This study describes the development of an operation-independent simulation model for electrified die-casting foundries which use a smart grid system to cover their energy requirements. The model uses real weather and electricity price exchange data for the simulation period. It can be used to determine and compare electricity costs for production at specific times of day and year, as well as the economic efficiency of different photovoltaic (PV) system and electricity storage variants. It also enables the proportion of different energy sources for each configuration to be analysed. This can be carried out using the model for locations throughout Germany. Additionally, this paper presents exemplary simulation studies that demonstrate the model’s wide range of applications. The results provide an initial overview of the potential savings and optimisation. In the future, the model will provide a basis for determining optimum plant layouts and production times using simulation-based optimisation.

This study demonstrates that hydrogen enrichment in lean-burn spark-ignition engines can simultaneously improve three key performance metrics, thermal efficiency, combustion stability, and nitrogen oxide emissions, without requiring modifications to the engine hardware or ignition timing. This finding offers a novel control approach to a well-documented trade-off in existing research, where typically only two of these factors are improved at the expense of the third. Unlike previous studies, the present work achieves simultaneous improvement of all three metrics without hardware modification or ignition timing adjustment, relying solely on the optimization of the air–fuel equivalence ratio 𝜆. Experiments were conducted on a six-cylinder engine for combined heat and power application, fueled with hydrogen–natural gas blends containing up to 30% hydrogen by volume. By optimizing only the air–fuel equivalence ratio, it was possible to extend the lean-burn limit from 𝜆≈1.6 to 𝜆>1.9, reduce nitrogen oxide emissions by up to 70%, enhance thermal efficiency by up to 2.2 percentage points, and significantly improve combustion stability, reducing cycle-by-cycle variationsfrom 2.1% to 0.7%. A defined 𝜆 window was identified in which all three key performance indicators simultaneously meet or exceed the natural gas baseline. Within this window, balanced improvements in nitrogen oxide emissions, efficiency, and stability are achievable, although the individual maxima occur at different operating points. Cylinder pressure analysis confirmed that combustion dynamics can be realigned with original equipment manufacturer characteristics via mixture leaning alone, mitigating hydrogen-induced pressure increases to just 11% above the natural gas baseline. These results position hydrogen as a performance booster for natural gas engines in stationary applications, enabling cleaner, more efficient, and smoother operation without added system complexity. The key result is the identification of a 𝜆 window that enables simultaneous optimization of nitrogen oxide emissions, efficiency, and combustion stability using only mixture control.

Abstract. Topological Interlocking Elements (TIEs) offer a novel approach to self-supporting and damage-resistant structures, relying on their geometric constraints rather than traditional adhesives or fasteners. While cubic-based TIEs provide ease ways of fabrication and modularity, their limited contact surface areas reduce load transfer efficiency and structural stability. This study introduces a new cubic-based TIE configuration with enhanced contact surfaces, improving interlocking efficiency and mechanical performance. By employing parametric modeling and geometric analysis, the study evaluates the proposed design against conventional cubic TIEs. This research advances interlocking structural systems by presenting a more efficient and stable cubic-based TIE, contributing to improved structural performance and broader applications in engineering and architecture.

This book reports on innovations and engineering achievements of industrial relevance, with a special emphasis on industrial engineering developments aimed at improving the quality of processes and products in the context of a sustainable economy. It gathers peer-reviewed papers presented at the 4th International Conference “Innovation in Engineering”, ICIE 2025, held on June 18-20, 2025, in Prague, Czech Republic. All in all, this third volume of a three-volume set provides engineering researchers and professionals with a timely snapshot of technologies and strategies that should help shaping different industrial sectors to improve production efficiency, industrial sustainability, and human well-being.

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.