Publication Server of Aschaffenburg University
Not a member yet
1531 research outputs found
Sort by
Surface roughness optimization of hybrid PBF-LB/M-built Inconel 718 using in situ high-speed milling
No full textAbstractWe report on the optimization of the surface roughness of hybrid additive manufactured Ni superalloys, combining a conventional laser powder bed fusion process with in situ high-speed milling. This remarkable hybrid approach has only recently been applied to different steel types and barely to Ni superalloys which opposite to steel appear to be challenging for milling processes, particularly within the powderbed of laser powder bed fusion. Different influencing factors on the surface roughness are varied in this study, following the Taguchi method. Their effect is evaluated with respect to the average surface roughness and the maximum surface roughness. The signal-to-noise ratio for the varied parameters infeed, z-pitch, feed rate, and spindle speed is calculated, determining their relevance on the surface roughness, and defining an optimal parameter combination. As the surface quality is optimized to \varvec{R_a=0.47\, \mu m}
R
a
=
0.47
μ
m
, the definition of the optimal parameter combination is of the highest relevance for the application of this novel manufacturing approach for Inconel. Using linear regression, the resulting surface roughness of these parameters is predicted, getting validated by the experimental evaluation. Due to a further analysis, including EDX analysis and a quantitative element analysis at different positions of the flank of the milling cutter, wear characteristics as well as the dissipation of the coating of the milling cutter are detected. The flank wear and the resulting breakage of the cutting edge are defined as the main reasons of a rising surface roughness
Ontology-Based Battery Production Dataspace and Its Interweaving with Artificial Intelligence-Empowered Data Analytics
No full textHeart disease, also known as cardiovascular disease, encompasses a variety of heart conditions that can result in sudden death for many people. Examples include high blood pressure, ischaemia, irregular heartbeats and pericardial effusion. Electrocardiogram (ECG) signal analysis is frequently used to diagnose heart diseases, providing crucial information on how the heart functions. To analyse ECG signals, quantile graphs (QGs) is a method that maps a time series into a network based on the time-series fluctuation proprieties. Here, we demonstrate that the QG methodology can differentiate younger and older patients. Furthermore, we construct networks from the QG method and use machine-learning algorithms to perform the automatic diagnosis, obtaining high accuracy. Indeed, we verify that this method can automatically detect changes in the ECG of elderly and young subjects, with the highest classification performance for the adjacency matrix with a mean area under the receiver operating characteristic curve close to one. The findings reported here confirm the QG method’s utility in deciphering intricate, nonlinear signals like those produced by patient ECGs. Furthermore, we find a more significant, more connected and lower distribution of information networks associated with the networks from ECG data of the elderly compared with younger subjects. Finally, this methodology can be applied to other ECG data related to other diseases, such as ischaemia
The Moderating Role of Non-Monetary Gamification in Reducing Algorithm Aversion in the Adoption of AI-based Decision Support Systems
No full textIntegrating artificial intelligence (AI) into decision-making processes is key to improving organizational performance. However, trust in AI-based decision support systems (DSSs), similar to other information systems, is important for successful integration. A disruptive phenomenon, “algorithm aversion”, can impede AI trust and, thus, acceptance. Although AI recommendations outperform human recommendations in different decision-making fields, individuals underweight recommendations from AI-based DSSs compared to human decision-makers due to a lack of AI trust. We conducted a lab experiment to investigate the role of AI recommendations in workplace-related tasks, first focusing on the mediating effect of AI trust and the negative impact of algorithm aversion on decision-making performance and the moderating effect of technical competence. Second, we analyzed the ability of gamification to reduce this phenomenon. We provide evidence regarding how to enhance decision-making performance when AI recommendations are deployed and identify countermeasures against algorithm aversion to facilitate the adoption of AI-based DSSs
Messtechnik und Prüfstände für Verbrennungsmotoren
No full textDieses Buch vermittelt sowohl Studenten, als auch Planern und Betreibern in Industrie und Wissenschaft das nötige umfangreiche Wissen, um Messungen an Motorenprüfständen durchführen zu können. Messtechnik und Prüfstände für Verbrennungsmotoren helfen, Kraftstoff einzusparen, Treibhausgase und Schadstoffe zu reduzieren, mit kleineren Motoren mehr Leistung abzugeben sowie Komponenten und Betriebsstoffe zu optimieren. Mit den Motoren und der Abgasgesetzgebung entwickelt sich auch die für die Entwicklung erforderliche mechanische, thermodynamische und Abgasmesstechnik weiter
Spaziergang in die Zukunft der Altenpflege: Wie Robis die Lebensqualität von Oldies verbessern könnten
No full textIn der Altenpflege herrscht Fachkräftemangel. Dieser beeinträchtigt Organisationen, Personal, Pflegebedürftige sowie Angehörige und erfordert innovative Lösungen. In der Berufspraxis entstand eine konkrete Forderung: Roboter, die mit den Älteren spazieren gehen. Dieser gehen wir nach. In diesem Beitrag skizzieren wir die Notwendigkeit einer Technologieinnovation in der Altenpflege aus wirtschaftspsychologischer Sicht, danach potenzielle Vorteile des begleitenden Roboters, seine Anwendungsszenarien, Hindernisse bei der Einführung und die notwendigen Entwicklungsschritte. Schließlich appellieren wir an die Praxis, die Technologieentwicklung zu unterstützen
Impact of Accidents on Traffic Congestions: A Bayesian Network Approach Using Real City Data
No full textTraffic congestion has been a major concern in urban areas due to its strong impact on various social, economic, and human safety sectors. Understanding the relationship and analyzing the trends and patterns between congestion and accidents can strengthen the strategy for reducing traffic congestion. Research on causes of accidents and their impact on congestion has recently been explored on a greater scale, but there is still a lot of scope for vast areas of improvement. To tackle this issue, we built a Bayesian Network (BN) model for analyzing and predicting congestion probability that can occur due to accidents. In this work, the complexity of handling real data obtained from Darmstadt city is described in detail. The accidents and congestion are correlated by introducing a novel threshold-based approach, which identifies congestion based on the change in vehicle density immediately following an accident. Different thresholds are explored to determine the most reliable measure of congestion, with the T4 threshold emerging as the optimal choice. Moreover, the proposed BN model is evaluated against several machine learning models, demonstrating competitive performance and its ability to understand the root cause of traffic congestion
