Purpose - This study aims to address challenges in the Laser Powder Bed Fusion process of polymers, focusing on the considerable amount of unsintered powder left post-printing. The objective is to understand the altered properties of this powder and find solutions to improve the process, reduce waste, and explore reusing reprocessed powder.

Design/methodology/approach - A novel methodology is employed to generate reprocessed powder without traditional printing, reducing time, cost, and waste. The approach mimics the aging effects during the printing process, providing insights into particle size distribution and thermal behavior.

Findings - Results reveal insights into artificial aging, showing an 8.2% decrease in particle size (60.256 - 69.183 μm) and a 9.1% increase in particle size (17.378 - 19.953 μm) compared to unsintered powder. Thermal behavior closely mirrors used powders, with variations in enthalpy of fusion (-0.55% to 2.69%) and degree of crystallinity (0.19% to 2.64%). The proposed methodology produces results that differ from those due to printing under 3% from a thermal point of view. The
new process reduces the time needed for aged powder, contributing to cost savings and waste reduction.

Originality/value - The study introduces a novel method for reprocessed powder generation, deviating from traditional printing. The originality lies in artificially aging powders, providing comparable results to actual printing. This approach offers efficiency, time savings, and waste reduction in the Laser Powder Bed Fusion process, presenting a valuable avenue for further research.

A reformulation of the dynamical component analysis (DyCA) via an optimization-free approach is presented. The original cost function approach is converted into a numerical linear algebra problem, i.e., the computation of coupled singular-value decompositions. A simple algorithm is presented together with numerical experiments to document the feasability of the approach. This methodology is able to recover the mixing and state matrices of multivariate signals from high-dimensional measured data fully.

Im Verlauf der Covid-19-Pandemie wurden in Deutschland nicht-pharmakologische Maßnahmen zur Infektionskontrolle entwickelt und implementiert. Dieser Beitrag analysiert die Akzeptanz sowie die Befolgung dieser Maßnahmen durch die deutsche Bevölkerung und untersucht statistisch signifikante Zusammenhänge in Bezug auf Alter, Geschlecht und Bildungsabschluss. Die zugrundeliegenden Daten wurden in einer qualitativen Befragung im Zeitraum März 2022 bis Mai 2022 erhoben. Die Ergebnisse zeigen drei statistisch signifikante Korrelationen: Die Wahrnehmung der AHA+L+A-Regel (Tragen von Alltagsmasken, Einhalten der Hygiene und Mindestabstands, Lüften, Nutzung der Corona-Warnapp) sowie die Nutzung einer Covid-19-App korrelieren mit dem Bildungsabschluss der Befragten, die Einstellung zur 3G-Regel (Zutrittserlaubnis bei vorhandener vollständiger Impfung, Genesung oder negativer Testung) korreliert mit dem Alter. Diese Ergebnisse können dazu beitragen, gezieltere Strategien zur Kommunikation und Entwicklung von Präventivmaßnahmen für den Bevölkerungsschutz zielgruppenspezifisch zu etablieren

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.

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.