The current energy crisis and the high cost of fossil fuels pose major challenges for energy-intensive industries such as the non-ferrous foundry industry. Therefore, it is important to promote the transition to renewable energy sources with the help of the electrification of the melting units. In this pilot study, for the first time the conversion of conventional foundries to sustainable technologies is simulated. For this purpose, a simulation model is developed based on a selected example company. It considers the energy consumption and the logistical effects of a converted operation. The simulation model is implemented as a hybrid simulation combining a discrete
event simulation at the plant level and a process simulation within the furnaces. The study shows how a sustainable energy supply can be achieved in foundries. It also considers the impact of efficiency, energy costs and emissions.

Epoxy resins are widely used polymers in the automotive and aerospace fields. Different blends of novel biodegradable resins have been studied in the last years in order to provide sustainability while maintaining the same properties of epoxy resins. Bio-based thermoset resins made with acrylated epoxidized soybean oil are well-studied in different vat polymerization techniques. The present work compares a bio-based resin and a petroleum-based resin. A benchmark with different features was designed and manufactured by a VAT photopolymerization process using both materials; measured with an optical scanning device; thus, the dimensional deviations were analyzed through inspection software. Tensile and flexural specimens were manufactured with the same procedure and tested with a dynamometer machine. Therefore, the comparison between a biodegradable resin and a petroleum-based resin is discussed in terms of the quality and mechanical performances of manufactured parts, considering the use of identical printing conditions. Some parts are required to satisfy both the requirements at the same time, such as the gears. Therefore, dimensional accuracy and mechanical strength need to be controlled and evaluated in a unique final quantification. This work proposes a novelty performance index to quantify dimensional accuracy and mechanical strength simultaneously. By combining the two aspects it is possible to define the overall performance obtained with the used material, optimizing the manufacturing process by choosing the proper material for each purpose.

In spring 2022, we implemented e-portfolios at a product design course for engineers in the bachelor's programme on ‘Sustainable Engineering’ at the University of Applies Sciences, Ansbach. The use of e-portfolios was new to both students and lecturers. To evaluate the effect the e-portfolio had on students, we accompanied the implementation with surveys and interviews. This paper provides a comprehensive analysis of the evaluated results. Among other things, the following three main findings convinced us to continue with e-portfolio work. First, ~91% of the interviewed students felt that they were supported very well by our introduction. Second, ~93% got along well / very well with the functions of the e-portfolio software. Third, ~86% of those interviewed appreciated the freedom of design. Prior to the implementation of e-portfolios in our first test course, we identified the following factors as crucial for the successful implementation of e-portfolios: a comprehensive personal introduction, extensive information material, continuous guidance, clear work instructions, room for flexibility and creativity to foster learners' individual strengths, and exchange between learners and teachers. This paper reflects on these initial factors. The aspects identified for further improvement in the second round of e-portfolios, in the summer of 2023, are better technical preparation of the lecturers, the communication of technical borders in advance, timing of the accompanying e-portfolio workshops and a more comprehensive promotion of teamwork. The suggested modifications will be discussed in detail in this paper.

Students not always enjoy an in-depth practical learning experience with an adequate portion of hands-on during their academic education. In many fields of study, traditional laboratories are common learning spaces that are, however, not accessible 24/7 and, the practical work is mostly pre-defined by the lecturer, resulting in a short and very “passive” active learning. To overcome this limitation and to provide a broader availability and to foster individual learning experience, we aim to transform this analog world into a modern learning and teaching environment using digital technologies and a corresponding digital framework for courses and laboratories. An existing laboratory on prototyping from our university’s bachelor program on sustainable engineering with an extensive machine park consisting of 3D printers, milling machines, lasers and various hand tools is digitized and will finally be linked with the real-world lab. In addition to digitizing the basic process of product development and prototyping as part of students' project works, all additional activities arising in the lab are also transferred from the analog to the digital world. This digitalization is implemented alongside the already existing (partly browser-based) software tools of the individual devices in the e-learning platform Moodle. This results in a digital copy of the lab, its equipment and defined processes – structured in accordance with the established proceedings on product development (such as Pahl/Beitz and VDI 2221). We consider it a digital twin of the work and learning environment, calling it the “digital learning environment twin” of the real-world lab. For the product development process, a course area is available in Moodle with various feedback loops and assessment levels for the individual development steps of the student projects. Through this, students can submit their project plans, design ideas, sketches, CAD-models, manufacturing codes (such as G-codes for 3D printers, laser cutters and carving machines), or “just” request feedback and initiate meetings on technical and/or organizational topics of their product design process, the lab equipment, etc. Also, a safety instruction with instructional videos, PDF documents with hazard warnings and operating instructions as well as a final test (to allow operating the lab equipment) are provided to introduce the students to the lab. In this paper, we will illustrate the overall methodological approach on the established digital learning environment twin of the lab. Furthermore, we will have a detailed view on the challenge of transferring underlying manufacturing process to the digital world and linking them to provide a continuous digital workflow. The paper will be closed with an analysis of feedback (by both students and lecturers) on the pros and cons as well as on the usability of the digital twin of the lab.

In recent years multi-material Additive Manufacturing gained more interest as new applications were found in domains such as bioengineering, soft robotics and actuators, electronics and many more. Fused Filament Fabrication (FFF) is one of the most used AM technologies for multi-material 3D printing due to relatively low-cost equipment and a large variety of thermoplastic materials. Even so, issues are still to be solved regarding the adhesion mechanisms at the level of bond interface, especially for dissimilar materials. Therefore, this paper aimed to identify influencing factors over the printing process of multi-material parts at the interface level. The influencing factors were determined using a literature review and Cura slicing tool user guide and systematised using a cause-effect diagram. The main domains of influence are Materials bonding, Printing equipment, Model geometry, and Method of printing and processing. These domains were further split into more specific factors and discussed based on their influence over multi-material interface printing.

A new topology based feature extraction method for classification of interictal epileptiform discharges (IEDs) in EEG recordings from patients with epilepsy is proposed. After dimension reduction of the recorded EEG signal, using dynamical component analysis (DyCA) or principal component analysis (PCA), a persistent homology analysis of the resulting phase space trajectories is performed. Features are extracted from the persistent homology analysis and used to train and evaluate a support vector machine (SVM). Classification results based on these persistent features are compared with statistical features of the dimension-reduced signals and combinations of all of these features. Combining the persistent and statistical features improves the results (accuracy 94.7 %) compared to using only statistical feature extraction, whereas applying only persistent features does not achieve sufficient performance. For this classification example the choice of the dimension reduction technique does not significantly influence the classification performance of the algorithm.

Cloud Computing allows companies to scale seamlessly, providing a broad range of state-of-the-art services. Another promise is to free users from the operational and administrative burdens [1]. However, with the advent of cloud-native applications [2], this promise becomes questionable – especially when DevOps [3] principles are used during development. Experience from practice shows that teams often struggle dealing with both the infrastructure, finding the right architecture, and implementing business logic. When working in decentralized teams, things are even worse, as standardization across teams cannot be assumed. To tackle those issues, automation by means of techniques such as Infrastructure-as-code [4] help to ease to cope with infrastructural concerns. However, when working with several teams in a decentralized manner, operational overhead is still there. Organizations struggle with standardization of infrastructure code and there is no clear centralized visibility for security-related concerns within the development lifecycle. To address these issues, we propose two things: First, building up a Platform Team [5], which serves as an organizational structure for continuous delivery. A Platform Teams can be the size of a typical small DevOps Team and support the whole organization with standardized security-hardened modules. Second, an Ops-Platform is needed that is operated by the Platform Team to centrally provide and maintain those modules. Other Dev-Teams can then consume those modules. In this paper, we report insights from the implementation of this approach in practice. We find out that developers are 75% less focused on operations by using such a platform and name specific success factors.

This study demonstrates the need for novel gas engine control systems for com-
bined heat and power plants, also known as cogeneration power plants, connected to natural
gas grids. Hydrogen addition to natural gas grids in a range of up to 5% by volume is already
permitted throughout Europe. This offers the possibility to reduce carbon dioxide emissions
of end consumers connected to public natural gas grids and contributes to climate protec-
tion. However, conventional engine controls are not designed for natural gas/hydrogen mixture
operation. We tested fuels with up to 30% hydrogen by volume using a commercial six-cylinder
spark ignition engine, designed for natural gas or biogas operation in power plants. With engine
settings according to usual cogeneration operation, nitrogen oxide emissions increased expo-
nentially with increasing hydrogen amounts. We demonstrate that the usual approach of using
the lower heating value of the fuel mixture to regulate the engine is unable to accommodate the
hydrogen induced changes. For this reason, we developed a mathematical model to determine
the nitrogen oxide emissions based on boost pressure and power output. The idea behind this
novel approach is to regulate the engine based on emissions, regardless of the fuel gas. In this
work the approach for this virtual sensor is described and its performance demonstrated.

Purpose: Magnetic separation of microbes can be an effective tool for pathogen identification and diagnostic applications to reduce the time needed for sample preparation. After peptide functionalization of superparamagnetic iron oxide nanoparticles (SPIONs) with an appropriate interface, they can be used for the separation of sepsis-associated yeasts like Candida albicans. Due to their magnetic properties, the magnetic extraction of the particles in the presence of an external magnetic field ensures the accumulation of the targeted yeast.

Materials and methods: In this study, we used SPIONs coated with 3-aminopropyltriethoxysilane (APTES) and functionalized with a peptide originating from GP340 (SPION-APTES-Pep). For the first time, we investigate whether this system is suitable for the separation and enrichment of Candida albicans, we investigated its physicochemical properties and by thermogravimetric analysis we determined the amount of peptide on the SPIONs. Further, the toxicological profile was evaluated by recording cell cycle and DNA degradation. The separation efficiency was investigated using Candida albicans in different experimental settings, and regrowth experiments were carried out to show the use of SPION-APTES-Pep as a sample preparation method for the identification of fungal infections.

Conclusion: SPION-APTES-Pep can magnetically remove more than 80% of the microorganism and with a high selective host-pathogen distinction Candida albicans from water-based media and about 55% in blood after 8 minutes processing without compromising effects on the cell cycle of human blood cells. Moreover, the separated fungal cells could be regrown without any restrictions.

This study investigates the potential benefits and drawbacks of adding hydrogen to natural
gas grids on stationary cogeneration plants. Fuel blended with up to 30% hydrogen by
volume was tested using a commercial six-cylinder spark ignition engine designed for pure
natural gas operation without modifications to the engine. In line with normal practice for
cogeneration plant engines, the power output, the lower heating value of the air/fuel
mixture, the ignition timing and the engine speed were held constant. Results show that
increasing hydrogen concentration led to an earlier peak cylinder pressure, indicating
significantly accelerated combustion. As a result, peak pressures were up to 39% higher
than with natural gas and up to 10% of fuel burned before top dead center. Despite this,
thermal efficiency improved up to 6%. Cycle-by-cycle variation decreased by half, indi-
cating reduced misfires on account of hydrogen. However, nitrogen oxide emissions
increased exponentially with increasing hydrogen amounts. Our findings suggest that
hydrogen-enriched natural gas is a promising fuel for stationary cogeneration plants, but
modifications to engine control settings are necessary to ensure optimal performance and
compliance with nitrogen oxide emission regulations. These modifications might include
adjustments to the mixture control system and ignition timing.

Industrial requirements to design high quality products in shorter and shorter times impose the use of numerical models
to estimate the geometrical deviations of these products, which are assemblies, starting from the geometrical deviations
of their components. Numerical models may support this estimation activity, thus reducing the time to market and the
design costs.
The free-body model is an interesting numerical method to translate the tolerance chain into a static problem solved by
algebraic or graphical procedures by using the free-body diagrams of force analysis. This work presents a free-body
model that is able to deal with dimensional and geometrical tolerances that involve translations of the features to which
the tolerances are applied. Moreover, it validates the free-body model for tolerance analysis of rigid parts by defining and
solving a case study through numerical and experimental activities. The present work uses a case study to demonstrate
the effectiveness of the free-body model by underlining that the experimental results obtained are very close to those
from the numerical model.

In recent years the interest in finding new shape memory polymers has increased. Withal, fewer studies approached the use of regular materials such as polyethene terephthalate (PET), a widely available material. The research investigated the shape memory characteristics of regular PET 3D printed samples in two testing cycles. The recovery temperature was determined through dynamical mechanical, followed by tensile testing and heat treatment of the specimens. The results show PET has good shape memory properties, recording a slight increase in shape recovery during the second testing cycle without affecting the sample integrity. The thermal analysis of the recovered material shows that heat treatment time or/and temperature need to be improved to stabilise the PET material structure.

Dielectric properties for nanocomposites with metallic fillers inside a polymer matrix were determined using CST STUDIO SUITE—Electromagnetic field simulation software followed by the free-space Nicolson–Ross–Weir procedure. The structure is randomly generated to simulate the intrinsic non-uniformity of real nanomaterials. Cubic insertions were equated to corresponding spherical particles in order to provide either the same volume index or the same exterior surface index. The energy concentration around the inserts and within the entire material was determined as useful information in practice in order to design materials tailored to avoid exceeding the field/temperature limit values. The paper successfully associated the dialectic measurements with the results from the computer simulations, which are mainly based on energetic effects in electromagnetic applications. The experimental results are comparable with the software simulation in terms of precision. The conclusions outline the practical applications of the method for both electromagnetic shielding and microwave domain/telecommunications applications.

Cast iron components have a good strength to weight ratio. This leads to their frequent use in the wind industry. The design of cast iron components is currently based on the use of individual simulation tools and material data that is common to all components. In order to better exploit the lightweight potential of cast iron components, it is necessary to link the simulation software tools and thus take into account local material properties already in the design phase. This is described in this paper using the example of a large casting for the wind industry