Seam visibility remains a significant challenge in Fused Filament Fabrication (FFF) 3D Printing, particularly on curved and complex geometries. Traditional seaming methods, such as the butt-joint, often produce visible artifacts, reducing the surface quality and the mechanical characteristics of parts. This study explores the effectiveness of the Scarf Seam method, a new seam management technique integrated into open-source slicing software, to improving surface finish. While previous research has addressed seam visibility and strength, limited studies have systematically analyzed the impact of seam parameters. Using a Taguchi L16 design, this study evaluates key parameters influencing Scarf Seam performance, including Scarf Joint Speed, Scarf Steps, Scarf Start Height, and Scarf Length. Results indicate that parameter optimization significantly enhances part’s shells concealment. Despite improvements, inconsistencies in seam start localization across different configurations remain unexplained, highlighting the need for further investigation. This research provides valuable insights into seam optimization strategies, contributing to better surface aesthetics and structural performance in FFF-printed parts.

Photovoltaic (PV) energy production in Western countries increases yearly. Its production can be carried out in a highly distributed manner, not being necessary to use large concentrations of solar panels. As a result of this situation, electricity production through PV has spread to homes and open-field plans. Production varies substantially depending on the panels’ location and weather conditions. However, the integration of PV systems presents a challenge for both grid planning and operation. Furthermore, the predictability of rooftop-installed PV systems can play an essential role in home energy management systems (HEMS) for optimising local self-consumption and integrating small PV systems in the low-voltage grid. In this article, we show a novel methodology used to predict the electrical energy production of a 48 kWp PV system located at the Campus Feuchtwangen, part of Hochschule Ansbach. This methodology involves hybrid time series techniques that include state space models supported by artificial intelligence tools to produce predictions. The results show an accuracy of around 3% on nRMSE for the prediction, depending on the different system orientations.

Dynamical Component Analysis (DyCA) and Dynamic Mode Decomposition (DMD), both data-driven dimension reduction methods, are introduced. After application to multivariate simulated signals the techniques of mode selection and the resulting amplitudes are compared with respect to noise robustness and sampling periods. The results indicate that DyCA is a useful alternative to DMD and outperforms DMD under certain conditions. These conditions are based on the underlying dynamics in the terms of differential equations and on the noise ratios of the signals.

Geometric deviations play a crucial role in the quality of additive manufacturing, particularly in parts made with biodegradable resins. Accurately controlling dimensional and geometric variations in manufactured components is critical for achieving defect-free production and meeting functional standards. However, defining a final quality score can be challenging due to numerous dimensional and geometric deviations associated with a part. An innovative metric for evaluating geometric performance was created to measure dimensional precision in components produced through VAT photopolymerization. The index measures the dimensional and geometrical deviations, revealing that external surfaces exhibit greater precision than internal ones. This difference is likely due to internal surfaces overcoming heat dissipation challenges during the cooling process, resulting in less shrinkage for external surfaces. This index is essential in various stages of the manufacturing process, including part design, design for manufacturing and assembly, quality assurance, and process planning, helping to select the appropriate additive manufacturing technology and optimal process parameters.

This paper discusses the challenges and methods for source reconstruction of evoked potentials using deep learning in the context of electroencephalography (EEG). We propose the use of deep learning to address known challenges and improve traditional approaches. We explain the creation of a suitable dataset for solving the inverse problem, including the simulation of neural activity and the use of lead field matrices for the forward solution. Furthermore, we undertake a comparative analysis of some initial deep learning models with similar classical methods.

The manifold of essential matrices is equipped with a one-parameter family of (pseudo-)Riemannian metrics. For the whole family, explicit formulas for geodesics are derived. Moreover, specific curves, so-called quasi-geodesics, are studied and a closed form expression for a quasi-geodesic connecting two given essential matrices is obtained.

In this paper, we show an application of spatiotemporal persistence landscapes to real world time series. Spatiotemporal persistence landscapes are a recent extension of persistence landscapes to time series that capture features of the data that are persistent with respect to time and space. We perform our analysis on EEG data to detect absence epileptic seizures. Further, we compare two dimension reduction techniques (DyCA and PCA) with no dimension reduction and show that the combination of DyCA and persistent landscapes yields the best results.

Splined assemblies ensure precise torque transmission and alignment in mechanical systems. Three-dimensional printing, especially FDM, enables fast production of customized components with complex geometries, reducing material waste and costs. Optimized printing parameters improve dimensional accuracy and performance. Dimensional accuracy is a critical aspect in the additive manufacturing of mechanical components, especially for splined shafts and hubs, where deviations can impact assembly precision and functionality. This study investigates the influence of key FDM 3D printing parameters—layer thickness, infill density, and nominal diameter—on the dimensional deviations of splined components. A full factorial experimental design was implemented, and measurements were conducted using a high-precision coordinate measuring machine (CMM). To optimize dimensional accuracy, artificial neural networks (ANNs) were trained using experimental data, and a genetic algorithm (GA) was employed for multi-objective optimization. Three ANN models were developed to predict dimensional deviations for different parameters, achieving high correlation coefficients (R2 values of 0.961, 0.947, and 0.910). The optimization process resulted in an optimal set of printing conditions that minimize dimensional errors. The findings provide valuable insights into improving precision in FDM-printed splined components, contributing to enhanced design tolerances and manufacturing quality.

Das zentrale Ziel eines Home-Energy-Management-System (HEMS) besteht darin, den Ladevorgang von Elektrofahrzeugen sowie den Betrieb von Wärmepumpen und Heizstäben (Power-to-Heat) in Kombination mit elektrischen und thermischen Speichern zeitlich zu flexibilisieren. Auf der einen Seite ermöglicht die Flexibilisierung durch ein HEMS einen kostenoptimierten Betrieb durch die Erhöhung des Eigenverbrauchs der Photovoltaikanlage (PV-Anlage) sowie von dynamischen Stromtarifen. Auf der anderen Seite besteht die Notwendigkeit flexible Verbraucher, sogenannte „steuerbare Verbrauchseinrichtungen“ (SteuVE), und zukünftig PV-Anlagen durch ein HEMS in das Stromnetz zu integrieren. Jedoch gibt es derzeit keine umfassende Marktübersicht beziehungsweise Markttransparenz.

Engineers and scientists, i.e. STEM educated persons, are seen as strong drivers for technology and knowledge-driven growth and productivity in the high-tech sector including ICT services. However, since 2020 there has been a decline in the absolute number of new entrants to STEM courses.

In 2023, the Federal Statistical Office of Germany reported that 6.5% fewer students had enrolled on STEM courses in Europe. By contrast, countries in the Arab world and East Asia were able to significantly increase the proportion of STEM graduates.

A variety of measures are needed to make STEM attractive to students. This paper explains a package of measures to systematically familiarize children and young people with STEM and thus allay their fears of studying science and engineering. Over the past eight years, the Faculty of Engineering at Ansbach University of Applied Sciences has developed a concept in which participants from pre-school age to high school graduates are addressed with all their senses in age-appropriate laboratory experiments. The Ansbach model for promoting STEM acceptance begins with children of pre-school age by playfully awakening their natural curiosity. In child-friendly experiential spaces at the university, children experience themselves as researchers. In workshop topics from the fields of microbiology, food technology, and molecular biology, which become increasingly complex with the level of education, pupils are introduced to engineering and scientific issues in an age-appropriate way. It is always about experiencing science with all the senses and thus opening up not only a cognitive but also an emotional awareness for STEM.

To reduce the heavy time burden on individual members of the university, the measures are coordinated within the faculty and realized with the involvement of as many faculty members as possible in a modular way resulting in approximately four to six person weeks to attract 400 pupils per year.

Industrial parts often demand high dimensional accuracy and mechanical strength. This study introduces a method using Response Surface Methodology and composite desirability to simultaneously optimize these performances in components manufactured through additive processes. The approach was validated on Polylactic Acid parts fabricated through Fused Filament Fabrication. Optimized process parameters included a print speed of 80 mm/s, a layer height of 0.2 mm, a fan speed of 50%, and an extrusion temperature of 210 °C, yielding tensile strength of 53.27 MPa and dimensional deviations under 5%. Experimental validations showed less than a 5% deviation between predictions and outcomes. The findings provide valuable insights into improving the quality and performance of printed components in various industrial applications, such as gears, highlighting the significance of multi-response optimisation in 3D printing processes. This study ultimately contributes to more efficient and cost-effective manufacturing processes.

Plastics components are degrading over time to small entities mostly known as microplastics (MP). Microplastics particles (MPPs) (by definition <5 mm) can enter the human food chain on different levels. MPPs have been found in several organisms and human heart tissue. MPPs also occur in the plant world. MPPs can be analyzed by light microscopy (LM). The different MP hydrocarbons distinguish from biomaterial matrices. But the degradation doesn’t stop. Microplastics can degrade further to nanoparticles (NPs), being smaller than cells. These nanoplastics particles (NPPs) require higher efforts to be detected. Electron microscopy (EM) is suitable to depict those nanoscale regions. Metal nanoparticles (MNPs) are often well visible in bulk biomaterial with scanning electron microscopy (SEM). But polymeric nanoparticles (PNPs) are more difficult to be detected in biomaterials than MNPs. Dependent on experiment and imaging conditions, PNPs can be distinguished from bulk biomaterial with SEM. Thus, we approach this issue with correlative microscopy (CM) to develop reliable NPP analytics. Using a plant model system with PNPs, combining LM and fluorescence microscopy (FM) with SEM data in a correlative light and electron microscopy (CLEM) approach, non-uniform patch formation of NPPs in- and outside the biomaterial matrix is observed. Energy-dispersive X-ray spectroscopy (EDS), Raman, micro-computer tomography (uCT), ultramicrotomy (UM) and focused ion beam (FIB) data provide additional information. Specific discovery of contaminated plant biomaterial areas is thus possible with single NP resolution. The investigation of the biomaterial matrix behaviour exposed to different PNP sizes and materials is so enabled. The procedural correlative microscopy approach reveals that PNPs seem to be tightly attached to the biomaterial. It indicates further that basic food cleaning procedures might be insufficient for PNP or NPPs removal. The model system can reliably detect PNPs, showing solution pathways in general NPPs analytics.

A systematic sequence of materials was investigated to develop phytic-acid (Phyt)–based flame retarded poly(lactide acid) (PLA), while factoring in molecular weight (MW), crystallinity and mechanical properties. Synergistic approaches were developed based on combinations with lignin and expandable graphite (EG), as well as by applying different Phyt salts of melamine (Mel), piperazine (Pip), and arginine (Arg). Compounds were twin screw extruded, injection molded, hot pressed and investigated with thermal analysis, size exclusion chromatography, infrared spectroscopy, tensile testing, limited oxygen index (LOI), UL 94, cone calorimeter, and scanning electron microscope. 16.7 wt.% flame retardant (FR) slightly enhances crystallization while MW remains unchanged in PLA Phyt Arg and PLA Phyt Mel. LOI was improved to 43.7 vol.% for PLA Phyt Arg, UL 94 V0 achieved for PLA Phyt Pip. Cone calorimeter results show total heat evolved reduced by 14 %, maximum average rate of heat emission 43 % lower, and peak heat release rate reduced by 50 % for PLA Phyt Mel. Phyt Mel combined with EG increased the char yield of PLA to 20 wt.% and 15.5 wt.% at 600 and 900 °C, respectively. Phyt is exploited to enhance char yield, stabilize the intumescent char, and lower the apparent effective heat of combustion. The combination of Phyt Mel and EG was proposed as an efficient FR for PLA via an evidence based developing route.

This study explores the unique operating behavior of an alkaline water electrolysis cell equipped with an ion-solvating membrane, operated with a diluted alkaline electrolyte, specifically 1-M potassium hydroxide (1M KOH), in anode feed mode. Our investigations reveal several key insights. Charge transport: In an ion-solvating membrane, charge transport occurs through both the cations and anions of the electrolyte. Due to electro-osmosis, cation transport to the cathode results in a combined hydrogen-electrolyte discharge from the cathode compartment of the electrolysis cell. The discharged electrolyte is more concentrated than the electrolyte supplied to the anode. The concentration and flow rate of the electrolyte increase with current density and electrolyte temperature. Current density dependence: Since only a fraction of the total charge is transferred by hydroxide ions within the membrane, current density strongly depends on the electrolyte flow through the anode compartment. Membrane stability and performance: The membrane’s mechanical and chemical stability enables operation at high temperatures, up to 80 ◦C. This stability enables increased current density at a given cell voltage. Effects of catalyst use: Using cathode catalysts with high surface areas, such as Raney-Ni, enhances current density because highly concentrated liquid potassium hydroxide forms at the cathode during operation. Anode catalysts with high surface areas increase current density, but only if the flow of hydroxide ions is not impeded. Otherwise, the jV-curve exhibits transport-limited behavior.

Abstract - Shape memory polymers (SMP) can be used in numerous applications, for example in medical engineering, control technology, or in the automotive industry. Various triggers can be used to "remember" a programmed shape, such as temperature, pH value, humidity or light. One example of SMP is polyvinyl alcohol (PVA), which is characterised by biocompatibility, high hydrophilicity and non-toxicity as well as efficient shape memory behaviour. PVA reacts to the stimulus of temperature. In this work, the influence of incorporated nanoparticles on the shape memory effect of PVA will be investigated. For this purpose, the glass transition temperature of PVA containing different iron nanoparticles is determined and compared by means of Differential Scanning Calorimetry (DSC). In addition to the impact of the integrated nanoparticles on the switching temperature, consideration is also given to the quality of the resetting characteristics linked with the shape memory effect. These studies employ thermomechanical analysis as a tool to gain insights into this phenomenon. The inclusion of iron nanoparticles has been observed to result in a slight alteration of the switching temperature, while significantly influencing the shape memory effect and reducing the strain recovery rate by approximately 15%. The strain fixity rate simultaneously increases by approximately 50%. However, the size and structure of the iron nanoparticles showed no discernible impact on the observed phenomenon.