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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.
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.
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.
In smart factories, maintenance is still an important aspect to safeguard the performance of their production. Especially in case of failures of machine components diagnosis is a time-consuming task. This paper presents an approach for a cyber-physical failure management system, which uses information from machines such as programmable logic controller or sensor data and IT systems to support the diagnosis and repairing process. Key element is a model combining the different information sources to detect deviations and to determine a probable failed component. Furthermore, the approach is prototypically implemented for leakage detection in compressed air networks.
The flexible and easy-to-use integration of production equipment and IT systems on the shop floor becomes more and more a success factor for manufacturing to adapt rapidly to changing situations. The approach of the Manufacturing Integration Assistant (MIALinx) is to simplify this challenge. The integration steps range from integrating sensors over collecting and rule-based processing of sensor information to the execution of required actions. This paper presents the implementation of MIALinx to retrofit legacy machines for Industry 4.0 in a manufacturing environment and focus on the concept and implementation of the easy-to-use user interface as a key element.
Additive manufacturing is a key technology which applies the ideas of Industry 4.0 in order to enable the production of personalized and highly customized products economically. Especially small and medium sized companies often lack the competence and experience to evaluate objectively and profoundly the potential of additive manufacturing technologies in small and medium sized companies. Furthermore, the method has been validated in a small medical technology company evaluating the additive manufacturing potential of an existing surgery tool.
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.