Simulationsmodelle der virtuellen Inbetriebnahme dienen dazu, Steuerungssysteme in einer digitalen Umgebung zu testen und Kollisionen frühzeitig zu erkennen. Für eine sichere Kollisionsvermeidung sind aber Modelle mit Online-Anbindung erforderlich, da Offline-Modelle durch laufende Hardware- und Softwareanpassungen ihre Gültigkeit verlieren. Ihre Entwicklung ist aufwendig, jedoch können viele Bestandteile aus bestehenden Offline-Modellen übernommen werden. Diese Arbeit beschreibt ein Vorgehensmodell zur systematischen Überführung und veranschaulicht es an einem Anwendungsfall.

Future Software-defined Value Networks enable data-driven innovation, diverse optimization opportunities, and resilient production environments. While significant progress has been made in standardizing communication protocols and data models, practical Industry 4.0 implementations require iterative prototyping and continuous refinement based on real-world experience. This paper presents a testbed designed to develop and explore industrial-scale software-defined manufacturing use cases, such as capability-based production. The testbed maps a full product life cycle within a circular economy context. Seven independent companies are interconnected within a data space, each contributing services like engineering, logistics, or production using robots or milling machines. Built on industry standards such as the Asset Administration Shell and OPC UA, the testbed demonstrates vertical and horizontal integration, enabling seamless cross-company automation. Following an overview of the conceptual setup, the testbed components are detailed.

Accurate monitoring and control of contact forces are essential in many robot manufacturing processes. Conventional approaches rely on force–torque sensors mounted between the robot wrist and end-effector, as existing estimation methods often suffer from limited bandwidth and accuracy. Building upon a previously introduced moving horizon estimator (MHE) for external torque estimation in industrial robot joints equipped with secondary encoders, this paper focuses on the impact of different modeling concepts on estimation performance. Specifically, various nonlinear stiffness models of a cycloidal drive are investigated using a dedicated test bench representing an isolated robot joint. The models are compared in terms of estimation accuracy and computational effort. Furthermore, the embedded implementation of the MHE is evaluated across multiple optimization frameworks to assess its real-time feasibility for industrial applications.

This work presents a concept and implementation for the secure storage and transfer of quality-relevant data of milled workpieces from online-quality assurance processes enabled by real-time simulation models. It utilises Non-Fungible Tokens (NFT) to securely and interoperably store quality data in the form of an Asset Administration Shell (AAS) on a public Ethereum blockchain. Minted by a custom smart contract, the NFTs reference the metadata saved in the Interplanetary File System (IPFS), allowing new data from additional processing steps to be added in a flexible yet secure manner. The concept enables automated traceability throughout the value chain, minimising the need for time-consuming and costly repetitive manual quality checks.

Abstract On the path to a sustainable transformation of the built environment, the concrete industry requires solutions that integrate mass customization with rigorous resource efficiency. This paper presents the results of SPP 2187 Subproject 12, which focuses on the development of an automated, zero‐waste formwork production cycle for complex lightweight concrete components. The technology utilizes Selective Binder Activation (SBA)—a powder‐bed‐based additive manufacturing process—to create water‐soluble sand formworks from a mixture of silica sand and organic binder. Key developments include a large‐scale 3D printing unit, a transparent CAD‐to‐machine‐code computational workflow, and a robust material reclamation strategy that enables near‐complete recycling of the formwork material. The technology's scalability and structural viability are validated through two prototypes: a 1.2 m functionally graded reinforced concrete beam and the Marinaressa Coral Tree, a 3.2 m tall architectural demonstrator exhibited at the 2023 Venice Architecture Biennale. The results demonstrate that this circular manufacturing approach can reduce concrete consumption by 60% while eliminating formwork waste during production and reach a production output of 34 l/h, effectively placing it within the performance range of current industrial standards.

This paper presents a method for dynamic adjustment of cable preloads based on the actuation redundancy of \acp{CDPR}, which allows increasing or decreasing the platform stiffness depending on task requirements. This is achieved by computing preload parameters with an extended nullspace formulation of the kinematics. The method facilitates the operator's ability to specify a defined preload within the operation space. The algorithms are implemented in a real-time environment, allowing for the use of optimization in hybrid position-force control. To validate the effectiveness of this approach, a simulation study is performed, and the obtained results are compared to existing methods. Furthermore, the method is investigated experimentally and compared with the conventional position-controlled operation of a cable robot. The results demonstrate the feasibility of adaptively adjusting cable preloads during platform motion and manipulation of additional objects.

The increasing digitization of manufacturing systems demands new strategies for quality assurance during the operational phase of computer numerically control (CNC) machines. This paper presents a novel approach to online-quality assurance using operational-parallel real-time simulation and point cloud comparison techniques. During machining, a real-time simulation generates a virtual representation of the manufactured workpiece, reflecting the actual position values and process conditions. This simulated point cloud is then compared directly to the nominal CAD model to detect geometric deviations early in the production process. Unlike conventional approaches relying on post-process optical measurements, this method enables in-process evaluation without interrupting operations. It supports fast, non-invasive quality assessment and facilitates adaptive process control. The work presented in this study presents an evaluation of different point cloud comparison methods for an online-quality assurance framework.

Virtual Commissioning (VC) is an increasingly integral practice in the engineering of production systems, evidenced by growing research and industrial adoption. Advancements in VC model fidelity, computational performance, and automated generation are improving the validation of complex production systems and point toward further applicability beyond conventional control software validation. In industrial practice, however, VC models are still confined to their original use. While emerging applications for VC models in the operational phase highlight the potential for model reuse, a structured method for transforming these valuable engineering assets into Digital Twins (DTs) is still absent. This paper addresses this gap through three main contributions: first, by clarifying the VC/DT boundary within production contexts and identifying open research questions; second, by introducing a novel six-phase transformation method; and third, by identifying industrially-derived use cases and their specific requirements. The proposed method adopts the Design Science Research (DSR) methodology to systematically structure the problem space, derive actionable objectives, and design the iterative development process. Initial results include a set of industrial use cases with their corresponding requirements, as well as the exemplary execution of one development cycle. Finally supplemented by a qualitative cost-benefit analysis, this work offers practical guidance for realizing VC-based DTs that support operational tasks in production environments.

This paper presents a new jerk-limited feedrate optimization algorithm for spline toolpaths with a regularized time-scaled nonlinear programming formulation, to increase the productivity of multi-axis machines with serial kinematics, such as machine tools or industrial robots. By combining the quadratic programming based solution with a pseudo-jerk estimate as its warm start, kinematic limits in both joint and Cartesian space can be handled directly in a time-discretized form without the need for arc length parameterization of toolpaths. The original time-optimal formulation is extended to a regularized form using integral smoothing to improve the smoothness of the feedrate profile. The associated optimization problem is formulated in a sparse form by direct transcription and can be solved efficiently and reliably using the interior point method. In addition, designed for its use in industrial CNC applications, a sequential windowing strategy is introduced to deal with long toolpaths with a large number of constraint checkpoints. This strategy allows its potential integration with the look-ahead functionality in a real-time capable CNC kernel. The constraint feasibility and the computational efficiency of the proposed algorithm are validated on a 6 DOF KUKA industrial robot kinematics with butterfly-shaped NURBS toolpaths. The computation time is kept well below the actual finishing time (about 10% measured on an i9 CPU with single core execution), and shows an almost linear dependence on the number of constraint checkpoints. The planning performance and computational robustness have also been validated experimentally on a milling machine following a 3D flower-shaped freeform toolpath with 10000 constraint checkpoints. Compared to an industrial numerical control kernel using an S-curve profile-based planning approach, the proposed algorithm reduces both the finishing time and the mean Cartesian contour error in the 3D tracking experiment by 17% and 21% respectively.

Virtual Commissioning (VC) marks the final engineering phase of machine tools, enabling convenient software testing and qualification. To efficiently create co-simulation models, VC demands consistent, inter-linked cross-domain engineering data. However, mechanical and electrical design, as the main data sources of VC, are fundamentally different domains, providing heterogeneous information. To address this challenge, this paper presents an ontology-based method that integrates MCAD and ECAD using an RDF knowledge graph and the Asset Administration Shell (AAS). A simplified domain ontology for VC is introduced that consists of the three sub-domains mechanics, electrics and simulation and links them accordingly. When applied in a knowledge graph, AAS engineering data from MCAD and ECAD can be semantically lifted and linked. Thereby, the concept enables a harmonized and integrated semantic representation of engineering data for VC. Compared to file-based integration, the presented approach aims to replace manual integration steps and establish a knowledge base for efficient simulation model creation.

This paper presents a method for 3D printing bio-concrete components with a compacted, heterogeneous sand mixture, to achieve geometrically complex biomineralized spatial structures with high compressive strength. The presented process uses a moist sand mixture (0.063–2 mm) and additional compaction to achieve high packing density. Bacteria suspension was selectively applied along predetermined paths by a peristaltic pump. Parameter studies established that a CaCl2 fixation solution concentration of 0.2 M and bacteria dispensing rates between 4 and 12 μL/mm were suitable to control bacterial localization and diffusion depth within the sand mixture. Unconfined compressive strength tests on 3D printed small-scale cylinders (D × h = 25 × 30 mm) yielded mean unconfined compressive strength values of 11 MPa and 17 MPa. A geometrically complex structure (D × h = 90 × 80 mm) was produced and showed dimensional deviations of −4 to +4 mm, attributable to compaction-induced deformations in both the horizontal plane and in the vertical axis.

Optimization-based feedrate planning offers the potential to significantly increase machining productivity, but its industrial adoption has been limited by high computational cost and extensive tuning effort. This paper proposes a lexicographic feedrate optimization principle that adaptively balances finishing time and motion smoothness in a tuning-free manner. To further improve computational efficiency, the optimization scheme is extended by a sparsity-exploiting formulation combined with a sequential windowing strategy, enabling real-time capable execution. In addition, a unified toolpath parameterization scheme is incorporated to synchronously handle tool position and orientation within the optimization framework. For a five-axis freeform test contour, the proposed method takes 14 s on an Intel i5-3470 CPU to optimize feedrate profiles for long toolpaths with 100,000 constraint checkpoints, and 52 s on a high-performance AMD 9950X CPU to handle one million checkpoints. Compared to an industrial CNC kernel, the resulting finishing time is reduced by more than 15

The quality of parts produced by machine tools depends on the utilized feed drives. For long travel distances or heavy workpieces, preloaded rack-and-pinion drive systems are typically applied. Due to continuously increasing demands for accuracy and dynamics, the cascade control system with proportional position control is approaching its limits. A promising approach to increase the accuracy and dynamics is a model predictive controller. This controller regulates the system based on an internal model and ensures good tracking performance, due to its predictive behavior. In this paper, a cascaded model predictive control approach for the position control of electrically preloaded rack-and-pinion drives is applied in order to increase accuracy. Three different internal models for the model predictive controller are investigated and compared in real-time with a proportional controller with feedforward control on a test bench. Experimental results show improvements in the tracking behavior with all three models. However, the highest order model does not always provide the best overall performance. Depending on the model, an increase in bandwidth up to 61.1 % or a reduction in tracking error up to 50.1 % is achieved.

Container-Technologien wie Docker und Podman bieten Vorteile für die Bereitstellung virtualisierter Steuerungen (vPLCs). Deren Isolationsmechanismen können jedoch das Echtzeitverhalten beeinflussen. Bisherige Studien nutzen Black-Box-Tests, die keine Rückschlüsse auf konkrete Ursachen ermöglichen. Dieser Beitrag stellt eine White-Box-Methodik vor, mit der einzelne Mechanismen systematisch bewertet werden, um Konfigurationsentscheidungen für echtzeitkritische Containeranwendungen zu treffen.

The core concept of Software-defined Manufacturing involves the separation of software and hardware. Functionalities are not predefined but can be added later through software changes. This necessitates a suitable software and hardware architecture, which allows for reconfiguration of control software, i.e., the dynamic orchestration of real-time software. Traditional orchestration tools from the realm of non-real-time computing lack real-time support and do not provide means to configure real-time communication channels such as Time-Sensitive Networking. Thus, this work describes an orchestration tool based on Kubernetes, which is capable of orchestrating real-time containers including the necessary communication interfaces required for TSN.

The increasing demand for flexible control and simulation concepts in the industry is driven by competitive pressures and shorter product life cycles. Virtual Commissioning (VC) offers the advantage of identifying design and planning errors before the system is commissioned in a real environment. In VC, the test configurations are referred to as Model-in-the-Loop (MiL), Software-in-the-Loop (SiL), and Hardware-in-the-Loop (HiL). The current approach of the different test configurations requires in addition to the organizational preparation and integration, technical efforts such as manual switching and setup. Therefore, this study applies a switching, conversion and transfer mechanism with open interfaces, which enable Continuous Virtual Commissioning (CVC). These mechanisms are tested on an industrial robot cell. This lays the technological foundation for flexible switching between the configurations of VC. Furthermore, robotic joint and cartesian position deviations are observed across the configurations of VC, indicating configuration-specific alterations.

Industrial control systems are hard real-time systems. The correctness of such a system is determined not only by the result, but also by the time frame in which the result is obtained. Logical controllers receive sensor inputs, onto which they perform logical operations and generate an output to affect the behavior of the system. The end-to-end response time in distributed real-time systems is composed of communication and processing time and is time-constrained. The execution time of a task depends on the node’s resources available for execution. Therefore, execution of compute-intensive control tasks with limited edge node resources is challenging. Since meeting deadlines is mandatory, additional resources must be provided. For resource-constrained edge nodes, horizontal scaling must occur at some point and additional nodes must be taken into account to satisfy the requirements. In this paper, we develop a concept for scaling real-time control tasks horizontally across multiple nodes. For this, we apply the approach of software pipelining to multiple nodes. To ensure deterministic communication, a real-time TSN network is used.

Efficient and accurate processing tracking of customer enquiries is crucial in terms of customer satisfaction and business success. Answering these enquiries is often done manually and is therefore linked with high personnel costs in the sales department. In a first step, this paper analyses the customer enquiries of a representative component and system manufacturer. In a second step, the paper compares four different concepts for the automatic extraction of relevant data based on real customer enquiries to generate a product recommendation. A hybrid approach consisting of a lexicon, regular expressions and a Large Language Model delivers the best overall results.