Although the Asset Administration Shell (AAS) standard provides a structured and machine-readable representation of industrial assets, their semantic comparability remains a major challenge, particularly when different vocabularies and modeling practices are used. Engineering would benefit from retrieving existing AAS models that are similar to the target in order to reuse submodels, parameters, and metadata. In practice, however, heterogeneous vocabularies and divergent modeling conventions hinder automated, content-level comparison across AAS. This paper proposes a hybrid graph matching approach to enable semantics-aware comparison of Digital Twin representations. The method combines rule-based pre-filtering using SPARQL with embedding-based similarity calculation leveraging RDF2vec to capture both structural and semantic relationships between AAS models. This contribution provides a foundation for enhanced discovery, reuse, and automated configuration in Digital Twin networks.

The position and path accuracy of industrial robots is strongly affected by temperature. In particular, temperature-dependent friction and altered hysteresis and transmission errors in the gearboxes are the main causes of these deviations. Since standard industrial robots lack temperature sensors, it is proposed to estimate the gearbox lubricant temperature using observer structures. It is planned to apply the proposed lubricant temperature observer to provide the input for temperature-dependent error compensation for gearboxes in industrial robots. First, a two-state heat transfer model describing the heat transfer from the lubricant through the gearbox housing to the environment is described and identified. It is integrated into a Luenberger observer and a Kalman filter with a correction of the lubricant temperature estimation based on measurements from a housing temperature sensor. Temperature measurements were performed in an experimental setup with a cycloidal gear filled with grease, which is common for industrial robots. Due to its changing viscosity and changing thermal conductivity at different shear rates, it shows a less uniform thermal behavior than gear-oil. For the validation measurements, the developed observers showed a mean absolute error of 1.3 K.

Large machine tools with long feed axes commonly utilize electrically preloaded rack-and-pinion drives, where two redundant drives on one axis are preloaded against each other by a dedicated control loop. Manufacturing- and assembly-related tolerances and elastic deformations lead to transmission errors in the drivetrains, that limit the achievable accuracy. Due to the limited bandwidth of the position control, these cannot be fully suppressed despite the use of linear measuring systems. In this paper, the superposition of the individual errors of the two drivetrains of electrically preloaded systems and the transfer function to the resulting path errors are theoretically derived in a first step. In addition, the negative effect of transmission errors on the preload control is investigated. To address the issue, an error compensation for the position and preload control loops is implemented. The basis for this are models of the state-dependent transmission errors utilizing a two-stage machine learning approach. The achievable reduction of path errors is validated in experimental investigations on a test bench and averages 57.8% for the measurements conducted.

Large industrial robots are inexpensive compared to their workspace. Due to flexibility, the positioning accuracy is compromised. This paper presents a simple yet effective static calibration method for robots using a laser tracker that can be applied to processes with no change in external load and without high processing forces. The calibration method is based on a novel elastokinematic model that captures the nonlinear gearbox and link compliance and also accounts for the effects of pose dependent joint friction and nonlinearities due to the gravity compensation mechanism. Furthermore, a feedforward concept is presented that separates kinematics from compliance to reduce computation time. The calibration method was implemented on industrial hardware and experimentally validated on a KUKA KR210–2. In comparison to state of the art compliance modeling, accuracy is improved by 23.1 %. Finally, the feedforward model reduces the mean positioning error in the entire workspace by 54.9% compared to the purely kinematically calibrated model, emphasizing the need for compliance compensation.

Recent advances in AI research are boosting the relevance of realtime neural networks in automation technology. To facilitate the deployment of trained neural networks for realtime applications, where hard limits on upper cycle time are required, this paper contributed a two-fold approach: A Python library for parsing Keras sequential models to structured text using a single function-call, which can be run directly on industrial PLCs and a structured text library to encapsulate reoccurring code and to provide a readable, tested interface for the generated code. The resulting models are experimentally validated regarding their inference time and precision of the outputs.

A key feature of cloud computing is elasticity, which enables resources to be allocated and de-allocated according to dynamic application demands. The evolution of Industrial Control Systems (ICS) is moving towards a distributed converged infrastructure that incorporates computing and communication resources as well as software, replacing dedicated hardware running control applications. While modern ICS and cloud environments share similarities, the corresponding applications differ significantly in their execution models and timing requirements. To enhance the flexibility of ICS, control applications must adapt to changing requirements throughout operation, resulting in varying workloads. The potential for dynamic scaling in ICS is considerable, but is not yet being fully exploited. The objective of this paper is to examine the extent to which state-of-the-art cloud autoscaling concepts are applicable to the scaling of real-time workloads and to propose enhancements to these concepts in order to enable the implementation of dynamic auto scaling of real-time workloads. In conclusion, the fundamental concept of cloud autoscaling can be applied, but must be extended, in particular to ensure deterministic resource provisioning and to improve task isolation in order to guarantee predictable task timing behaviour.

The value added by virtual commissioning of in-dustrial production systems is evident through significant cost savings achieved via early error detection. Detecting errors early in the development process enhances project success. However, the development of simulation models is associated with manual effort, particularly during the early phases of the project when it comes to creating the first models. To reduce costs and increase automation in the redesign of simulation models, a novel concept is presented that automatically transforms unstructured customer requirements into initial model placeholders within a simulation environment. The approach employs a large language model to identify necessary processes and machines, estimating their dimensions and positioning them relative to each other. The output is then converted to a simulation model format and imported into a simulation environment for further refinement. To verify the solution, a synthetic expert, trained using a large language model, learns the probability correlations between plant types, machines, and processes, thereby enabling a more accurate assessment of the resulting simulation model.

In today's global market, manufacturers must be highly adaptable, producing profitably smaller batches. Disin-tegrating hardware and software and embracing modern IT concepts like containerization and Service-oriented Architecture enable this flexibility. While offering rapid reconfiguration, these approaches add complexity. Orchestration tools aid in analyzing response times, workload mapping, and parameter selection for containerized real-time applications. However, current tools struggle with managing distributed, heterogeneous, real-time apps. This paper proposes an orchestration solution based on Compositional Performance Analysis and Genetic Algorithm to optimize task allocation across diverse computing resources. The approach enables the orchestration of heterogeneously scheduled, container-based. distributed real-time systems.

Distributed real-time applications perform deterministic tasks across multiple connected compute platforms, referred to as endpoints. Deterministic behavior of the communication and the compute are prerequisites, but coordinating and managing both in a holistic way is a significant challenge. Converged real-time networks based on Time-Sensitive Networking (TSN) are considered the key enabler across industries for the required connectivity. To date, the potential of TSN cannot be exploited due to many sub-standards still being under development, gaps in the management and configuration functions, lack of best practices and unresolved integration with higher application layers and domain specific engineering. Previous work, focusing on a modular open source framework for the engineering and management of distributed real-time applications, identified a unified approach for TSN-based endpoints as a key gap. This paper presents a novel endpoint architecture to operate, engineer and manage distributed real-time applications by seamlessly integrating real-time networking and compute. The architecture provides interfaces to a central engineering, allows to integrate centralized as well as distributed network management and offers a unified data management. An abstraction layer for applications allows the efficient use and complements existing open source solutions.

The increasing digitization of the manufacturing industry offers a wide range of opportunities for the effective use of production data, which can lead to significant time and cost savings. One promising approach is the use of synchronous simulations during the actual operation of cyber-physical production systems (CPPS) to provide valuable insights into possible performance improvements and at the same time reduce the risk of costly downtime. This paper presents a novel concept that enables structured storage of CPPS production data as part of a digital twin that serves as a virtual replica of the physical production environment. In particular, this approach is based on the Asset Administration Shell (AAS), which provides a comprehensive framework for the management of production data and allows for a seamless integration with simulation models. Storing production data in a structured and standardized format can make data queries efficient, facilitate data processing, and provide a solid foundation for simulation model development, calibration, and validation. These simulation models can then be used to identify potential process improvements and test different scenarios, minimizing the risk of disruption to actual production.

Static configurations and slow adaption to changing requirements characterize today’s production systems. In order to cope with variable external influences, these systems need to become more flexible. One building block towards meeting this requirement from the software perspective is employing quality-preserving processes to develop and update automation applications. While this condition applies to classical automation applications, it is increased if Software-defined Manufacturing (SDM) is applied. Containers can be used to deploy software through orchestration. Orchestration covers initial deployment and the roll-out of new software versions. This makes it in theory possible to deploy automation applications, such as computerized numerical control (CNC) software. However, available tools from information technology cannot be used to orchestrate and update automation applications, as real-time requirements must be considered. This work presents an approach that supports orchestration, specifically the deployment and update process, of real-time containers safeguarded by real-time capable simulation coupled with the control system during the operation phase. It shows how to achieve the right timing for real-time software updates and how to prevent causing damage by observation and safeguarding through information obtained from the real-time simulation. Using real-time simulation in the operating phase, container-based control and real-time orchestration become safer and more applicable in industrial manufacturing systems.

This paper presents a new method for correspondence estimation between a previously known topology of a branched deformable linear object and an image representation from a 3D stereo camera. Although frequently encountered in production, robotic deformable linear object manipulation still lacks reliable sensor feedback. Especially for branched deformable linear objects, such as wire harnesses, correspondence estimation is very challenging. Due to their flexible nature, they have an infinite-dimensional configuration space, such that visual appearances of the same object can vary strongly. Knowing the correspondence is vital for various applications, e.g., estimating valid grasping positions for robotic wire routing or augmented reality support for workers. Therefore, this paper presents a method for matching the topology of a branched deformable linear object to camera sensor data. Asymmetries in the wire harness design reduce the solution space by comparing the known topology of a model to the topology extracted from sensor data. The problem of finding the most likely solution to the matching problem requires features extracted from camera images. These features are used to construct a graph-based topology representation, which can then be matched to a graph-based topology representation of the known branched deformable linear object. The presented method is evaluated using multiple different non-overlapping configurations of a wire harness, showing the effectiveness of a graph-based segment matching approach.

Industrial robots are now used in a wide range of manufacturing processes. The increasing demands on quality and the complexity of the processes cannot always be met by the range of functions that are available in numerical control systems. In the coreless winding of fiber-polymer composites, it is important to keep the roving tension as constant as possible while maintaining a high processing speed. Currently, the winding paths are programmed via a number of control points. This results in a trade-off between lower process properties and higher processing speed with fewer control points. Conversely, finer interpolation of the path can improve process characteristics at lower processing speeds. With interpolation methods that are adapted to the process, new possibilities open up to overcome these trade-offs. This paper presents a spline path planning algorithm based on clothoids. This algorithm considers both the process requirements and the dynamic characteristics of the manufacturing unit. Furthermore, the algorithm is validated on a test contour and compared with established commercial methods.

TSN, or Time Sensitive Networking, is becoming an essential technology for integrated networks, enabling deterministic and best effort traffic to coexist on the same infrastructure. In order to properly configure, run and secure such TSN, monitoring functionality is a must. The TSN standard already has some preparations to provide such functionality and there are different methods to choose from. We implemented different methods to measure the time synchronisation accuracy between devices as a C library and compared the measurement results. Furthermore, the library has been integrated into the ControlTSN engineering framework.

Automating complex tasks using robotic systems requires skills for planning, control and execution. This paper proposes a complete robotic system for maintenance automation, which can automate disassembly and assembly operations under environmental uncertainties (e.g. deviations between prior plan information). The cognition of the robotic system is based on a planning approach (using CAD and RGBD data) and includes a method to interpret a symbolic plan and transform it to a set of executable robot instructions. The complete system is experimentally evaluated using real-world applications. This work shows the first step to transfer these theoretical results into a practical robotic solution. Note to Practitioners—This paper is inspired by the actual deficit to automate maintenance tasks in manufacturing environments through robotic systems. The focus of this work is on providing a practical robotic solution which can automate maintenance tasks completely autonomously. For this, the different skills from manipulation planning to the control and execution are explained. One main aspect of this work is to show how to create elementary robot motion tasks from symbolic plans. The second important part is to explain the practical capabilities applied on real-world applications.

In flexible production systems with distributed control systems which communicate with each other via a real time network, changes in the requirements for real time communication during operation must not lead to a temporary failure of the deterministic communication. Rather, the changes of the network configuration must become active at exactly predictable times in order to guarantee the functionality of the deterministic communication all the time. This paper shows a realization of how the configuration of a real time network schedule under Linux can be adjusted at predetermined times without interrupting the communication. For this purpose, the real time scheduler TAPRIO is integrated into the library libnl, and the performance of this extension is evaluated in a test case. It is shown that the modification of the network configuration is reliably possible in several successive communication cycles.

This article investigates the use of impuls transfer during mechanical impacts for the acceleration of feed axes. For this purpose, additional masses are attached to linear axes and moved by an actuator. Very high accelerations can be obtained by impacts with a defined velocity on contact surfaces. Thus, almost abrupt velocity changes can be achieved on the main drives. To enable driving through corners with constant path velocity, the concept consists of a cross table with two axis equiped with an impact actuator each. Investigations are carried out by using a reference contour and the results are compared with existing strategies for adjusting the corners.

The digitalization of production is a strategic goal in the automation industry, though visions and industrial reality remain far apart. A key issue is that today's segmented communication architecture based on fieldbus networks does not fulfill the future requirements. Converged networks connecting IT and OT systems while maintaining deterministic guarantees for real-time communication are required instead. Time Sensitive Networking (TSN) in the context of IEEE 802.1 Ethernet is a key enabling technology for converged networks. The transition from current communication architectures towards converged networks will not be a disruptive change, but rather an evolutionary process involving many steps. In this paper, an analysis of different dimensions of this transition process and their evolutionary steps is presented. Potential migration paths from current to future production architectures and resulting intermediate brownfield scenarios are identified and analyzed with a focus on their communication architectures.

The handling of deformable linear objects (DLOs), such as cables or hoses, with industrial robots is hardly industrially automated due to the underactuated material behavior. Increasing computing power and decreasing cost of processors over the last decades are now allowing the online computation of more complex handling processes. Within this paper, DLOs are approximated as a multibody chain with a finite number of degrees of freedom. Based on that model, a nonlinear, partially IO-linearizing controller is derived to control an underactuated end point on a trajectory when grasped by a robot arm at the other end. The controller is verified in simulation for high dynamics and shown to significantly reduce the tracking error. A practical evaluation finally shows the qualitative suitability of the multibody approximation and is used to validate the functionality of the controller in an open-loop experiment.

Control and localization of deformable linear objects (DLOs) require models to handle their deformation. This paper proposes an approach to automatically generate a model from available visual sensor information. Based on point cloud data obtained from a 3D stereo camera, the kinematics of a multibody model formulation are derived. The approach aims to balance the tradeoff between computational complexity and model accuracy. This is achieved with a geometric error criterion that reduces the introduced degrees of freedom (DOF) of the model to a necessary minimum, representing the continuous shape with as few bodies as possible. The approach is evaluated analytically and validated with an experimental scenario of DLO manipulation.

Open Platform Communications Unified Architecture (OPC UA) is a relevant technology in the field of machine-to-machine communication that defines data transport protocols and standardizes information modeling. Instead of defining a generalized world model, domain-specific models enable interoperability. These domain-specific models can be developed and released as Companion Specifications. More than 40 Companion Specifications are currently in development or already released. In this paper the current approaches to generate an OPC UA information model or an OPC UA Companion Specification are analyzed. A new modeling tooling based on the Eclipse Modeling Framework (EMF) is presented. This tooling demonstrates the potential of a model-driven approach for the creation of the information model and the structured part of the descriptive text document of a Companion Specification.

Future production systems are expected to support dynamic plug-and-produce scenarios across different component manufacturers. Therefore, the concept of I4.0 components was developed. An I4.0 component describes an asset with an asset administration shell providing a standardized representation of its capabilities. Real-time I4.0 components additionally demand for a deterministic communication with bounded low latency, where the network topology is typically defined by physical links and infrastructure. In order to avoid rewiring and to support dynamic plug-and-produce scenarios with real-time I4.0 components, we present so-called virtual topologies decoupled from the physical network infrastructure. Thereby, we extend the concept of real-time I4.0 components to configure the required behavior and introduce an additional submodel for the administration shell. Moreover, a central SDN controller is enhanced to automatically configure the virtual topologies during runtime. These topologies are based on the new real-time communication technology IEEE 802.1 Time-Sensitive Networking (TSN). Finally, we evaluate the latency of the prototypical implementation within real-time I4.0 components depending on the frame size and network bandwidth.

If existing communication systems are converted to OPC UA, an OPC UA information model is required for the existing data structures. In small systems, an information model can be created manually with reasonable effort. In systems with numerous data structures, however, the effort for manual creation is too demanding. In order to continue using the existing data structures in the code without having to handle OPC UA-specific data structures, a transformation between the existing data structures and the OPC UA information model is necessary. This article presents a possibility of generating an OPC UA information model by a description of the existing data structures, e.g. generated based on ROS messages. The description of data structures is evaluated at runtime and linked with the existing data structures. The different representations for the different access types (read, request/response) in the OPC UA information model are discussed. In addition, an OPC UA client can read the information model from the descriptions of the data structures and write the read data back to the original data structure. In this way, both the provision of the data via OPC UA and the reception are possible. The adaptation effort of the existing software is minimal, since the existing structures are automatically populated and can still be used as usual.

Within the context of Industry 4.0, future production systems are expected to support mass customization up to lot size one. In order to realize the individual production processes, a changeable and consistent integration of assets from different manufacturers is required. Therefore, the concept of I4.0 components was developed. Every I4.0 component provides a so-called asset administration shell, i.e. a standardized virtual representation of its capabilities. This paper focuses on real-time I4.0 components that require a deterministic communication with bounded low-latency in-between. In contrast to non-real-time I4.0 components, Ethernet (IEEE 802.3) is enhanced by the new real-time communication technology Time-Sensitive Networking (TSN, IEEE 802.1). The basic configuration of TSN connections within the asset administration shell was already addressed by previous work. In this work, we present the configuration of application layer protocols using the example of OPC UA Publish/Subscribe (PubSub). More precisely, we outline the underlying concept and define additional submodels for the asset administration shell of real-time I4.0 components. Finally, we discuss the advantages of the presented approach in terms of changeability.

To increase the changeability of future production systems, assets have to provide a standardized representation of their capabilities. Therefore, the concept of I4.0 components was developed, which describes assets with an asset administration shell providing a self-description. Real-time I4.0 components, like safety critical systems or motion control applications, require a deterministic communication with bounded low latency. Moreover, they demand for redundant connections to achieve fault tolerance with respect to the network infrastructure and the components. The integration of redundancy mechanisms in realtime I4.0 components requires additional implementation and configuration effort, which has not been covered in detail yet. In this work, we extend the concept of asset administration shells to configure end-to-end redundancy between real-time I4.0 components. Thereby, we apply the new real-time communication technology Time-Sensitive Networking (TSN) with focus on the standard IEEE 802.1CB. Finally, we evaluate the performance of the redundancy implementation in terms of the transmission offset between the primary frame and its duplicate.

The research and development of intelligent automation solutions is a ground-breaking point for the factory of the future. A promising and challenging mission is the use of autonomous robot systems to automate tasks in the field of maintenance. For this purpose, the robot system must be able to plan autonomously the different manipulation tasks and the corresponding paths. Basic requirements are the development of algorithms with a low computational complexity and the possibility to deal with environmental uncertainties. In this paper, an approach is presented, which is especially suited to solve the problem of maintenance automation. For this purpose, offline data from CAD is combined with online data from an RGBD vision system via a probabilistic filter, to compensate uncertainties from offline data. For planning the different tasks, a method is explained, which uses a symbolic description, founded on a novel sampling-based method to compute the disassembly space. For path planning, we use global state-of-the-art algorithms with a method that allows the adaption of the exploration stepsize in order to reduce the planning time. Every method is experimentally validated and discussed.