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.

The ARC 2024 is the thirteenth edition of the conference of the Master’s program in Applied Research in Engineering Sciences (M-APR), hosted by Technische Hochschule Nürnberg Georg Simon Ohm. With a total of 115 presentations and 40 posters, the ARC 2024 conference is a remarkable collection of research by M-APR students from nine cooperating Bavarian universities of applied sciences. The conference provided an exceptional platform for networking and collaboration. In addition, the organizers expect many interesting conversations and fruitful interactions during the breaks and poster sessions, as well as active participation in the discussions during the paper sessions. The M-APR degree program focuses on teaching skills for scientific work in the field of R&D. In project-oriented studies, students work on and research an engineering problem over 3 semesters. The ARC is the platform where students and lecturers exchange the results of this work. Under the supervision of a professor, each student works on his or her own research topic in close collaboration with other researchers over a period of one and a half years. Each summer, the students present their results to a wide audience made up of almost all students from all M-APR programs at the cooperating universities. This helps them to advance their own scientific careers and to learn from their peers what problems are being tackled in different fields. As applied research is a key issue in solving new challenges in the current unstable world, this is a small building block in solving these challenges.
We would like to thank the supporters of the conference, the session chairs and the colleagues from the cooperating universities. We would also like to thank Prof. Dr. Tilman Botsch, Prof. Dr. Frank Pöhlau and Prof. Dr. Tim Hosenfeldt who contributed to the conference with their keynote speeches.

This study describes the development of an operation-independent simulation model for an electrified die-casting foundry that uses a smart grid system to meet its energy needs. The model uses real weather and stock exchange electricity price data for the simulation period. The model can be used to determine and compare the cost of electricity for production at a given time (time of day and season) as well as the economics of different PV system and electricity storage options. It is also possible to analyze the share of different energy sources for each configuration. This can be done for sites throughout Germany. In addition, exemplary simulation studies are presented in this paper which demonstrate the wide range of applications of the model. The results provide an initial overview of the potential for savings and optimization. In the future, the model will provide a basis for determining optimum plant layouts and production times by means of simulation-based optimization.

Additive manufacturing has transformed the production process by enabling the construction of components in a layer-by-layer approach. This study integrates Artificial Neural Networks to explore the nuanced relationship between process parameters and mechanical performance in Fused Filament Fabrication. Using a fractional Taguchi design, seven key process parameters are systematically varied to provide a robust dataset for model training. The resulting model confirms its accuracy in predicting tensile strength. In particular, the mean squared error is 0.002, and the mean absolute error is 0.024. These results significantly advance the understanding of 3D manufactured parts, shedding light on the intricate dynamics between process nuances and mechanical outcomes. Furthermore, they underscore the transformative role of machine learning in precision-driven quality prediction and optimization in additive manufacturing.

In times of digitalisation, visual assistance systems in assembly are increasingly important. The design of these assembly systems needs to be highly complex to meet the requirements. Due to the increasing number of variants in production processes, as well as shorter innovation and product life cycles, assistance systems should improve quality and reduce complexity of assembly processes. However, many large kitchen manufacturers still assemble kitchen cabinets manually, due to the high variety of components, such as rails and fittings. This paper focuses on the analysis and evaluation of virtual assistance systems to improve quality and usability in individualised kitchen cabinet assembly processes at a large German manufacturer. A solution is identified and detailed.

ABSTRACT

Biomimicry is a new science that examines natural models, takes inspiration from them, and imitates designs or processes to solve problems and create innovations. Thus, it creates a field of work that aims to produce products that are functional, sustainable, do not harm the environment, and are economical and durable, taking nature and its cycle as an example. Biology and design work go hand in hand with biomimicry applications to develop products using living organisms such as algae, bacteria, and fungi. Algae are photosynthetic organisms in almost all ecosystems, including various aquatic environments and land. Algae are classified as macroalgae or microalgae according to their size and morphological characteristics. Microalgae are microscopic, primarily unicellular, or simple multicellular, and prokaryotic or eukaryotic. Many microalgae can adapt to extreme conditions such as a wide range of temperatures, salinities, pH values, and light intensities. Microalgae convert carbon dioxide into valuable biologically active compounds through solar energy. Thus, they constitute one-third of the total biomass worldwide and contribute 50% of atmospheric oxygen. Secondary metabolites may consist of 50-70% protein and more than 10- 30% lipids and vitamins, in varying amounts depending on the species. Microalgae form a central basis for achieving sustainability. They accomplish this by reproducing rapidly, producing important products, and accumulating lipids and carbohydrates as storage materials to produce biofuels.

The functional properties of microalgae have directed the attention of researchers, engineers, and architects from different disciplines to investigate the use of microalgae for sustainability. Designers have begun to use microalgae, influenced by their life processes, functions and physiological structures in nature, to serve as a sustainable solution. These include creating oxygen-rich areas, producing, and developing transparent roof and exterior coating systems that can be used to help regenerate urban environments, biodegradable pigments for textile prints, lighting, biofilms and fillers. The focus of this study is to underline how microalgal systems can be used in industrial and technological applications by modelling microalgal systems through biomimicry applications and bioengineering. Thus, we aim to reveal how a versatile contribution can be made to sustainability using microalgae in biomimicry designs.

The current energy crisis and high fossil fuel costs are challenging energy intensive industries such as non- ferrous foundries. It is therefore important to promote the transition to renewable energy sources with the electrification of melting units. This pilot study is the first to simulate the transition of conventional foundries to sustainable technologies. For this purpose, a simulation model based on a selected example company is developed. It takes into account 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. The effects of efficiency as well as energy costs and emissions are also taken into account.