Refine
Document Type
- Article (4)
- Conference Proceeding (3)
Is part of the Bibliography
- yes (7)
Keywords
Institute
The results of the empirical study based on an AR Landolt C vision were examined to compare real and virtual visibility as a function of stimulus size on an HMD (e.g. MS HoloLens 2). A counterbalanced within-subject-design study with n = 32 probands was conducted. The results show on one hand a visibility loss between real and immersive systems by the same size of representation. On the other hand, a transfer function can be derived to ensure comparable visibility between the systems. This transfer function can be used to compensate for the visibility loss as a design guideline for immersive applications. In approximation, the Weber-Fechner's relation can be applied on AR visibility versus size of stimuli.
In the context of urban production and sustainable reuse of existing buildings, a detailed planning of the later usage is indispensable. One approach is to enable large-scale AR simulation on site with a sufficient Level of Detail (LoD) and stability. To determine performance metrics, a technology-stack is created and presented that enables a realistic field experiment in an industrial environment (area of 1,314 m2) using Microsoft HoloLens 2. For the experiment, a 3D model was instantiated as often as possible up to the limit of system stability and in different LoDs (100% down to 10%). The result shows that it is feasible to represent 2.63 million polygons (equivalent to about 1,909 m3 of augmented space) on LOD-35%; LoD-100% is equivalent to 327.38 m3 and 1,284 million polygons. Polygonal density [polygons/m3] is introduced as new indicator for better comparability when using 3D models. Thus, it is possible to immersively visualize urban production planning processes in large-scale scenarios. This expands the functional planning space of Urban Production and overcomes previous technical limitations.
Although the increasing use of automation in industry, manual assembly stations are still common and, in some situations, even inevitable. Current practice in manual assembly lines is to balance them using the takt-time of each workstation and harmonize it. However, this approach mostly does not include ergonomic aspects and thus it may lead to workforce musculoskeletal disorders, extended leaves, and demotivation. This paper presents a holistic human-centric optimization method for line balancing using a novel indicator ̶ the ErgoTakt. ErgoTakt improves the legacy takt-time and helps to find an optimum between the ergonomic evaluation of an assembly station and its balance in time. The authors used a custom version of the ErgoSentinel Software and a Microsoft Kinect depth camera to perform online and real-time ergonomic assessment. An optimization algorithm is developed to find the best-fitting solution by minimizing a function of the ergonomic RULA-value and the cycle time of each assembly workstation with respect to the worker's ability. The paper presents the concept, the system-setup and preliminary evaluation of an assembly scenario. The results demonstrate that the new approach is feasible and able to optimize an entire manual assembly process chain in terms of both, economic aspects of a well-balanced production line as well as the ergonomic issue of long term human healthy work.
Currently, many sources predict increasing use of AR technology in the industrial environment. The task of immersive productive assistance systems is to provide information contextually to the industrial user. Therefore, it is essential to explore the factors and effects that influence the visibility and the corresponding quality of this information. Caused by the technical limitations of additive display technology and application conditions, this new approach has evaluated the immersive visibility of Landolt Rings in various greyscales against ambient illuminance levels on different industrial-like surfaces, coupled with and without a white virtual background. For this purpose, an empirical study in a within-subjects-design with full factorial experimental design (n=23) was conducted on Microsoft HoloLens 2 hardware. The mean values of the main effects indicate that visibility is significantly affected by ambient illuminance (best results at lower level), greyscale (best results at middle level) and virtual background (best results with background). In contrast, the choice of surface is shown to have no statistically significant effect on visibility, however it affects the response time. Additionally, cross-interactions of variables were analyzed and lead to a design recommendation for immersive industrial applications.
Die Weiterentwicklung von Endgeräten wie z.B. von NReal oder die Microsoft HoloLens 2 im Bereich Augmented Reality (AR) führt zu einem erweiterten Nutzungsfeld von AR außerhalb klassischer Entwicklungs- und »Gaming«-Umgebungen. Eine der sich intensivierenden Nutzungsoptionen ist der wertorientierte Einsatz im industriellen Umfeld. In diesem Bereich bedarf es noch einer weitreichenden Identifizierung und Evaluierung der Einsatzpotenziale und des -nutzens [1]. Naheliegender Vorteil der Technologie ist die Anwendung in integrativen Planungsprozessen z.B. in der Anlagenplanung. Durch die Visualisierung von virtuellen Inhalten in einer realen Umgebung, können zukünftige Veränderungen auf mögliche Störfaktoren bereits vor Umsetzung detailliert geprüft und optimiert werden. Dies führt zu Zeit- und Kostenersparnis in Planungsphasen, da nachträgliche Änderungen signifikant kostenintensiver und komplexer sind. Eine weitere Nutzungsmöglichkeit ist die Simulation von Fertigungsprozessen und deren Auswirkungen. Die Simulation kann insbesondere bei der Identifikation unternehmensunabhängiger Probleme helfen. Auch kann der zeitliche Faktor, der jedem Prozess zugrunde liegt, eingebettet in einem virtuell erweiterten Realszenario geprüft werden. Als Beispiel kann hier die Störungsbeseitigung in industriellen Fertigungsprozessen dienen, welche meistens einer notwendigen engen Taktung unterliegen. Störungen sind häufig durch die direkte Umwelt oder Probleme in Arbeitsabläufen begründet. An diesem Punkt können die möglichen Optimierungsoptionen unter den gegebenen Bedingungen eine erste Validierung erfahren, ohne direkten Eingriff in laufende Prozesse. Diese Anwendungsmöglichkeiten gilt es zu untersuchen und in eine gebrauchstaugliche Lösung für den Einsatz im wertschöpfenden Bereichen zu überführen. Ziel dabei ist es die noch hypothetischen Potenziale zu bewerten und in einsetzbare Applikationen zu transformieren, die den Anforderungen in einer industriellen Umgebung standhalten. Um eine nutzerorientierte Forschung zu gewährleisten, ist es notwendig den Bedarf der Industrie zu erfassen. An diesem Punkt setzt das Vorhaben PlanAR an und untersucht die interdisziplinären Auswirkungen der Anwendungsmöglichkeiten und Applikationen bei industrienahen Nutzungsanforderungen.
Despite the increasing degree of automation in industry, manual or semi-automated are commonly and inevitable for complex assembly tasks. The transformation to smart processes in manufacturing leads to a higher deployment of data-driven approaches to support the worker. Upcoming technologies in this context are oftentimes based on the gesture-recognition, − monitoring or – control. This contribution systematically reviews gesture or motion capturing technologies and the utilization of gesture data in the ergonomic assessment, gesture-based robot control strategies as well as the identification of COVID-19 symptoms. Subsequently, two applications are presented in detail. First, a holistic human-centric optimization method for line-balancing using a novel indicator – ErgoTakt – derived by motion capturing. ErgoTakt improves the legacy takt-time and helps to find an optimum between the ergonomic evaluation of an assembly station and the takt-time balancing. An optimization algorithm is developed to find the best-fitting solution by minimizing a function of the ergonomic RULA-score and the cycle time of each assembly workstation with respect to the workers’ ability. The second application is gesture-based robot-control. A cloud-based approach utilizing a generally accessible hand-tracking model embedded in a low-code IoT programming environment is shown.
In the context of environmental protection, the construction industry plays a key role with significant CO2 emissions from mineral-based construction materials. Recycling these materials is crucial, but the presence of hazardous substances, i.e., in older building materials, complicates this effort. To be able to legally introduce substances into a circular economy, reliable predictions within minimal possible time are necessary. This work introduces a machine learning approach for detecting trace quantities (≥0.06 wt%) of minerals, exemplified by siderite in calcium carbonate mixtures. The model, trained on 1680 X-ray powder diffraction datasets, provides dependable and fast predictions, eliminating the need for specialized expertise. While limitations exist in transferability to other mineral traces, the approach offers automation without expertise and a potential for real-world applications with minimal prediction time.