FSP3: Produktion
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Qualitätsmanagement für Wirtschaftsingenieure: Qualitätsmethoden, Projektplanung, Kommunikation
(2016)
Dieses Lehrbuch für Studium und Praxis ist speziell abgestimmt auf Wirtschaftsingenieure. Es wird ein breites Wissen über die wesentlichen Elemente des Qualitätsmanagements dargestellt: vom Qualitätsmanagement in der Beschaffung, der Statistik, der ISO 9001, den Qualitätsmethoden, bis hin zum Projekt- und Risikomanagement und der Servicequalität. Zudem wird ein Schwerpunkt auf die interkulturellen Herausforderungen im internationalen Qualitätsmanagement gelegt. Zentral für den Wirtschaftsingenieur sind kommunikative Kompetenzen – sei es beim Umgang mit Fehlern, bei der Lösungssuche oder beim Aufbau eines Qualitätsmanagementsystems.
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.
Robots are often showcased as precise machines that seamlessly collaborate with humans. However, reality diverges significantly, as robots encounter failures that lead to suboptimum outcomes, misunderstandings and socially awkward situations. Conversely, human-induced errors in these interactions often go undetected by robots, amplifying the complexity of the interaction dynamics on top of the uncertainties in the environment and interaction contexts.
In human-robot interaction (HRI) research, errors–wrong actions that are made due to the lack of knowledge–and mistakes–actions that turn out to be wrong–are commonly viewed as impediments to achieving flawless collaboration. Scholars and practitioners aspire to meticulously control variables, creating environments with predictable storylines and outcomes. Nevertheless, the controlled setting of a laboratory rarely mirrors the unpredictable nature of real-world scenarios, contributing to a notable disparity between expectations and actual experiences. The ability of robots to navigate erroneous situations is paramount to the sustained success of HRI. These imperfections are also perfect learning opportunities for robots to continuously adapt to the ever-shifting complexity and dynamics in real-world HRI.
This Research Topic contains research that addresses the gap between anticipated perfection and the inherent uncertainties in a diverse range of real-world applications. The papers presented here shine light on a variety of aspects ranging from novel technical approaches to repair failures in HRI, to user studies that aim to understand social dimensions of errors in HRI.
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.
Despite the wide body of literature on motion planning for autonomous robots targeting structured agricultural environments, there remains a need for more efficient and reliable approaches for unstructured terrains. This work is motivated by a cooperation with the startup Paltech, which develops a weed-removal robot for grasslands. In this work, we employ a typical layered approach for robot navigation. However, in contrast to standard navigation tasks, such as point-to-point navigation, the global planner must solve the more challenging problem of visiting multiple targets in an optimal way while considering the kinematic constraints of the vehicle.
Die Managementkonferenz SUMMIT ALLGÄU Produktion fand 2025 bereits zum dritten Mal statt und hat sich als feste Plattform „von der Industrie für die Industrie“ etabliert. Die begleitende Publikation bündelt die Beiträge der Veranstaltung und greift zentrale Themen des industriellen und gesellschaftlichen Wandels sowie verantwortlichen Handelns auf.
Angesichts tiefgreifender ökologischer, ökonomischer und digitaler Transformationen wird der Dialog zwischen Wirtschaft, Industrie und Unternehmertum als entscheidend hervorgehoben. Produktion wird dabei nicht mehr nur als effiziente Herstellung verstanden, sondern als gestaltendes Element nachhaltiger Wertschöpfung.
Im Fokus stehen neue Denkweisen industrieller Wertschöpfung zwischen Digitalisierung und Dekarbonisierung, globaler Vernetzung und regionaler Verantwortung sowie Effizienz und Resilienz. Besondere Bedeutung kommt der Integration von Mensch, Technik und Daten in lernende Systeme zu.
Unter den Leitthemen „Im Fokus der Zukunft“ und „Die Kraft der Veränderung“ wurden beim SUMMIT 2025 aktuelle Erkenntnisse, visionäre Strategien und praxisnahe Erfahrungen diskutiert. Ziel ist es, den Wandel aktiv und verantwortungsvoll zu gestalten.