67 Industrielle Fertigung
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Schwenkbiegen ist ein etabliertes Umformverfahren, bei dem Materialverlust vermieden und Ressourcen effizient genutzt werden. Der Prozess erfordert jedoch aufwändige Optimierungen, die bisher stark vom Fachwissen der Bediener abhängen. Dies führt zu hohem Zeit- und Materialaufwand, da Optimierungsschritte iterativ erfolgen. Angesichts des Fachkräftemangels ist eine technologische Aufrüstung der Anlagen im Sinne von Industrie 4.0 notwendig. Im Rahmen eines Projekts wurden mittels intelligenter Sensorik kritische Einflussfaktoren erfasst, die Korrelationen zwischen Produktfehlern und Anlagenverformungen aufzeigen. Darauf basierend wurde eine Methodik entwickelt, die die Grundlage für eine Inline-Kompensation schafft, bei der die Anlage eigenständig Prozessparameter anpasst, um Produktfehler zu korrigieren und perspektivisch eine fehlerfreie Fertigung ab dem ersten Bauteil zu ermöglichen.
Digitalisierungsprojekte helfen dem Anwender, komplexe Prozesse einfacher und effizienter darzustellen. Allerdings gibt es viele Hemmnisse, welche die Umsetzung deutlich erschweren. Zurückhaltung bei der Umsetzung ist spürbar. Dies trifft unter anderem Arbeitgeber und Arbeitnehmer, die durch das Warten oder Vermeiden ins ökonomische Abseits geraten können. Diese Beobachtungen lassen sich auf eine übergeordnete wissenschaftliche Leitfrage zurückführen: Welche Barrieren und systemischen Herausforderungen erschweren eine nachhaltige Transformation im Rahmen von Industrie 4.0, insbesondere unter Berücksichtigung menschlicher Arbeit in der Produktionstechnik? Welche Fragen stellen sich die betroffenen Akteure? Das wesentliche Ziel dieser langfristig ausgelegten Forschungsarbeit ist es, diese Fragen dezidiert und im Detail zu konkretisieren, um daraus ein konzeptionelles Fundament zu entwickeln, das Forschung, Lehre und technologische Entwicklung integriert und die Potenziale digitaler Technologien mit dem Erfahrungs- und Handlungswissen der Beschäftigten in der Produktion langfristig synergetisch verbindet.
Das Gewindeformen erfordert eine präzise Schmierstoffapplikation, da hohe Flächenpressungen und lokale Temperaturspitzen die Werkzeugbelastung erheblich beeinflussen. Aktuelle Sprüh- und Minimalmengenschmierungssysteme (MMS) weisen trotz etablierter Technik häufig Streuverluste, unzureichende Benetzung und instabile Tropfendynamik auf. Diese wissenschaftliche Betrachtung beinhaltet und untersucht einen integrativen Ansatz zur adaptiven Präzisionsbeölung beim Gewindeformen, der auf Computational Fluid Dynamics (CFD)-basierter Strömungsanalyse, experimenteller Validierung und Künstliche Intelligenz (KI)-gestützten Optimierungsverfahren basiert. Im Fokus stehen Tropfengröße, Strahlgeometrie, Düsenposition und Umgebungsströmung sowie deren Einfluss auf die Benetzungsintensität. Erste simulationsgestützteVoruntersuchungen zeigen das Potenzial einer datenbasierten Optimierung zur Reduktion von Benetzungsdefiziten und zur Auslegung künftiger Regelstrategien für eine ressourceneffiziente Schmierstoffapplikation.
Metal additive manufacturing (AM) using laser powder bed fusion (LPBF) is often associated with material and energy savings when compared to conventional manufacturing (CM). In industrial applications such as commercial vehicle components, structurally optimized designs can reduce environmental impacts – provided that increased complexity and build time do not negate these gains. Given that up to 80 % of a product’s environmental impact is determined during the design phase, prospective product carbon footprint (PCF) assessment tools could support more sustainable design choices. However, existing solutions remain insufficiently validated for use in metal AM, particularly regarding their scope, precision, and usability in real-world industrial workflows. This study examines the applicability of existing prospective PCF tools using simulated and physically produced parts. In a comparative pilot case study, a commercial bus component is optimized for AM using multiple design approaches and compared to an initial and an optimized CM variant. While the optimized CM parts are not physically produced, their resource demand is assessed using a combination of simulation and database information. For AM parts, process data including energy consumption, inert gas use, powder input, build time, and part weight is recorded and analyzed. The PCF for each design and manufacturing scenario is calculated, covering both production (cradle-to-gate) and use-phase emissions. Environmental impacts are evaluated using a classical life cycle assessment (LCA), considering functional equivalence between designs. The goal is to evaluate whether weight savings from AM-optimized designs translate into net life cycle CO2eq- reductions, and to assess the accuracy and usability of early-stage PCF prospection tools against detailed LCA results. The study highlights research gaps and integration challenges, laying the groundwork for extensive investigations.
This contribution investigates decentralized mechanisms for publishing and discovering Asset Administration Shell (AAS) endpoints across federated industrial dataspaces. Building on experiences from Catena-X, AAS Dataspace for Everybody (ADE), and MX-Port, the paper identifies key design patterns enabling secure, interoperable, and automated digital
twin communication. To address the growing need for cross-dataspace interoperability, a conceptual Dataspace Boundary Connector (DBC) is proposed that enables dataspace-level trust establishment, discovery access, and policy negotiation without centralized coordination. The proposed architecture supports scalable and data-sovereign endpoint federation across
heterogeneous ecosystems, contributing to the evolution of Industry 4.0 toward Industry 5.0 by empowering SMEs to participate effectively in self-organizing digital manufacturing networks.
Increasing environmental requirements demand more sustainable punching processes. This study investigates how minimal quantity lubrication (MQL) and CO₂-reduced, biodegradable lubricants can jointly lower the ecological footprint of metal forming without compromising tool life or productivity.
In industrial practice, lubrication is typically applied via felt rollers using conventional oils, often in excessive quantities. This approach increases material consumption and cleaning effort. Although MQL is a mature technology, it is still rarely implemented in punching. To evaluate its potential, two lubrication conditions were examined: MQL with standard oil and MQL with a CO₂-reduced, biodegradable oil. Tool wear, particularly at the punch edge, served as the main performance indicator and was compared to both traditional felt-roller lubrication and an additional dry punching reference.
Results show that MQL drastically reduces lubricant use while maintaining tool performance. Both oils achieved wear behaviour comparable to felt-roller lubrication, with the CO₂-reduced oil offering further ecological advantages through lower emissions and biodegradability. The dry reference exhibited accelerated wear and early tool failure, confirming the necessity of lubrication for process stability.
A CO₂ balance illustrates the environmental benefit: replacing conventional lubrication with MQL and CO₂-reduced oils can significantly reduce the overall carbon footprint of punching operations. The improvement results from lower lubricant consumption and the reduced carbon intensity of biodegradable oils. The findings demonstrate that combining MQL systems with CO₂-reduced lubricants enables more sustainable punching by aligning environmental responsibility with industrial efficiency.
Efficient production planning, machine optimization, and bottleneck analysis are critical for reducing cycle times in manufacturing processes. This paper introduces an automated process mining (PM) approach designed to streamline these tasks by generating and optimizing finite state machines (FSMs) from event logs derived from signal changes in industrial plants. Unlike conventional methods, this approach requires no prior knowledge of the configuration or behavior of the target programmable logic controllers (PLCs). The synthesized FSMs facilitate the extraction of process-oriented insights, enabling the identification of underperforming manufacturing processes and detailed analysis of their durations. The proposed method is validated through a case study on a real industrial plant, demonstrating its efficacy in uncovering process inefficiencies and supporting decision-making. This work provides a novel, generalizable framework for process analysis in manufacturing environments, contributing to the broader field of automated process optimization.
For several decades most of the engineering effort in glass has gone into different forms of joints. The predominant type of joint is the bolted joint in tempered glass. This type of joint has been shown to have severe limitations. Adhesive joints traditionally have had a large degree of mistrust because of the complications in manufacture, easy contamination and doubts about the moisture resistance. Three years ago DELO developed a new family of adhesives, the Glass Bond family. These are water resistant blue light/UV-a curing adhesives. It will be shown on the basis of several case studies that this type of adhesive has the potential to revolutionize glass structures.
Vibrations of thin sheet-metal parts during robotic manipulation on a production line create a number of serious challenges for production process planning. Modeling the behavior of an elastic plate or shell as a function of the robot manipulator trajectory is typically performed using the finite element method (FEM) and requires significant computational effort. The time factor remains a key limitation for integrating operations involving flexible parts into the virtual commissioning process. In this work, a methodology is proposed that enables accurate real-time reproduction of the behavior of an elastic part during linear robotic manipulation. The approach is based on modeling the response of an elastic part to a prescribed base excitation using the FEM and on the development of a reduced model compliant with the FMI/FMU standard. This reduced model computes, in real time, the convolution of the precomputed base response with the acceleration profile corresponding to the robot TCP trajectory. This makes it possible to determine the total cycle duration, which consists of the part transfer time and the time required for vibration decay at the end of the trajectory down to an acceptable threshold, as well as to perform collision checking while accounting for the deformation of the flexible part. As a result, operations involving elastic parts can be integrated into the virtual commissioning process.
Gegenwärtige Entwicklungen der Digitalisierung und Datenökonomie, insbesondere multilateraler Plattformen zum Datenaustausch, bieten das Potenzial für eine beschleunigte Umsetzung von Kreislaufwirtschaftspraktiken in der produzierenden Industrie. Der Beitrag untersucht systematisch und anhand originärer Forschung, inwieweit die Digitalisierung als Katalysator der Kreislaufwirtschaft im Beschaffungswesen solcher Unternehmen dienen könnte. Dafür wurden acht Experten aus fünf weltweit führenden Herstellern und Zulieferern der Automobil- und Luftfahrtbranche interviewt. Es werden praxisnahe Hypothesen für die nachhaltige Gestaltung von Lieferketten entwickelt und zwei spezifische Use Cases für Kreislaufwirtschaftspraktiken vorgeschlagen, die dem Ressourceneinsatz proaktiv entgegenwirken können.