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In dieser Arbeit wird ein Ansatz zur Unterstützung von Werkern, Meistern und Instandhaltern vorgestellt, der es ermöglicht, aus der auftretenden Situation heraus (ad hoc), auf aktuelle notwendige Informationen und die Zusammenhänge in einer variantenreichen Serienfertigung zuzugreifen. Schwerpunkt bildet das unternehmensneutrale Gesamtkonzept des fertigungsnahen Kontextinformationssystems, das aus dem Produktionsumgebungsmodell und der Systemarchitektur besteht. Das Produktionsumgebungsmodell beschreibt und vernetzt enthaltene Informationen und Zusammenhänge einer variantenreichen Serienfertigung. Hauptordnungskriterien sind hier die Zugehörigkeit zu einer bestimmten Gruppe (Typ), die Identität eines Gegenstands, dessen Ort und Betriebszustand über die Zeit. Die Systemarchitektur ist modular aufgebaut. Die Module werden in Erfassungsmodule, Kontextverwaltungsmodule, Funktionsmodule zur automatischen und manuellen Informationsfilterung sowie Präsentationsmodule untergliedert und kommunizieren über eine einheitliche Schnittstelle.
Der Digitale Zwilling ist ein Technologie-Trendthema mit großen Potenzialen in einer Vielzahl von Anwendungsbereichen – insbesondere für produzierende Unternehmen. Eine Studie des Reutlinger Zentrums Industrie 4.0 beschäftigt sich mit heutigen und zukünftigen Anwendungsmöglichkeiten von Digitalen Zwillingen und gibt Impulse für eine schrittweise Implementierung im Unternehmen.
The supply of customer-specific products is leading to the increasing technical complexity of machines and plants in the manufacturing process. In order to ensure the availability of the machines and plants, maintenance is considered as an essential key. The application of cyber-physical systems enables the complexity to be mastered by improving the availability of information, implementing predictive maintenance strategies and the provision of all relevant information in real-time. The present research project deals with the development of a cost-effective and retrofittable smart maintenance system for the application of ultraviolet (UV) lamps. UV lamps are used in a variety of applications such as curing of materials and water disinfection, where UV lamps are still used instead of UV LED due to their higher effectiveness. The smart maintenance system enables continuous condition monitoring of the UV lamp through the integration of sensors. The data obtained are compared with data from existing lifetime models of UV lamps to provide information about the remaining useful lifetime of the UV lamp. This ensures needs-based maintenance measures and more efficient use of UV lamps. Furthermore, it is important to have accurate information on the remaining useful lifetime of a UV lamp, as the unplanned breakdown of a UV lamp can have far-reaching consequences. The key element is the functional model of the envisioned cyber-physical system, describing the dependencies between the sensors and actuator, the condition monitoring system as well as the IoT platform. Based on the requirements developed and the functional model, the necessary hardware and software are selected. Finally, the system is developed and retrofitted to a simulated curing process of a 3D printer to validate its functional capability. The developed system leads to improved information availability of the condition of UV lamps, predictive maintenance measures and context-related provision of information.
The use of learning factories for education in maintenance concepts is limited, despite the important role maintenance plays in the effective operation of organizational assets. A training programme in a learning factory environment is presented where a combination of gamification, classroom training and learning factory applications is used to introduce students to the concepts of maintenance plan development, asset failure characteristics and the costs associated with maintenance decision-making. The programme included a practical task to develop a maintenance plan for different advanced manufacturing machines in a learning factory setting. The programme stretched over a four-day period and demonstrated how learning factories can be effectively utilized to teach management related concepts in an interdisciplinary team context, where participants had no, or very limited, previous exposure to these concepts.
Powder coatings provide several advantages over traditional coatings: environmental friendliness, freedom of design, robustness and resistance of surfaces, possibility to seamlessly all-around coating, fast production process, cost-effectiveness. In the last years these benefits of the powder coating technology have been adopted from metal to heat-sensitive natural fibre/ wood based substrates (especially medium density fibre boards- MDF) used for interior furniture applications. Powder coated MDF furniture parts are gaining market share already in the classic furniture applications kitchen, bathroom, living and offices. The acceptance of this product is increasing as reflected by excellent growth rates and an increasing customer base. Current efforts of the powder coating industry to develop new powders with higher reactivity (i.e. lower curing temperatures and shorter curing times; e.g. 120°C/5min) will enable the powder coating of other heat-sensitive substrates like natural fibre composites, wood plastic composites, light weight panels and different plastics in the future. The coating could be applied and cured by the conventional powder coating process (electrostatic application, and melting and curing in an IR-oven) or by a new powder coating procedure based on the in-mould-coating (IMC) technique which is already established in the plastic industry. Extra value could be added in the future by the functional powder toner printing of powder coated substrates using the electrophotographic printing technology, meeting the future demand of both individualization of the furniture part surface by applying functional 3D textures and patterns and individually created coloured images and enabling shorter delivery times for these individualized parts. The paper describes the distinctiveness of powder coating on natural fibre/ wood based substrates, the requirements of the substrate and the coating powder.
Zur Entwicklung einer Sofortpreiskalkulation für CNC-Drehteile werden Machine-Learning-Ansätze sowie ein deterministischer Algorithmus untersucht. Der deterministische Algorithmus funktioniert ausschließlich für Drehteile mit geringer Komplexität. Die Machine Learning Modelle hingegen sind zukunftsfähiger, da die ersten Ergebnisse bereits sehr geringe Abweichungswerte zu den festgelegten Referenzpreisen erreichen können. Mit steigendem Datenaufkommen können beide Machine-Learning-Modelle mit geringem Aufwand weiter verbessert werden.
Manufacturing has to adapt to changing situations in order to stay competitive.It demands a flexible and easy-to-use integration of production equipment and ICT systems. The contribution of this paper is the presentation of the implementation of the Manufacturing Integration Assistant (MIALinx). The integration steps range from integrating sensors over collecting and rule-based processing of sensor information to the execution of required actions. Furthermore, we describe the implementation of MIALinx by commissioning it in a manufacturing environment to retrofit legacy machines for Industrie 4.0. Finally, we validate the suitability of our approach by applying our solution in a medium-size company.
Driven by digital transformation, manufacturing systems are heading towards autonomy. The implementation of autonomous elements in manufacturing systems is still a big challenge. Especially small and medium sized enterprises (SME) often lack experience to assess the degree of Autonomous Production. Therefore, a description model for the assessment of stages for Autonomous Production has been identified as a core element to support such a transformation process. In contrast to existing models, the developed SME-tailored model comprises different levels within a manufacturing system, from single manufacturing cells to the factory level. Furthermore, the model has been validated in several case studies.
Process quality has reached a high level on mass production, utilizing well known methods like the DoE. The drawback of the unterlying statistical methods is the need for tests under real production conditions, which cause high costs due to the lost output. Research over the last decade let to methods for correcting a process by using in-situ data to correct the process parameters, but still a lot of pre-production is necessary to get this working. This paper presents a new approach in improving the product quality in process chains by using context data - which in part are gathered by using Industry 4.0 devices - to reduce the necessary pre-production.
In recent years, machine learning algorithms have made a huge development in performance and applicability in industry and especially maintenance. Their application enables predictive maintenance and thus offers efficiency increases. However, a successful implementation of such solutions still requires high effort in data preparation to obtain the right information, interdisciplinarity in teams as well as a good communication to employees. Here, small and medium sized enterprises (SME) often lack in experience, competence and capacity. This paper presents a systematic and practice-oriented method for an implementation of machine learning solutions for predictive maintenance in SME, which has already been validated.
