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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.
Advances in machine learning detecting changeover processes in cyber physical production systems
(2020)
The performance indicator, Overall Equipment Effectiveness (OEE), is one of the most important ones for production control, as it merges information of equipment usage, process yield, and product quality. The determination of the OEE is oftentimes not transparent in companies, due to the heterogeneous data sources and manual interference. Furthermore, there is a difference in present guidelines to calculate the OEE. Due to a big amount of sensor data in Cyber Physical Production Systems, Machine Learning methods can be used in order to detect several elements of the OEE by a trained model. Changeover time is one crucial aspect influencing the OEE, as it adds no value to the product. Furthermore, changeover processes are fulfilled manually and vary from worker to worker. They always have their own procedure to conduct a changeover of a machine for a new product or production lot. Hence, the changeover time as well as the process itself vary. Thus, a new Machine Learning based concept for identification and characterization of machine set-up actions is presented. Here, the issue to be dealt with is the necessity of human and machine interaction to fulfill the entire machine set-up process. Because of this, the paper shows the use case in a real production scenario of a small to medium size company (SME), the derived data set, promising Machine Learning algorithms, as well as the results of the implemented Machine Learning model to classify machine set-up actions.
In the last decade many different additive manufacturing (AM) technologies for metal, plastic or ceramic processing raise from research to commercialization. As a result, AM grows into different business areas and transforms structures and processes. Hence, the contribution tends to show the change in added values though the availability of different additive manufacturing technologies based on a technology screening and market research. Regarding the named purpose, a broad market research of 83 companies and 339 printer models has been conducted to find patterns of AM technology market share and regions to structure indicators such as accuracy by processed material classes with a specified AM method. Printing materials as metal, plastic, ceramic and carbon have been considered. The categorization is done by the AM principles: power bed fusion, material extrusion, vat photopolymerization and …
Eines der zentralen strategischen Ziele unserer Hochschule ist die Internationalisierung, sowie der »internationalisation@home«. Als die weltweite Corona-Pandemie die Präsenzlehre und -forschung ebenso wie den internationalen Austausch von Studierenden und Forschenden zu Beginn 2020 quasi zum Erliegen brachte wurden die Rufe nach digitalen Angeboten im internationalen Bereich schnell laut. Vor diesem Hintergrund reagierte der »Deutsche Akademische Auslandsdienst (DAAD)« mit der kurzfristig ins Leben gerufenen Förderlinie »International Virtual Academic Collaboration« (IVAC), um internationale Hochschulkooperationen und weltweite Mobilität unter digitalen Vorzeichen strategisch zu gestalten und auszubauen [1].
Mit einem Umsatz von 103 Milliarden Euro ist die Metallindustrie eine der größten deutschen Industriebranchen. Diese ist von volatilen Marktbedingungen und hohem Wettbewerb geprägt [1][2]. Kleine und mittlere produzierende Unternehmen (sogenannte KMU) sehen zunehmend gravierende Probleme bei der Einhaltung von Lieferterminen bedingt durch hohe Durchlaufzeiten in der Produktion [3]. Neben kaufmännischen Planungssystemen zur Erstellung von Produktionsplänen nutzen Unternehmen als Planungsgrundlage weiterhin Excel mit 31 % und manuelle Prozesse mit 10 % [4]. Gleiches gilt für Produktwechselvorgänge auf Maschinen (Rüsten). Aufgrund dieser Aspekte ist es notwendig, die Rentabilität der KMU in der Metallindustrie zu steigern. Das wird durch effiziente Produktionsplanung und -steuerung, sowie der daraus resultierenden hohen Reaktionsfähigkeit und Flexibilität realisiert. Daher ist die Produktionsplanung auf die Markt- und Kundenanforderungen und die Anlageneffektivität auf ein hohes und stabiles Niveau auszurichten [5]. Hier bietet die Erfassung von Echtzeitdaten eine adäquate Reaktion auf die genannten Anforderungen. Ebenfalls liefert sie großes Potenzial für die Produktionsplanung und -steuerung, um die Disposition und Koordination von Arbeitsaufträgen zu optimieren. Weiterhin werden Störgrößen oder unvorhergesehene Planungsabweichungen reduziert [4][6]. Zusätzlich ist eine erhöhte Transparenz und Verbesserung menschlicher Entscheidungsprozesse notwendig. Dies kann durch datengetriebene Methoden unterstützt und sichergestellt werden [7]. Ein Ansatz zur Optimierung des Produktionsergebnisses kann durch die Erhöhung der Anlagenproduktivität selbst realisiert werden. Dazu muss die Verfügbarkeit der Anlagen durch Lokalisierung und Reduzierung von Verlusten erhöht werden. Die Umrüstungsprozesse tragen stark negativ zur Verfügbarkeit einer Produktion bei. Eine Steigerung der Gesamtanlageneffektivität (overall equipment effectiveness oder kurz OEE) in einer Fertigungsumgebung ist jedoch möglich durch eine intelligente Nutzung von Sensordaten mit Techniken wie z. B. Machine Learning (ML).
Computerized Numeric Control (CNC) plays an essential role in highly autonomous manufacturing systems for interlinked process chains for machine tools. NC-programs are mostly written in standardized G-code. Evaluating CNC-controlled manufacturing processes before their real application is advantageous due to resource efficiency. One dimension is the estimation of the energy demand of a part manufactured by an NC-program, e.g. to discover optimization potentials. In this context, this paper presents a Machine Learning (ML) approach to assess G-code for CNC-milling processes from the perspective of the energy demand of basic G-commands. We propose Latin Hypercube Sampling as an efficient method of Design of Experiments to train the ML model with minimum experimental effort to avoid costly setup and implementation time of the model training and deployment.
On the way to climate neutrality manufacturing companies need to assess the Carbon dioxide (CO2) emissions of their products as a basis for emission reduction measures. The evaluate this so-called Product Carbon Footprint (PCF) life cycle analysis as a comprehensive method is applicable, but means great effort and requires interdisciplinary knowledge. Nevertheless, assumptions must still be made to assess the entire supply chain. To lower these burdens and provide a digital tool to estimate the PCF with less input parameter and data, we make use of machine learning techniques and develop an editorial framework called MINDFUL. This contribution shows its realization by providing the software architecture, underlying CO2 factors, calculations and Machine Learning approach as well as the principles of its user experience. Our tool is validated within an industrial case study.