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Eine zunehmende Anzahl von Planungssystemen spielt ihre Inhalte auf digitalen Kollaborationsplattformen aus. Neben Echtzeit-Kollaboration, fotorealistischer Visualisierung und KI-gestützten Anwendungen liegt der größte Mehrwert in der intelligenten Vernetzung spezialisierter Autorensysteme. Am Beispiel dreier Planungsgewerke und -systeme wird solch eine Integration aufgezeigt. Dadurch entstehen durchgängige digitale Planungsprozesse, konsistente Digitale Zwillinge sowie neue Potenziale für die kooperative, interdisziplinäre Fabrikplanung.
Automated sheet metal handling in the automotive industry using robot manipulators is a standard in modern production. However, the desire of automotive companies to speed up the production process on the assembly line and at the same time to reduce expensive hardware components poses new challenges for robotics. Excessively rapid movement of flexible parts or sheet metal can either lead to its plastic deformation or increase the decay time of the vibrations to such an extent that it is necessary to wait before the part can be further processed, for example by welding. The traditional approach to solving these problems is to add fixing points for the part and/or to allow for waiting times at the end of the robot movement to ensure that the sheet metal vibration subsides. This means that more effort than necessary has to be put into the hardware setup or a poor cycle time has to be accepted. In our work, we propose to improve virtual commissioning systems by conducting a deeper analysis of the dynamics of sheet metal parts during its movement by the robot. To achieve this, a three-step optimization strategy is proposed. The first step is a structural transient analysis of the thin metal part under disturbances that arise during its movements. The second step is the creation of a substitute model, which is trained on the basis of the data obtained in the first phase and considerably reduces the computing time required compared to the time needed for a finite element method (FEM) simulation. Subsequently, this is used to optimize robotic handling efficiency by optimizing the robot trajectory. By addressing the challenges posed by sheet metal dynamics, enhanced process control, reduced cycle times, and ultimately, improved manufacturing outcomes are anticipated.
This study presents a method for early identification of AM-suitable components and circular design routes. A structured, data-lean questionnaire with AHP-based weighting, combined with rule-based R-strategy identification and an AM-specific feasibility gate, enables transparent screening in early development. Demonstrated on a structural bus component for both diesel and electric operation, the approach proposes a dominant R-strategy and integrates a prospective LCA-in-the-loop workflow, showing how early circularity signals and life-cycle feedback inform robust redesign decisions.
Dense correspondence estimation is an essential computer vision task, however, existing methods often strug-gle to produce accurate and reliable dense flow fields when encountering significant occlusions. This is primarily due to scenes with high depth complexity and wide baselines between input images. To address this challenge, MH-Flow is introduced, a novel approach specifically designed to generate occlusion-robust dense flow fields from two to four images. It leverages iterative dense feature matching to perform a multi-homography (MH) decomposition for an image pair. This decomposition allows to effectively detect and analyze occluded and non-occluded pixel regions. Furthermore, MH-Flow incorporates a multi-view strategy and matching transitivity to extrapolate flow information from two additional input views and to enhance robustness, particularly in challenging occluded areas. Experimental results across multiple datasets demonstrate MH-Flow’s significant superiority in handl ing large viewpoint changes and occlusions compared to leading CNN-based approaches, achieving more accurate and complete dense flow fields.
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.
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.
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.
In event-based control systems, actuator operations are typically triggered by physical sensor signals to ensure process safety. However, this can lead to systematic delays, especially in pneumatic systems, due to system-related reaction times. This paper presents a method to compensate such delays by statistically advancing control signals without the need for additional sensors.
A bidirectionally coupled digital twin, based on virtual commissioning models, continuously evaluates live data and derives optimized advance signals during operation. The method integrates event-based control with time-based triggering and employs a hybrid approach combining empirical quantiles with analytical confidence intervals to maintain safe operation. Validated in a testbed under realistic industrial conditions, the approach achieves an average time saving of 315 ms per compensated transition. The system architecture decouples real-time PLC execution from non-real-time analysis, enabling scalable integration with diverse control and simulation platforms. The results demonstrate the potential of digital twins for active, data driven optimization in discrete manufacturing systems.
Fehlende Interoperabilität proprietärer Digitaler-Zwilling-Lösungen hemmt die Skalierung in der Industrie. Um dieses Problem zu lösen, analysiert diese Studie erstmals Deutschlands Asset Administration Shell (AAS), auch bekannt als Verwaltungsschale, durch Befragung von über 50 Manufacturing-X und KoPa35c Projekten, an denen mehr als 200 Unternehmen beteiligt sind. Der internationale Vergleich mit den USA, China und Japan zeigt dabei: Nur Deutschland führt sowohl bei Frameworks als auch Umsetzung. Dies resultiert in der Etablierung der AAS als IEC-Standard mit praktischer Implementierung in Forschungsprojekten.
