fields of an edge dislocation depend on up to two characteristic lengths. For the numerical analysis of the dislocation fields, the material parameters including the characteristic lengths have been used, computed from a second nearest neighbor modified embedded-atom method (2NN MEAM) potential for aluminum.

As markets demand targeted products for highly differentiated use cases, the number of variants in production increases, whilst the volume per variant decreases. Different product variants result in differences in work content on workstation level which cause takt time losses and result in a poor utilization. In this context, matrix-structured production systems with neither temporal nor spacial linkage emerged to reduce the effects of different work content on the entire production system. However, matrix-structured production systems require far more complex production control. To that end, this paper presents a scheduling approach. The proposed scheduling system considers variable process sequences and their allocation to different workstations in order to optimize scheduling objectives. This contribution presents a Monte Carlo tree search based optimizer combined with local search as post optimizer to derive schedules in a short time span to enabling reactive scheduling. The application of the scheduler to a benchmark problem and an industrial scheduling problem demonstrates the quality of the results and illustrates how the scheduler reassigns the work content dynamically.

Craniosynostosis is a congenital disease characterized by the premature closure of one or multiple sutures of the infant’s skull. For diagnosis, 3D photogrammetric scans are a radiation-free alternative to computed tomography. However, data is only sparsely available and the role of data augmentation for the classification of craniosynostosis has not yet been analyzed. In this work, we use a 2D distance map representation of the infants’ heads with a convolutional-neural-network-based classifier and employ a generative adversarial network (GAN) for data augmentation. We simulate two data scarcity scenarios with 15% and 10% training data and test the influence of different degrees of added synthetic data and balancing underrepresented classes. We used total accuracy and F1-score as a metric to evaluate the final classifiers. For 15% training data, the GAN-augmented dataset showed an increased F1-score up to 0.1 and classification accuracy up to 3 %. For 10% training data, both metrics decreased. We present a deep convolutional GAN capable of creating synthetic data for the classification of craniosynostosis. Using a moderate amount of synthetic data using a GAN showed slightly better performance, but had little effect overall. The simulated scarcity scenario of 10% training data may have limited the model’s ability to learn the underlying data distribution.

The material recycling of complex waste streams such as external thermal insulation composite systems (ETICS) is challenging, which is why their recycling in the sense of a circular economy is currently hardly established. Therefore, the combined mechanical and thermochemical recycling of ETICS based on expanded polystyrene (EPS) is investigated experimentally and by simulating full process chains in order to evaluate circular economy opportunities. Model ETICS as example for building and construction waste is pretreated mechanically, followed by either pyrolysis and / or gasification steps, and full mass and energy balances are derived. By the combined recycling, inorganic compounds can be separated to a large extent allowing a pre-concentrate generation. The plastic-rich pre-concentrate is converted into either pyrolysis oil with a high styrene monomer content of 51 wt% or to synthesis gas in the subsequent thermochemical conversions. The holistic approach enables a high carbon recycling rate between 53 and 68 wt%. In addition, the investigation reveals technology limitations and opportunities to be further developed and optimized.

Optimization along the chain processing-structure-properties-performance is one of the core objectives in data-driven materials science. In this sense, processes are supposed to manufacture workpieces with targeted material microstructures. These microstructures are defined by the material properties of interest and identifying them is a question of materials design. In the present paper, we addresse this issue and introduce a generic multi-task learning-based optimization approach. The approach enables the identification of sets of highly diverse microstructures for given desired properties and corresponding tolerances. Basically, the approach consists of an optimization algorithm that interacts with a machine learning model that combines multi-task learning with siamese neural networks. The resulting model (1) relates microstructures and properties, (2) estimates the likelihood of a microstructure of being producible, and (3) performs a distance preserving microstructure feature extraction in order to generate a lower dimensional latent feature space to enable efficient optimization. The proposed approach is applied on a crystallographic texture optimization problem for rolled steel sheets given desired properties.

Machine learning (ML) methods for the analysis of electrocardiography (ECG) data are gaining importance, substantially supported by the release of large public datasets. However, these current datasets miss important derived descriptors such as ECG features that have been devised in the past hundred years and still form the basis of most automatic ECG analysis algorithms and are critical for cardiologists’ decision processes. ECG features are available from sophisticated commercial software but are not accessible to the general public. To alleviate this issue, we add ECG features from two leading commercial algorithms and an open-source implementation supplemented by a set of automatic diagnostic statements from a commercial ECG analysis software in preprocessed format. This allows the comparison of ML models trained on clinically versus automatically generated label sets. We provide an extensive technical validation of features and diagnostic statements for ML applications. We believe this release crucially enhances the usability of the PTB-XL dataset as a reference dataset for ML methods in the context of ECG data.

Purpose Synthetic realistic-looking bronchoscopic videos are needed to develop and evaluate depth estimation methods as part of investigating vision-based bronchoscopic navigation system. To generate these synthetic videos under the circumstance where access to real bronchoscopic images/image sequences is limited, we need to create various realistic-looking image textures of the airway inner surface with large size using a small number of real bronchoscopic image texture patches. Methods A generative adversarial networks-based method is applied to create realistic-looking textures of the airway inner surface by learning from a limited number of small texture patches from real bronchoscopic images. By applying a purely convolutional architecture without any fully connected layers, this method allows the production of textures with arbitrary size. Results Authentic image textures of airway inner surface are created. An example of the synthesized textures and two frames of the thereby generated bronchoscopic video are shown. The necessity and sufficiency of the generated textures as image features for further depth estimation methods are demonstrated. Conclusions The method can generate textures of the airway inner surface that meet the requirements for the texture itself and for the thereby generated bronchoscopic videos, including “realistic-looking,” “long-term temporal consistency,” “sufficient image features for depth estimation,” and “large size and variety of synthesized textures.” Besides, it also shows advantages with respect to the easy accessibility to required data source. A further validation of this approach is planned by utilizing the realistic-looking bronchoscopic videos with textures generated by this method as training and test data for some depth estimation networks.

A droplet deposited on a solid substrate leads to the wetting phenomenon. A natural observation is the lotus effect, known for its superhydrophobicity. This special feature is engendered by the structured microstructure of the lotus leaf, namely, surface heterogeneity, as explained by the quintessential Cassie–Wenzel theory (CWT). In this work, recent designs of functional substrates are overviewed based on the CWT via manipulating the contact area between the liquid and the solid substrate as well as the intrinsic Young's contact angle. Moreover, the limitation of the CWT is discussed. When the droplet size is comparable to the surface heterogeneity, anisotropic wetting morphology often appears, which is beyond the scope of the Cassie–Wenzel work. In this case, several recent studies addressing the anisotropic wetting effect on chemically and mechanically patterned substrates are elucidated. Surface designs for anisotropic wetting morphologies are summarized with respect to the shape and the arrangement of the surface heterogeneity, the droplet volume, the deposition position of the droplet, as well as the mean curvature of the surface heterogeneity. A thermodynamic interpretation for the wetting effect and the corresponding open questions are presented at the end.

In the last decades, there have been great efforts to bring robotic teams to the necessary autonomy level that is required for the safe and efficient exploration of unknown environments. Particularly, long-term, large-scale, or time-critical missions can profit from deploying multiple self-sustaining robotic systems due to their improved accuracy and robustness. The typical paradigm for deploying multiple robots in these cases is that each robot maps and operates in a distinct region of the environment and then collaborates by sharing the gathered information with the other robots. Accordingly, the collective knowledge of all robots allows collaborative teams to foster a more accurate joint estimation which can significantly contribute to the success of a mission. These aspects are essential for rescue robotics in disaster response since multiple robots can explore the environment faster and, when equipped with complementary sensing modalities, can increase the chances of successfully locating human survivors or identifying hazardous areas. However, many of these environments are unstructured and degraded, thus imposing significant challenges on a robot’s perception and locomotion systems. Robotic teams can mitigate these challenges by utilizing various sensor cues, such as visual, thermal, and depth, and by employing different robotic systems, such as ground and aerial robots. Although many perception systems are already designed to rely on several input modalities, the support of various heterogeneous sensors with different characteristics and parameters is often impaired. Consequently, heterogeneous sensor systems might inflict substantial limitations on the system’s capabilities that can inhibit the overall performance of the robots. Nevertheless, a perception system’s ability to generalize well to different sensors ensures long-term applicability with less fine-tuning, which is highly relevant to search and rescue applications. Therefore, resilient mapping and localization systems require a refined orchestration of the input modalities to be robust and suitable for the multitude of robot and sensor deployment scenarios. In this doctoral thesis, we investigated the use of compact spectral representations to analyze the characteristic properties of measurements and trajectory estimations. In particular, we identified several crucial aspects and open problems of collaborative multi-robot mapping and localization where spectral approaches yield useful insights. Assuming an existing centralized multi-robot framework for communication and joint optimization, we initially deal with the multi-modal global localization task to reduce estimation errors by recognizing already visited places at the centralized server. Since the measurement representation and sensor fusion strategy constitutes a significant aspect for achieving good accuracy and precision, we propose a robust localization pipeline that exploits the spectral domain along with a novel spherical representation to circumvent issues associated with noise and drifts. In the second part, we broadcast the optimized multi-robot map to the individual robots to reduce the onboard estimation errors. We can efficiently identify discrepancies between the onboard and the server estimates by analyzing their spectral properties. The novelty of this approach lies within the spectral analysis that enables our system to compensate for the type of onboard failure by classifying the structural disparities. We show that our approach can even overcome estimation failures and degeneracies in the onboard estimation by exploiting the combined knowledge from the robotic team. In the final part of this thesis, we explore localization and mapping approaches with active consideration of semantic information in the environment. We infer an accurate semantic segmentation of surroundings by virtue of a spherical spectral analysis. Utilizing the resulting semantic segmentations to build a constellation of semantic objects provides a more unique representation of a scene than only a description using appearance or geometric primitives. Therefore, we further investigate the use of semantic objects apparent in structured urban environments to improve the detection of already visited places by conservatively filtering the mapped locations. In summary, this thesis advances the research in robust global localization, collaborative multi-robot mapping, and semantic scene understanding by means of spectral analysis in the Euclidean, graph, and spherical domains. We demonstrate the effectiveness of our systems in multiple experiments within complex underground scenarios but also in structured urban scenarios. Finally, the problems addressed in the context of the thesis can also readily help to solve many of the typical robot localization and mapping problems in various other environments.

Additive manufacturing of continuous carbon fibre-reinforced polymer (CCFRP) parts enables the production of high-strength parts for aerospace, engineering and other industries. Continuous fibres allow for parts to be reinforced along the load path, multiplying their mechanical properties. However, current additive manufacturing processes for producing CCFRP parts do not optimally meet the requirements of the matrix. With resin- and extrusion-based processes, the time-consuming and costly post-processing required to remove support structures severely limits design freedom, and producing small batches requires increased effort. In contrast, laser sintering has proven to be a promising alternative in an industrial environment, allowing the production of robust parts without support structures in a time-efficient and economical manner for single and small-batch production. Based on a novel laser-sintering machine with the automated integration of continuous fibres, a combination of the advantages of the laser-sintering process and the advantages of continuous fibres is to be achieved. This paper describes an experimental analysis and optimisation of this laser-sintering machine using design of experiments. The processing time for fibre integration could be reduced by a factor of three compared to the initial state.

Introduction: The most important assessed quality-control (QC) criteria for improvements in high-resolution imaging are represented by the contrast-to-noise-ratio and spatial resolution. Ultra-High-Field (UHF) Magnetic-Resonance-scanners (B ≥ 7 T) for medical research allowed for the improvement in spatial resolution up to the microimaging and nominal microscopy range [pixel-size: ps < (100 μm)2], even in-vivo on humans just recently. Preclinical MRI- and dedicated MR-microscopy (MRM) scanners already allow for microimaging and MRM (1-256 μm) but lack a sensible spatial resolution phantom for QC and performance improvements in hardware, pulse-sequencing and MRprotocols. In most scientific MRI articles, the spatial resolution is characterized by the ps, though this measurement parameter only limits the actual resolution.Methods: Here the Modulation-Transfer-Function (MTF) is used as evaluation concept for the determination of the spatial resolution in MRM using simple intensity profiles. The resolution limit is defined using a critical modulation-level. In approaching visual impressions on spatial resolution an additional criterion derived from the Modulation-depth-to-Noise-Ratio (MNR) is proposed. A practical method for assessment based on a concrete phantom design and its realization is shown.Results: The phantom design consists of several sets of fine grids, specifically featuring high structural anisotropy for optimum SNR and CNR, with different spatial periods ranging from a1 = 256 μm down to a8 = 2 μm, not only for a quick visual qualitative check, but also for quantification of resolution using the MTF for two different spatial encodings in two orthogonal in-plane directions. The challenging demands on the manufacturing technology especially with regard to the aspect-ratio are approached using Deep-X-Ray-Lithography (DXRL) relying on the high brilliance of Synchroton-radiation. Smallest grid plates with width of 4 μm corresponding to 125 line pairs/mm at a plate depth of 100 μm were achieved.Discussion: MR-microscopic images, originating from a microscopy insert on a human UHF-MR-scanner, were used for demonstration of the evaluation process with two independent resolution-criteria. The developed prototype offers unique possibilities for quantitative resolution QC on UHF human and preclinical MR-scanners. Such a resolution-phantom might be very important for the improvement of MR-pulse-sequences, MR-protocols and even hardware. In principle the phantom can also be used for other microscopic imaging-modalities as for instance μCT and Optical-Coherence-Tomography (OCT).

The through-thickness compaction behavior of engineering textiles significantly influences the resulting component properties during liquid composite molding processes (LCM). It determines the final fiber volume content and thus the necessary press force, the permeability as well as the final mechanical properties. In the present work, the behavior of a uni- and bidirectional carbon fiber non-crimp fabric (UD- & Biax-NCF) with the same fiber type and areal density of fibers in the respective main reinforcement directions is tested in a punch-to-plate setup. Thereby, the influence of the relative fiber orientation at the interfaces of a layup as well as the number of plies is investigated. A combined influence of roving nesting and superposition of stitching patterns is observed. This results in a common influence of decreasing resistance to compaction for higher numbers of layers, while the relative orientation of the interfaces in a layup is only significant for the Biax-NCF.

A high level of automation in vehicles is accompanied by a variety of sensors and actuators, whose malfunctions must be dealt with caution because they might cause serious driving safety hazards. Therefore, a robust and highly accurate fault detection and diagnosis system to monitor the operational states of vehicle systems is an indispensable prerequisite. In the area of fault diagnosis, numerous techniques have been studied, and each one has pros and cons. Selecting the best approach based on the requirements or usage scenarios will save much needless work. In this article, the authors examine some of the most common fault diagnosis methods for their applicability to automated vehicle systems: the traditional binary logic method, the fuzzy logic method, the fuzzy neural method, and two neural network methods (the feedforward neural network and the convolutional neural network). For each approach, the diagnosis algorithms for vehicle systems were modeled differently. The analysis of the detection capabilities and the suitable application scenarios of each fault diagnosis approach for vehicle systems, as well as recommendations for selecting different methods for various diagnosis needs, are also provided. In the future, this can serve as an effective guide for the selection of a suitable fault diagnosis approach based on the application scenarios for vehicle systems.

In this work, a process chain for the realization of dense Ti6Al4V parts via different material extrusion methods will be introduced applying eco-friendly partially water-soluble binder systems. In continuation of earlier research, polyethylene glycol (PEG) as a low molecular weight binder component was combined either with poly(vinylbutyral) (PVB) or with poly(methylmethacrylat) (PMMA) as a high molecular weight polymer and investigated with respect to their usability in FFF and FFD. The additional investigation of different surfactants’ impact on the rheological behaviour applying shear and oscillation rheology allowed for a final solid Ti6Al4V content of 60 vol%, which is sufficient to achieve after printing, debinding and thermal densification parts with densities better than 99% of the theoretical value. The requirements for usage in medical applications according to ASTM F2885-17 can be fulfilled depending on the processing conditions.

Long-fiber thermoplastic (LFT) materials compounded via the direct LFT (LFT-D) process are very versatile composites in which polymers and continuous reinforcement fiber can be combined in almost any way. Polycarbonate (PC) as an amorphous thermoplastic matrix system reinforced with glass fibers (GFs) is a promising addition regarding the current development needs, for example battery enclosures for electromobility. Two approaches to the processing and compression molding of PC GF LFT-D materials with various parameter combinations of screw speed and fiber rovings are presented. The resulting fiber lengths averaged around 0.5 mm for all settings. The tensile, bending, Charpy, and impact properties were characterized and discussed in detail. Special attention to the characteristic charge and flow area formed by compression molding of LFT-D materials, as well as sample orientation was given. The tensile modulus was 10 GPa, while the strength surpassed 125 MPa. The flexural modulus can reach up to 11 GPa, and the flexural strength reached up to 216 MPa. PC GF LFT-D is a viable addition to the LFT-D process, exhibiting good mechanical properties and stable processability.

This work is concerned with synthetic microstructure models of polycrystalline materials. Once a representation of the microstructure is generated, the individual grains need to be furnished with suitable crystal orientations, matching a specific crystal orientation distribution. We introduce a novel method for this task, which permits to prescribe the orientations based on tensorial Fourier coefficients. This compact representation gives rise to the texture coefficient optimization for prescribing orientations method, enabling the determination of representative orientations for digital polycrystalline microstructures. We compare the proposed method to established and dedicated algorithms in terms of the linear elastic as well as the non-linear plastic behavior of a polycrystalline material.

With the complexity of modern microstructured materials, computational homogenization methods have been shown to provide accurate estimates of their effective mechanical properties, reducing the involved experimental effort considerably. After solving the balance of linear momentum on the microscale, the effective stress is traditionally computed through volume averaging the microscopic stress field. In the work at hand, we exploit the idea that averaging the elastic energy may lead to much more accurate effective elastic properties than through stress averaging. We show that the accuracy is roughly doubled when using energy equivalence instead of strain equivalence for compatible iterates of iterative schemes. Thus, to achieve a prescribed accuracy, the necessary effort is roughly reduced by a factor of two. In addition to the theory, we provide a handbook for utilizing these ideas for modern solvers prominent in FFT-based micromechanics. We demonstrate the superiority of energy averaging through computational examples, discuss the peculiarities of polarization methods with their non-compatible iterates and expose a superaccuracy phenomenon occurring for the linear conjugate gradient method.

This paper demonstrates how the PIXAPP Photonics Packaging Pilot Line uses its extensive packaging capabilities across its European partner network to design and assemble a highly integrated silicon photonic-based optical transceiver. The processes used are based on PIXAPPs open access packaging design rules or Assembly Design Kit (ADK). The transceiver was designed to have the Tx and Rx elements integrated on to a single silicon photonic chip, together with flipchip control electronics, hybrid laser and micro-optics. The transceiver used the on-chip micro-optics to enable a pluggable fiber connection, avoiding the need to bond optical fibers directly to the photonic chip. Finally, the packaged transceiver module was tested, showing 56 Gb/s loop-back modulation and de-modulation, validating both the transmitter and receiver performance.

Spatial Thinking (ST) is an important part of reasoning. In contrast to Rigid ST (R-ST), Non-Rigid ST (NR-ST) has hardly been researched and tests do not exist, even though NR-ST is crucial for professions that deal with space and time such as engineering and design. Our study at 4 different universities confirms that R-ST does not predict performance in NR-ST; that performance in Non-Rigid ST is significantly lower than in R-ST; and that performance of students in later semesters is not better than in the first semesters. These results highlight the need for further research into NR-ST.

In this paper, we present the analysis of an interpenetrating metal ceramic composite structure. We introduce a new generation algorithm for the modeling of interpenetrating composite microstructures with connected, spherical cavities embedded into an open-porous foam structure. The method uses a geometric ansatz and is designed to create structures of special topology, as the investigated metal ceramic composite structures consisting of a connected AlSi10Mg phase showing spherical shapes embedded into an Al2O3 preform. Based on the introduced enhanced random sequential absorption approach, the generated microstructures yield numerical insights into the material that are not accessible by experimental techniques. The generated microstructures are compared to structures reconstructed from experimental CT scan data considering microstructural features and mechanical behavior. We show that the proposed method is able to generate statistically equivalent microstructures by using only a small number of statistical descriptors. The numerical formulation is validated using compression tests including plastic yielding in the aluminum and damage progression in the ceramic phase. Both the composite material and the pure ceramic preform are considered in this analysis, and good agreement is found between reconstructed and generated microstructures. Furthermore, the observations reveal the importance of the local geometrical sphere arrangement with respect to the mechanical behavior. A validation with experimental results is presented and it is shown that the model predicts microstructural properties and gives meaningful insights into the structural and material interplay. Finally, we discuss the potential of the method for the investigation of failure mechanisms.

During product engineering, engineers draw on existing knowledge as a basis. This knowledge is applied in design processes either by adopting elements, alternating some attributes, or changing the solution principles. While concepts for design reuse exist, the proactive design of a suitable knowledge base is still a challenge. In this paper, we show influencing factors in engineering and their influence on the knowledge base. These factors are of the areas market, company, project, and team. Based on our results, we intent to develop a support for engineers to set up the knowledge base.