This paper builds on our Uncertainty-Guided Live Measurement Sequencing (UGLMS) method. UGLMS is a closedloop test strategy that adaptively selects SAR ADC code edges based on model uncertainty and refines a behavioral mismatch model in real time via an Extended Kalman Filter (EKF), eliminating full-range sweeps and offline post-processing. We introduce an enhanced UGLMS that delivers significantly faster test runtimes while maintaining estimation accuracy. First, a rank-1 EKF update replaces costly matrix inversions with efficient vector operations, and a measurement-aligned covarianceinflation strategy accelerates convergence under unexpected innovations. Second, we extend the static mismatch model with a low-order carrier polynomial to capture systematic nonlinearities beyond pure capacitor mismatch. Third, a trace-based termination adapts test length to convergence, preventing premature stops and redundant iterations. Simulations show the enhanced UGLMS reconstructs full Integral- and Differential-Non-Linearity (INL/DNL) in just 36 ms for 16-bit and under $\mathbf{7 0 ~ m s}$ for 18-bit ADCs ($\mathbf{1 2 0 ~ m s}$ with the polynomial extension). Combining the faster convergence from covariance inflation with reduced per-iteration runtime from the rank-1 EKF update, the method reaches equal accuracy $8 \times$ faster for 16 -bit ADCs. These improvements enable real-time, production-ready SAR ADC linearity testing.

Offshore Power-to-X platforms enable flexible conversion of renewable energy, but place high demands on adaptive process control due to volatile operating conditions. To face this challenge, using Digital Twins in Power-to-X platforms is a promising approach. Comprehensive knowledge integration in Digital Twins requires the combination of heterogeneous models and a structured representation of model information. The proposed approach uses a standardized description of behavior models, semantic technologies and a graph-based model understanding to enable automatic adaption and selection of suitable models. It is implemented using a graph-based knowledge representation with Neo4j, automatic data extraction from Asset Administration Shells and port matching to ensure compatible model configurations.

As Federated Learning (FL) expands to larger and more distributed environments, consistency in training is challenged by network-induced delays, clock unsynchronicity, and variability in client updates. This combination of factors may contribute to misaligned contributions that undermine model reliability and convergence. Existing methods like staleness-aware aggregation and model versioning address lagging updates heuristically, yet lack mechanisms to quantify staleness, especially in latency-sensitive and cross-regional deployments. In light of these considerations, we introduce SyncFed, a time-aware FL framework that employs explicit synchronization and times-tamping to establish a common temporal reference across the system. Staleness is quantified numerically based on exchanged timestamps under the Network Time Protocol (NTP), enabling the server to reason about the relative freshness of client updates and apply temporally informed weighting during aggregation. Our empirical evaluation on a geographically distributed testbed shows that, under SyncFed, the global model evolves within a stable temporal context, resulting in improved accuracy and information freshness compared to round-based baselines devoid of temporal semantics.

In the automation industry, concepts such as Digital Twins, representing virtual counterparts of physical assets, enhance efficiency, sustainability, and economic viability by enabling e.g., optimizations, advanced monitoring, and predictive maintenance. To achieve this, Digital Twins rely on models (including simulation models), which in turn depend on data describing the current state of the physical asset. As connectivity increases, the available data extends beyond the data of the sensors of the asset itself. It extends to data from the surrounding environment and even beyond. This introduces new challenges for Digital Twins and their models in terms of data integration. In previous research it was shown that current implementations are not adequately prepared for this scenario. Currently, no analysis in the literature addresses how interaction within a connected environment alters the requirements for Digital Twins. Therefore, this paper explicitly identifies the emerging challenges associated with extending the information acquisition scope of Digital Twins beyond the immediate physical asset. This is accomplished through a systematic analysis of potential scenarios that consider the inclusion of external data. Our analysis reveals several new requirements, such as the need for an extended description of the model’s purpose and the specific requirements for the data input. Digital Twins must possess enhanced capabilities to identify appropriate data sources within a dynamic production environment, as well as methods to manage multiple, potentially redundant data streams effectively. Through this analysis, we aim to highlight potential research directions for Digital Twins in the automation industry and advocate for the integration of interconnectivity into Digital Twin systems.

The transition to connected vehicles (CVs) is shaping the future of autonomous and highly connected vehicles. As the vehicles’ reliance on increasingly complex software increases, more central points of failure are introduced. This necessitates more thorough and scalable anomaly and intrusion detection systems. Conventional approaches can struggle with the complex and dynamic structures of CVs while not providing any automatic response actions. Therefore, machine learning-based anomaly detection methods are gaining popularity. Reinforcement Learning especially has shown the potential to increase configurability and provide end-to-end solutions. Thus, this paper presents a Systematic Literature Review (SLR) on Reinforcement Learning (RL) for anomaly and intrusion detection, applicable for CVs. In it, different RL algorithms and the analysis of how they can be used for training and detecting anomalies are presented. The problem formulations for different approaches are compared, their advantages and challenges are discussed. Additionally, the potential of using RL to react to anomalies is presented, discussed and advocated for. The results of the survey indicate that many of the identified RL formulations are not inherently superior from supervised learning approaches. However, RL techniques can result in better configurability of anomaly detection system, enabling site reliability engineers to efficiently adjust the model via reward functions. Furthermore, it becomes evident that the strengths of RL-based anomaly detection are best leveraged when used for reacting to anomalies. Therefore, the authors propose to focus on the contextual or sequential decision making formulation including corrective actions, compared to the classification-based detection formulations using RL.

Large Language Models (LLMs) offer flexible reasoning capability but lack physical embodiment, while traditional automation systems can execute physical processes yet lack cognitive capability. This paper presents a layered architecture that bridges this gap by integrating LLMs with digital twins and physical automation systems, with reference to practical case studies as proof of concept. The proposed architecture comprises three layers: a cognitive layer powered by LLMs, a bridging layer based on digital twins, and a physical layer consisting of technical process and automation system. Within the digital twin layer, we introduce three design paradigms for structuring information to support effective LLM integration: state snapshot modeling, event message modeling, and plan sequence modeling. These paradigms are demonstrated through prototypical case studies on robotic automation control and process simulation. To address challenges such as hallucination, task complexity, and system reliability, we distill a set of practical strategies, including multi-agent system design, human validation, and test-driven development. Additionally, we propose the concept of "Return on Intelligence" as a conceptual tool for evaluating the efficacy of investments in intelligent automation. This research contributes to the theoretical foundation and the architecture design for developing intelligent, adaptive automation systems powered by LLMs.

Traditional industrial automation systems require specialized expertise to operate and complex reprogramming to adapt to new processes. Large language models offer the intelligence to make them more flexible and easier to use. However, LLMs’ application in industrial automation settings is underexplored. This paper introduces a framework for integrating LLMs to achieve end-to-end control of industrial automation systems. At the core of the framework is an agent system designed for industrial automation tasks. A structured prompting method and an event-driven modeling mechanism provide the information for LLMs to perform reasoning on different context levels, allowing them to semantically interpret the information, generate production plans, and control operations on the automation system. Furthermore, this framework facilitates the creation of structured datasets for fine-tuning LLMs on this specific downstream application. Our contribution includes a formal system design, proof-of-concept implementation, and a method for generating task-specific datasets for LLM fine-tuning and testing. This approach enables a more adaptive automation system that can respond to spontaneous events, allowing intuitive system operation and configuration through natural language. Demo videos and detailed evaluation data are accessible on GitHub: https://github.com/YuchenXia/LLM4IAS.

This paper introduces a novel closed-loop testing methodology for efficient linearity testing of high-resolution Successive Approximation Register (SAR) Analog-to-Digital Converters (ADCs). Existing test strategies, including histogram-based approaches, sine wave testing, and model-driven reconstruction, often rely on dense data acquisition followed by offline post-processing, which increases overall test time and complexity. To overcome these limitations, we propose an adaptive approach that utilizes an iterative behavioral model refined by an Extended Kalman Filter (EKF) in real time, enabling direct estimation of capacitor mismatch parameters that determine INL behavior. Our algorithm dynamically selects measurement points based on current model uncertainty, maximizing information gain with respect to parameter confidence and narrowing sampling intervals as estimation progresses. By providing immediate feedback and adaptive targeting, the proposed method eliminates the need for large-scale data collection and post-measurement analysis. Experimental results demonstrate substantial reductions in total test time and computational overhead, highlighting the method’s suitability for integration in production environments.

Software systems often evolve into multiple variants to meet diverse requirements. This is usually achieved with the clone-and-own approach, where an existing variant is copied and modified. While efficient in the short term, this approach presents challenges for long-term maintenance. A suitable solution to overcome this, is to re-engineer the variants into a software product line (SPL). However, this process is labor-intensive and prone to errors. Although initial studies explore the use of large language models (LLMs) to assist in the re-engineering tasks, they do not address challenges such as hallucination and limited context windows, which restricts the applicability.In this paper, we present a novel approach to assist the transformation of cloned software variants into an SPL using an LLM. To mitigate hallucination, we propose a self-refinement feedback loop to validate the generated SPL. Additionally, we introduce a variation point filtering technique that reduces the input size, while preserving essential information. To quantify and evaluate the generated output, we propose the use of existing metrics that can be employed for the evaluation. Our evaluation demonstrates the effectiveness of the self-refinement feedback loop and variation point filtering based on an existing case study. The results, benchmarked against the proposed variability metrics, indicate that the generated SPL maintains equivalent complexity and potential for reusability, to the system it is compared against.

The limitations of on-board sensors and blind spots caused by occlusion cause the reduction of perception quality in autonomous vehicles. In such cases, cooperative perception provides additional data via Vehicle-to-Everything communication to enhance local perception, causing a large volume of data transmission. The vehicle can focus on acquiring and utilizing relevant data according to the prevailing road context by identifying the current traffic situation. To achieve this, we propose a concept for the situation identification of the vehicle using Bird's-Eye-View images. Firstly, the situation around the vehicle is identified using object detection with semantic segmentation, followed by understanding the context of the traffic using a situation identification module consisting of an open-source projective transformation network Cam2BEV and a situation identification neural network. The concept was evaluated and validated by running the software on the CARLA simulator using the in-built RGB camera and the semantic segmentation camera. Additionally, the portability of the situation identification module for real-world applications was verified on Cityscapes and nuScenes urban driving datasets. Overall, the proposed situation identification approach enables efficient sensor data management by prioritizing relevant data to the current traffic situation. The source code is available in the following link: https://github.com/akshaynarla/DySi_Select

A Digital Twin of a production plant comprises a variety of simulation models. When these models are coupled for co-simulation, they can reflect the behavior of the entire production plant and be used in various application scenarios. To leverage the benefits of the Digital Twin and simulation during the operational phase, manual efforts in model adaption must be automated. This contribution presents a flexible co-simulation approach that enables automated selection of simulation tool interfaces, parameterization, and execution of different model configurations. Finally, application scenarios in the context of Power-to-X production are discussed, and the current implementation of the approach is presented.

The rapid innovation in the automotive industry highlights the increasing importance of user comfort, especially when integrated with advanced learning scenarios. However, there is a noticeable gap in research focusing on vehicle cabin comfort, particularly in the context of learning and personalization of features. This study conducts a systematic literature review to assess the current state of research in this area. By utilizing federated learning with personalization, a novel and promising technique, various use cases related to vehicle interior comfort are explored. These use cases help derive the requirements needed to address the research question. The methodology of the systematic literature review is detailed, including the evaluation of specific prerequisites. The key finding reveals that no existing study meets all the predefined requirements, underscoring the need for further research in this domain.

Most of the existing vehicle comfort features operate solely based on user input, lacking consideration for individual preferences, and environmental conditions. This manual adjustment while driving can lead to potential distractions, and jeopardizing user safety. On the other hand, implementing a system where the vehicle control unit learns individual preferences and autonomously adjusts accordingly would significantly enhance the driving experience. In this paper, we examine the thermal comfort of users as one of the comfort features within software-defined vehicles. Given that thermal comfort is influenced by both physiological and external factors, and people have diverse individual preferences, this paper proposes leveraging personalized federated learning to automate and personalize temperature regulation, thereby enhancing thermal comfort of passengers in the vehicle cabin. To validate this concept, we conducted experiments employing a prototype equipped with sensors to collect real-time data, which is then used to train a predictive model. The model's accuracy was assessed using metrics and compared against a centralized approach. In addition, we used a simulator to visualize the potential improvement in thermal comfort with the predicted values. Our findings indicate that temperature control in the vehicle cabin, utilizing federated learning for individualized regulation, outperforms conventional learning approaches, thus yielding significant improvement for thermal comfort of passengers.

Software-intensive systems emerge in a multitude of variations to meet diverse customer requirements. To develop such variant-rich software systems, software product line (SPL) Engineering has emerged as a key strategy for managing the variability. However, the adoption of SPLs is highly complex due to the diversity of feature model formats and specifications, and the complexity of implementing variability. Leveraging the capabilities of powerful large language models (LLMs) can facilitate the adoption of SPLs. Nonetheless, these LLMs often lack knowledge of the various specifications of potential feature models as well as efficient implementation of different variability mechanisms. To address these challenges, we propose a novel method based on retrieval-augmented generation. This method generates reusable artefacts and a corresponding feature mapping based on a given feature model, thereby aiding system engineers in adopting an SPL.

This paper presents a novel design of a multi-agent system framework that applies large language models (LLMs) to automate the parametrization of simulation models in digital twins. This framework features specialized LLM agents tasked with observing, reasoning, decision-making, and summarizing, enabling them to dynamically interact with digital twin simulations to explore parametrization possibilities and determine feasible parameter settings to achieve an obj ective. The proposed approach enhances the usability of simulation model by infusing it with knowledge heuristics from LLM and enables autonomous search for feasible parametrization to solve a user task. Furthermore, the system has the potential to increase user-friendliness and reduce the cognitive load on human users by assisting in complex decision-making processes. The effectiveness and functionality of the system are demonstrated through a case study, and the visualized demos and codes are available at a GitHub Repository: https://github.comlYuchenXia/LLMDrivenSimulation

This paper presents a novel application of Large Language Models (LLMs) to improve Failure Mode and Effect Analysis (FMEA) for managing risks in technical processes and products. We designed an LLM multi-agent system that utilizes Retrieval Augmented Generation (RAG) for risk analysis and text generation. This system interfaces directly with an FMEA knowledge database to dynamically extract relevant information, perform reasoning based on user input and retrieved information, and generate targeted text recommendations for completing FMEA spreadsheets. By automating the analysis and synthesis of risk information, this system can provide expert knowledge and reduces the cognitive load on users, speeding up the FMEA process. The effectiveness and functionality of this LLM-enhanced FMEA application is demonstrated through a prototype, accessible via a GitHub Repository at: https://github.com/YuchenXia/LLMRiskAnalyzer.

In safety-critical applications such as automotive or railway technology, prediction of the condition and remaining life of electronic components is a major challenge. To determine the condition, a comprehensive digital fingerprint of the components is needed, which already starts during the production phase. Currently, inspection data such as SPI, AOI and AXI data are used to group the components into two groups ("OK" and "not OK"). In the future, however, this inspection data will also be used to predict the service life and current condition of the assembly. This article deals with the investigation and processing of the imaging inspection data, as well as the geometry parameters determined from it. First, a dimensional reduction of the input data is carried out with the help of mathematical methods (PCA, t-SNE and Autoencoder). This reduction extracts the relevant information from the input data from the point of view of the AI. So far, clustering methods such as K-Means, DBSCAN and Hierarchical Clustering have been used to generate groups. In the future, these groupings can be used to predict the service life together with the loads on the components and thus make a statement about the current condition of the components

The rise of software-defined automation systems is accompanied by an increase in interconnected services and networks. Among other implications, this leads to an expansion of the attack surface for cyber threats, which in turn can lead to significant security issues and financial losses. Against this background, this paper investigates the effectiveness of anomaly-based intrusion detection systems (IDS) for detecting and preventing new and evolving cyber threats. The paper especially considers the challenge of concept drift, which describes the phenomenon that the statistical properties of the network traffic or behavior patterns that an IDS is trying to detect change over time. To accomplish this, a comprehensive literature review, prioritizing recent studies since 2019, highlights the challenges of concept drift and the application of reinforcement learning in IDS. The results suggest that the integration of adaptive strategies and reinforcement learning in IDS enables significant improvements in anomaly detection.

As software-defined vehicles continue to dominate automotive research, the vision for a future of extensively connected vehicular environments, including vehicle-to-everything (V2X) communications and nationwide connected infrastructures, grows more pronounced. However, despite the existence of standards to support these advances, widespread adoption and integration into broader vehicular ecosystems remain elusive.Thus, the scope of this paper is to explore potential connected vehicle functions of the future and outline potential paths toward their realization. Utilizing the Delphi method, the authors engaged with experts from the German automotive industry to gain insights into emerging and feasible connected vehicle functions. The interviews served as a primary source for identifying promising vehicle functions, which were subsequently compared against existing literature to detect related functions and to group them into coherent clusters. Finally, by combining these clusters and the acquired knowledge, visions for the future of mobility were derived.The findings of this study reveal that the trajectory toward implementing connected vehicle functions is unlikely to follow a linear path. Instead, development is expected to occur through multiple, parallel scenarios. The success and prevalence of specific use cases will largely depend on the development of viable monetization strategies and business models that can sustain these individual "islands" of innovation. The paper concludes that future research should focus on strategies to integrate these disparate islands, aiming to forge a unified and comprehensive vehicular communication system.

Industrial automation is driven by trends such as autonomy, intelligence and networking. Increasing energy demands, scarce resources and shorter product lifecycles pose challenges to production systems such as interoperability, flexibility and extensibility. The concept of the Digital Twin, acting as a virtual representation of a production system, can address these challenges. This paper presents a concept that realizes a dynamic production scheduling as partial function of the Digital Twin using the Asset Administration Shell and a service-oriented architecture. The authors specifically address automated asset production scheduling and propose an MQTT broker with a Functionality Dictionary server. Through the Asset Administration Shell interface, the proposed production scheduler communicates with the proposed product calendar to generate a specialized production schedule for each product. The evaluation scenario in a cyber-physical laboratory shows the advantages of this concept.

The Digital Twin is a well-known concept of Industry 4.0 and is the cyber part of a cyber-physical production system providing several benefits such as virtual commissioning or predictive maintenance. The existing production systems are lacking a Digital Twin which has to be created manually in a time-consuming and error-prone process. Therefore, methods to create digital models of existing production systems and their relations between them were developed. This paper presents the implementation of the methodology for the creation of multi-disciplinary relations and a quantitative and qualitative evaluation of the benefits of the methodology.

Industrial automation is becoming increasingly networked, intelligent and autonomous. Digital Twins, which serve as virtual representations, are a key technology in this context. The Digital Twin of a modular production system contains many different models that are mostly created for specific applications and fulfil different requirements. In particular, simulation models created in the development phase can be used in the operational phase for applications such as prediction or operation-parallel simulation. Due to the high heterogeneity of the model landscape in the context of a modular production system, the plant operator is faced with the challenge of adapting the models in order to ensure an application-oriented realism in the event of changes to the asset and its environment or the addition of applications. Therefore, this paper proposes an approach for the continuous model adaption in the Digital Twin of a modular production system during the operational phase. An agent-based implementation of the concept demonstrates the benefits of the approach for an operational phase application scenario.

In the era of Industry 4.0, data is becoming increasingly vital for future manufacturing and digital twin applications. Currently, primarily asset-created data is utilized, while the potential benefits of incorporating external data from connected production systems remain untapped. This underutilization is largely due to the complex and time-consuming nature of integrating external data. Automating this process could significantly improve efficiency and unlock valuable insights for more effective decision-making. In this paper, we present the requirements for a system aimed at automating the integration of external data, considering factors such as data heterogeneity and the need to support diverse communication protocols. We further introduce an assistant system that facilitates this automated integration, enhancing manufacturing processes and digital twin applications. Furthermore, we provide a prototypical implementation of the assistant system in the context of a collaborative robot utilizing information from its surroundings obtained through external camera and LiDAR imaging. By addressing the challenges of external data integration, this research contributes to the advancement of data-driven manufacturing and the optimization of digital twin technologies.

Machine learning implementations in an industrial setting poses various challenges due to the heterogeneous nature of the data sources. A classical machine learning algorithm cannot adapt to dynamic changes in the environment, such as the addition, removal, or failure of a data source. However, to handle heterogeneous data and the challenges coming with this, it is a mandatory capability to build robust and adaptive machine learning models for industrial applications. In this work, a novel architecture for robust and adaptive machine learning is proposed to address these challenges. For this, an architecture consisting of different modular layers is developed, where different models can be easily plugged in. The architecture can handle heterogeneous data with different fusion techniques, which are discussed and evaluated in this paper. The proposed architecture is then evaluated on two public datasets for condition monitoring of automation systems to prove its robustness and adaptiveness. The architecture is compared with baseline models and shows more robust performance in case of failing/removed data sources. In addition, new data sources can easily be added without the need to retrain the whole model. Furthermore, the architecture can detect and locate faulty data sources.

Discrete-event simulation is a widely used method for modeling complex systems and evaluating their performance in production. However, the management of discrete-event simulation models and their associated data can be challenging, especially when dealing with large and heterogeneous data sets. In this paper, we present a knowledge graph-based framework that facilitates the management of discrete-event simulation models by representing them and their associated data as a knowledge graph. Our framework enables the integration of multiple data sources, supports the reuse of existing models, and enables the support for different applications based on the knowledge graph. Specifically, we demonstrate the application of our framework to the management of discrete-event simulation models in a use case of automatic model generation for a production line. Our results show that our framework provides an effective solution for managing discrete-event simulation models and their associated data, enabling users to easily query, update, and extend the models while maintaining their integrity and consistency. Overall, our work contributes to the development of knowledge-based solutions for managing complex systems and supports the broader adoption of discrete-event simulation in practice.

Advancements in the Internet of Things (IoT) applied by automation systems lead to an increasing and flexible interconnection of various devices and the generation of voluminous data. In the domain of home automation for instance, having a multitude of interconnected data leveraged to knowledge could enhance user-centeredness and energy efficiency, among other use cases. However, the data as well as data sources are dynamic and heterogeneous, posing a great challenge to unfold their full capabilities. Thus, a unifying and correspondingly dynamic architecture is needed for supporting the flexibility and extensibility of such systems and data sources. In this paper, we present the concept and implementation of a context-aware INTElligent LIVing (inteLiv) architecture. Consisting of a data layer, a context layer and a service layer, the architecture abstracts the heterogeneous data of the linked devices via a middleware. The resulting system enables the collection of data across networked heterogeneous devices and models the collected data by using a unified dynamic context model. A hybrid approach was chosen for the designed context model: A combination of an ontology-based and property graph-based model represent a core aspect of this work as opposed to common static ontological representations and pre-defined context-based use cases. In the following contribution, the inteLiv ontology is first defined to describe the concepts of the intelligent living system. Based on the modeled context, reasoning algorithms also enable the derivation of new context information. The context is stored as a knowledge graph and can be used for context-aware applications, which can be designed during runtime. Furthermore, a user interaction with the system is possible via a web interface.

Gathering sufficient labeled training data to effectively train a high-performing deep learning model can be particularly challenging in the realm of industrial automation. Depending on the data type, this may require expensive interruptions to production processes or similar disruptions on the factory floor for data collection. It is often uncertain which data types are crucial for enhancing the performance of the trained model. For vision models, factors such as specific viewing angles or lighting conditions may be important, while for models utilizing radio signals, unique reflections generated by moving metal surfaces could be significant. Moreover, data labeling is expensive as it is primarily conducted manually by human workers. This paper demonstrates how to automatically generate relevant, labeled synthetic training data to boost a neural network's accuracy for deep learning-based 5G indoor positioning tasks. We reveal that employing this generated synthetic data to train a convolutional neural network can improve its median positioning accuracy by a notable 25%.

An increasing proportion of software characterizes modern automated systems such as vehicles. It can be used to provide new functions to improve the customer experience in terms of automation, personalization, and connectivity. For system development, this means that classic processes must be rethought and supplemented by new strategies. This is mapped via the trend of the software-defined systems. In this context, individual components can be improved over the entire life cycle. In order for these updates to function properly, testing before deployment is commonplace. However, since the software is complex and it is infeasible to test 100% of the possible scenarios, a software update can still lead to unforeseen side effects. At the same time, it must be monitored whether the desired update effects actually apply. In the worst case, the side effects include errors that must be found as quickly as possible in order not to negatively influence product quality and subsequent innovation cycles. The goal of this work is to detect side effects that manifest themselves as anomalies at runtime and to trace them back to the corresponding code change. For this purpose, a new approach is presented that uses the digital twin of the vehicle as an information base, providing operational data and simulations parallel to operation. The simulation generates synthetic data also containing the rare and risky cases that can then be matched against the operational data to identify anomalies. Once an anomaly is detected, a subsequent analysis follows that relates the anomaly to a causal code sequence. The presented approach is realized using the example of a model vehicle with microcontrollers and corresponding peripherals such as sensors and motors. The model vehicle is simulated using the simulation software SimulIDE, which provides the synthetic data. The evaluation shows that the trace back of side effects to the respective code sequences can successfully be achieved.

In this paper, we present a novel framework that combines large language models (LLMs), digital twins and industrial automation system to enable intelligent planning and control of production processes. We retrofit the automation system for a modular production facility and create executable control interfaces of fine-granular functionalities and coarse-granular skills. Low-level functionalities are executed by automation components, and high-level skills are performed by automation modules. Subsequently, a digital twin system is developed, registering these interfaces and containing additional descriptive information about the production system. Based on the retrofitted automation system and the created digital twins, LLM-agents are designed to interpret descriptive information in the digital twins and control the physical system through service interfaces. These LLM-agents serve as intelligent agents on different levels within an automation system, enabling autonomous planning and control of flexible production. Given a task instruction as input, the LLM-agents orchestrate a sequence of atomic functionalities and skills to accomplish the task. We demonstrate how our implemented prototype can handle un-predefined tasks, plan a production process, and execute the operations. This research highlights the potential of integrating LLMs into industrial automation systems in the context of smart factory for more agile, flexible, and adaptive production processes, while it also underscores the critical insights and limitations for future work. Demos at: https://github.com/YuchenXia/GPT4IndustrialAutomation

The creation of a Digital Twin for existing manufacturing systems, so-called brownfield systems, is a challenging task due to the needed expert knowledge about the structure of brownfield systems and the effort to realize the digital models. Several approaches and methods have already been proposed that at least partially digitalize the information about a brownfield manufacturing system. A Digital Twin requires linked information from multiple sources. This paper presents a graph-based approach to merge information from heterogeneous sources. Furthermore, the approach provides a way to automatically identify templates using graph structure analysis to facilitate further work with the resulting Digital Twin and its further enhancement.

The Digital Twin is one of the future key technologies of digitization and Industry 4.0. Through coupling the vast amount of available static and dynamic data about a physical asset with intelligent software functions, it aims for simplifying the increasingly complex functions and interconnections of automation systems. This makes it necessary to provide the Digital Twin with updated and high-quality data about the physical asset. However, little attention is paid to data not generated by the asset itself but from external data sources. This is partly due to a lack of methodologies for identifying and evaluating external data relevant for a specific Digital Twin. The contribution of this work is a categorization of data for the Digital Twin and a methodology to support the identification of data from outside of the physical asset. The work-in-progress refers to the context of industrial automation and is to be validated in the future in laboratories containing discrete manufacturing facilities at Pforzheim University and the University of Stuttgart.

Failure analysis is essential for improving the reliability and manufacturability of electronic devices. With the time-domain reflectometry method, failures can be analyzed non-destructively. The method enables the detection, location, and characterization of hard interconnection failures (open or shorts) as well as of soft interconnection failures, which can give an outlook on imminent hard failures. Generating measurement data from real failed devices is costly since failed devices need to be selected and the measurements need to be performed and prepared. In contrast, simulation models are often available where all possible kinds of failures can be created. Therefore, we propose simulation to real transfer learning for the failure analysis on time-domain reflectometry data. A deep learning model shall first be trained on time-domain reflectometry simulation data and then be transferred to measurement data of a power transistor. We investigate different possibilities of transfer and evaluate the performance of a SiC power transistor.

Semantic technologies and knowledge graphs are becoming increasingly important in academia and industry. Nevertheless, they remain a rarity in the manufacturing industry, and the advantages they offer are only partially exploited. The same is true for semantic technologies for simulations within production systems. Simulation serves as an essential tool for virtual manufacturing, but the required models are still created by experts in an often elaborate and mostly manual way. In this paper, we derive the requirements for semantic models to capture production simulation knowledge and present an ontological architecture for a knowledge graph for manufacturing and simulation that meets these requirements. The different ontologies of the architecture are based on established production and simulation standards, namely ISA-95 and VDI guidelines. By implementing the ISA-95 standard in an ontology, heterogeneous data from the production system can be integrated. Furthermore, the connection of ISA-95 models to simulation-specific concepts allows the representation of simulation models within the knowledge graph. The implementation of the knowledge graph is shown within a prototypical simulation framework.

In partially automated manufacturing, humans work together with mobile robots. Trajectory prediction, i.e. predicting future positions of human workers, improves collaboration and coexistence between humans and robots on the shop floor. In this paper, we discuss the interrelated research questions of how human motion trajectories can be predicted and how mobile robots such as Autonomous Mobile Robots and Automated Guided Vehicles can take such predictions into account in their pathfinding and navigation. On the robot side, advanced D* pathfinding algorithms allow robots to take dynamic obstacles into account. For trajectory prediction, the position of human workers is determined by an Ultra-Wideband-based Real-Time Locating System. A trajectory prediction framework is introduced to support the implementation and use of pattern- and planning-based trajectory prediction algorithms. The evaluation is based on scenarios from the addressed problem area of manufacturing.

Industrial transfer learning increases the adaptability of deep learning algorithms towards heterogenous and dynamic industrial use cases without high manual efforts. The appropriate selection of what to transfer can vastly improve a transfer’s results. In this paper, a transfer case selection based upon clustering is presented. Founded on a survey of clustering algorithms, the BIRCH algorithm is selected for this purpose. It is evaluated on an industrial time series dataset from a discrete manufacturing scenario. Results underline the approaches’ applicability caused by its results’ reproducibility and practical indifference to sequence, size and dimensionality of (sub-)datasets to be clustered sequentially.

Industrial mobile robots increasingly operate in dynamic and complex environments, resulting in risks that arise from the situation. Consequently, mobile robots need knowledge about the situative risk to make responsible decisions, e.g., when planning a trajectory. In this paper, the authors present a new approach to evaluate these situative risks at runtime. To compute the situative risk, the proposed approach uses multi-agent adversarial reinforcement learning. Unlike the usual approach that uses the self-play mechanism to harden the agents against disturbances, the approach directly uses the self-play to determine the confidence that the system can handle the current situation. Based on the evaluation of the strength of the perceived perturbations, the robot’s digital twin calibrates the action space of the adversarial agents. In this way, the adversarial agents represent the disturbances that are likely to occur. From the simulation results of the competing agents, the digital twin infers the confidence with which the system can handle the situation. In the event that the system cannot handle the situation and crashes in the simulation, the digital twin calculates the probability that the simulated scenario will occur in reality. To calculate this probability, the authors propose the method of game-theoretic event trees. Combining the probability of an accident scenario with the damage caused by the simulated hazard, the results of situative risk are obtained. The approach is qualitatively evaluated using a case study. In this case study, a industrial mobile robot called Robotino is considered that connects workstations in a matrix production system. The case study shows that the approach is promising.

In the production of the future, people and robots will work together in dynamic environments. Industrial mobile robots drive past workers and obstacles to the assemblers' workstations, pick up workpieces and deliver them to the next free workstation. To ensure reliable production, robots must carefully consider their actions based on the current situation to minimize losses. In order to reason about losses, it is necessary to identify loss scenarios that are likely to arise from the system's situation. In this paper, the authors propose a novel methodology for identifying these likely loss scenarios based on fault injectors. The fault injectors are trained using reinforcement learning. Based on a probability model of disruptive events (faults), the agents learn to destabilize the system by simulating such events. When a chain of these events leads to a loss, a loss scenario is found. The higher the probability of the loss scenario, the higher the reward for the agent. In this way, the fault injectors optimize for maximally likely loss scenarios. After describing the methodology, the authors propose a framework for systematically classifying different types of fault injectors and give advice on how to build them. Finally, the authors demonstrate reinforcement learning-based fault injectors on the path planning of the mobile robot platform Robotino.

Current industrial automation systems are facing increasing dynamics. Thus, acquiring and managing heterogeneous data within the Digital Twin to enable decision making is necessary although challenging. Knowledge Graphs unify and relate data, enabling the derivation of new insights. In this contribution, an approach for context-enriched modeling of cyber-physical production systems is proposed, in order to realize a Knowledge Graph enhanced intelligent Digital Twin further considering the context. Therefore, the modeling approach of the Knowledge Graph considers context and serves as a base for the graph embeddings to gain further knowledge about the production system. This knowledge is used within the architecture of the intelligent Digital Twin. The resulting benefits, i.e. diverse manifestations of an improved decision making, are highlighted by the use case of self-organized reconfiguration management.

Behavior models in the Digital Twin are relevant to many use cases such as digital product development, virtual system optimization or virtual commissioning. However, such behavior models are structured in the literature according to different criteria and levels, which complicates the selection of the correct properties for specific use cases. One criterion discussed very differently is the modeling depth of behavior models. This paper presents different approaches to structure behavior models from literature and introduces a new concept with focus on the modeling depth. These levels of modeling depths, as well as the characteristics of the different modeling depths, are illustrated using a representative industry scenario.

The use of Digital Twins is versatile and offers a wide range of advantages. However, their creation is often a manual process and therefore very time-consuming and cost-intensive. The creation of behavior models stands out as particularly time-consuming. Therefore, this paper deals with the creation of Digital Twins from the perspective of an industrial component manufacturer, with a particular focus on the creation of behavior models. An automated approach based on libraries is presented. The creation of behavior models is validated in an industry-relevant application scenario and compared with the manual creation process.

Indoor positioning systems are an enabling technology for many current developments in the manufacturing field like digital twins and robot fleet management. Utilizing 5G for positioning promises high accuracy, reliability, and cost-efficiency due to shared hardware usage for communication and positioning. Which positioning technique suits 5G-bases positioning best for manufacturing is still an open research question. This paper presents a deep learning approach for 5G-based positioning. The first results of our research work in progress obtained at the research factory ARENA 2036 indicate a positioning accuracy in the centimeter range.

Vision applications are becoming increasingly important for product quality surveillance in manufacturing. Training consistently well-performing visual detection algorithms based on convolutional neural networks is very challenging. Typically, there is too much training data for engineers to keep track of possible gaps in it. But even small cases of missing training data e.g. certain viewing angles can lead to trained CNNs that are unable to detect objects, that seem obvious to engineers i.e. cognition gaps. This paper presents how synthetic training data can be created in a targeted manner to close cognitive gaps of a CNN for specific use-cases. The proposed methodology uses 3D rendering to create new image data by variating scene parameters. The created data is used to reveal a cognition gap of a CNN. We show that by using this created synthetic data to train the CNN the cognition gap can be successfully closed. This is evaluated with the well-known AlexNet CNN used as a visual bicycle detector. The bicycle example is used as a stand-in for a geometrically interesting, but simple product, that is manufactured in large and growing amounts.

Today’s systems engineering is challenged by an increasing number of requirements aimed at smaller target audiences. This leads to a rise of variants to fulfill individual consumer demands. Because of the complexity introduced by the surge of software-defined mechatronic systems, deriving new variants manually is becoming increasingly difficult. As of today, however, there exist no well-established methods or tools to assist in variant generation. This article aims to fill this gap by presenting a novel approach that enables an automated, requirements-based variant generation. For this purpose, a variant meta-model capable of both storing and interconnecting physical parameters and qualitative criteria of system components is designed and translated into a machine-readable form. The resulting data model is described to be stored inside a multi-model database, allowing for the integration of past project data by using data mining techniques. Additionally, a variant generation algorithm with a strategy for targeted exploration of the variant solution space is presented. The complete approach is realized by implementing a demo application that includes both the multi-model database and a java application for algorithm execution. Our work is evaluated by benchmarks using a dataset with real-world components, indicating that requirements-conform variants can be generated in a reasonable time.

The Asset Administration Shell (AAS) is a standardized data container for sharing data in the context of Industry 4.0. It allows different participants or systems to communicate their information based on shared meaning. Today, however, AAS makes data interoperable at the cost of extra development effort. Developers must map a proprietary information model to a standardized AAS model during the data transformation. In this work-in-process paper, a novel data transformation method based on transfer learning with neural language model is proposed to automatically map the data properties from an arbitrary information model into a standardized AAS model. The term "semantic fingerprint" is used to characterize a pivot intermediate vector generated by a neural network, containing latent conceptual meaning about a data property, which is in turn used for generating the mappings between data properties with similar conceptual meaning. By this means, the proposed approach fills the research gap on automated generation of AAS models with semantic analysis and is able to lower the barrier to adopting AAS with tool support.

Automation systems are increasingly being used in dynamic and various operating conditions. With higher flexibility demands, they need to promptly respond to surrounding dynamic changes by adapting their operation. Context information collected during runtime can be useful to enhance the system's adaptability. Context-aware systems represent a design paradigm for modeling and applying context in various applications such as decision-making. In order to address context for automation systems, a state-of-the-art assessment of existing approaches is necessary. Thus, the objective of this work is to provide an overview on the design and applications of context and context models for automation systems. A systematic literature review has been conducted, the results of which are represented as a knowledge graph.

Systematic Literature Reviews aim at investigating current approaches to conclude a research gap or determine a futuristic approach. They represent a significant part of a research activity, from which new concepts stem. However, with the massive availability of publications at a rapid growing rate, especially digitally, it becomes challenging to efficiently screen and assess relevant publications. Another challenge is the continuous assessment of related work over a long period of time and the consequent need for a continuous update, which can be a time-consuming task. Knowledge graphs model entities in a connected manner and enable new insights using different reasoning and analysis methods. The objective of this work is to present an approach to partially automate the conduction of a Systematic Literature Review as well as classify and visualize the results as a knowledge graph. The designed software prototype was used for the conduction of a review on context-awareness in automation systems with considerably accurate results compared to a manual conduction.

The success of the reconfiguration of existing manufacturing systems, so called brownfield systems, heavily relies on the knowledge about the system. Reconfiguration can be planned, supported and simplified with the Digital Twin of the system providing this knowledge. However, digital models as the basis of a Digital Twin are usually missing for these plants. This article presents a data-driven approach to gain knowledge about a brownfield system to create the digital models of a Digital Twin and their relations. Finally, a proof of concept shows that process data and position data as data sources deliver the relations between the models of the Digital Twin.

Conversational Assistants like Alexa, Siri, and Google Assistant have become part of our everyday lives and faced wide adoption in industry. They support users in various tasks, such as playing songs or booking flights, making users' lives easier. Nevertheless, conversational assistants have not yet found their way into production, even though they would offer advantages, such as intuitive use through natural language and easy access to information. Simulation-based skills can also add a new layer in production by making simulation power accessible in an automated way. In this paper, we aim to discuss the concept of a simulation-based Conversational Assistant by presenting an architecture and the workflow within the architecture.

Deep learning promises performant anomaly detection on time-variant datasets, but greatly suffers from low availability of suitable training datasets and frequently changing tasks. Deep transfer learning offers mitigation by letting algorithms built upon previous knowledge from different tasks or locations. In this article, a modular deep learning algorithm for anomaly detection on time series datasets is presented that allows for an easy integration of such transfer learning capabilities. It is thoroughly tested on a dataset from a discrete manufacturing process in order to prove its fundamental adequacy towards deep industrial transfer learning - the transfer of knowledge in industrial applications' special environment.

Advances in automation technology and AI research, as well as the trend toward digitalization and networking, make modern automation systems complex, heterogeneous and dynamic, i.e., changes to the system and its environment are increasingly becoming the norm. Under such conditions, the classic safety process of certifying a snapshot at the end of the design phase becomes insufficient. To overcome this, we propose an approach that evaluates the safety of the actions of such systems at runtime. In doing so, the approach addresses three fundamental challenges of online safety assurance, namely, incorporating safety requirements into the online design process, coping with dynamic changes in the environment, and dealing with the uncertainties arising from dynamics and unpredictable environments and the adaptation of the system to them. The presented approach is applied to a specific scenario that shows promising results in simulation.

Digital Twins are being increasingly used in manufacturing industry to support the whole plant lifecycle. The constantly changing environmental parameters, interactions between various systems and their Digital Twin as well as changes inside the Digital Twin influences the context of the available information. This context information is insufficiently considered to analyze process and optimize the interaction. This article presents an approach to model the internal and external context of a system using a graph-based context tier model. The usage and benefits of the concept are shown for a flexible production system.

Nowadays, formal methods are used in various areas for the verification of programs or for code generation from models in order to increase the quality of software and to reduce costs. However, there are still fields in which formal methods haven’t been widely adopted, despite the large set of possible benefits offered. This is the case for the area of programmable logic controllers (PLC). This article aims to evaluate the potential of formal methods in the context of PLC development. For this purpose, the general concepts of formal methods are first introduced and then transferred to the PLC area, resulting in an engineering-oriented description of the technology that is based on common concepts from PLC development. Based on this description, PLC professionals with varying degrees of experience were interviewed for their perspective on the topic and to identify possible use cases within the PLC domain. The survey results indicate the technology’s high potential in the PLC area, either as a tool to directly support the developer or as a key element within a model-based systems engineering toolchain. The evaluation of the survey results is performed with the aid of a demo application that communicates with the Totally Integrated Automation Portal from Siemens and generates programs via Fastsynth, a model-based open source code generator. Benchmarks based on an industry-related PLC project show satisfactory synthesis times and a successful integration into the workflow of a PLC developer.

Large-scale manufacturing systems consist of multiple complex process steps that are highly interdependent and characterized by dynamic nonlinear physics. These processes are subject to model uncertainties and external disturbances that cause reduced process stability and fluctuant product quality. To avoid defects and to ensure a high product quality, an adequate detection and control of disturbance effects is needed. Hence, the paper presents a novel adaptive control scheme with disturbance rejection property for discrete large-scale manufacturing systems. To model the complex process physics and disturbance effects, Long short-term memory (LSTM) networks are applied. These recurrent networks improve the modeling of time-variant systems regarding short-term and long-term dependencies. The adaptive control scheme is predicated on a self-learning and cooperative prediction of process and disturbance to achieve an improved performance. Based on this neural prediction, a compensation of disturbance effects and thus a part-centered quality optimization can be achieved. The approach is tested and evaluated for a plant of hot forging.

Recently, ontologies and semantic data technologies have increasingly come back into the focus of research due to the emerging use of knowledge graphs. However, even though the modeling and simulation community has recognized the potential of using this technology for the modeling process, for example for automatic model generation, adaptation or to represent simulation expert knowledge, a general and reusable approach for the aforementioned purposes is still missing. Therefore, in this paper a state of the art review for using knowledge representation during modeling and simulation processes of complex technical systems is conducted, such as factories or process plants with specific focus on the production and logistic domain. Based on that, requirements and benefits of knowledge graphs in this specific domain are evaluated.

Digital Twins will change how systems and products are engineered and operated. Individual virtual representations of assets help to develop, maintain and change single components or whole factories. Aerospace engineering, product design and intelligent manufacturing are hot spots for the use of Digital Twins. Simultaneously, globalized markets lead to a growing awareness of dependability and quality, which increases the importance of verification and validation. The Digital Twin could prove to be key enabler for efficient verification and validation processes. This paper presents the results of the literature review of approaches that use Digital Twins for verification and validation purposes. Many solutions have been found for a wide range of challenges in various fields of application. This survey discusses the underlying methods and the elements of Digital Twins already in use. Most research approaches focus on simulations and three methodological clusters of approaches sharing similar ideas were identified.

Flexible intralogistics systems use automated guided vehicles (AGV) to transport goods. In assembly and warehouses, AGVs and human workers often work side by side. For optimal navigation, AGVs must consider human movement and estimate future positions of workers. Using real-time locating systems (RTLS) to improve human-robot collaboration enables more energy-efficient and safer AGV wayfinding strategies. This paper gives a summary on the topics RTLS, AGV wayfinding and trajectory prediction and introduces the momentum-based approach to predicting future worker positions in factories and warehouses. The results show that ultra-wideband-based RTLS are very well suited for trajectory prediction in the production sector.

Fault prediction based upon deep learning algorithms has great potential in industrial automation: By automatically adapting to different usage contexts, it would greatly expand the usefulness of current predictive maintenance solutions. However, restrictions regarding the centralized accumulation of data necessary for such automatic adaption call for a distributed approach to training these algorithms. Therefore, in this paper, a continual learning based algorithm for fault prediction is presented, allowing for distributed, cooperative learning by elastic weight consolidation. This algorithm is then evaluated on a large NASA turbofan engine dataset and shows promising results regarding the performant training on decentral sub-datasets for industrial automation scenarios.

Networked and intelligent cyber-physical systems are being increasingly applied in automation technology. Surrounding parameters representing the ambient environment and the users interacting with these systems can greatly influence their operation and tend to be heterogeneous and dynamic. There is insufficient consideration for these parameters and how they relate to the system and can contribute to optimizing its analysis and operation. Enabling context-awareness for cyber-physical systems presents an approach to consider surrounding and situational factors. In this contribution, the design of context-aware cyber-physical systems is addressed with the goal to first introduce a fitting definition for context-aware cyber-physical systems as well as propose an approach to dynamically model the system's context in relation to its user and environment. The concept is shown for a user-centric pill dispenser, based on which the role as well as applicability of the context model is presented.

Smart Manufacturing adopts an extensive amount of enabling technologies. Progress in ICT constantly drives innovations. Ubiquitous connectivity enabled by IoT technologies, increasing compute capacity and efficiency, and the subsequent push of digitalization in industry enabled new data-driven applications and the re-emergence of AI-technologies over the last decade. At the same time, the deficiencies of current infrastructures and system architectures are revealed when companies fail to adopt data-driven technologies extensively. This paper discusses how modern IT-architectures and infrastructure for cyber-physical production systems can enable their inherent self-x capabilities to pave the road towards higher levels of autonomy facilitated by data-driven technologies.

OPC UA is one of the most important communication protocols for IIoT applications in the automation domain. One important requirement for Industry 4.0 scenarios is standardized semantics, which is introduced into OPC UA through Companion Specifications. Nevertheless, OPC UA still lacks a comfortable interface for accessing these huge graphs, especially on the edge- and cloud-layer. Of course, there is a query language specified for OPC UA. However, to the best of our knowledge, there is no publicly available implementation.In this paper, we will present how SPARQL can be used to query OPC UA graphs natively and also will present rules to transform OPC UA queries into SPARQL queries. Furthermore, we will highlight some issues of OPC UA Query and how they can be bypassed by native SPARQL. Finally, we were able to correctly execute all nine example queries of OPC UA Part 4 Annex B (complex examples) with both approaches.

In the automation domain, OPC UA can be considered one of the most important communication protocols for IIoT applications. According to a study of McKinsey the potential economic impact of the IoT within factories may reach up to 3,7 Trillion Dollar by 2025. To unlock this potential it is very important to bridge the interoperability gap. Since V1.04 OPC UA also supports session-less clients and therefore allows the development of RESTful clients which can be used to address the cross-domain interoperability issue. However, currently, RESTful clients cannot use Subscriptions, because the corresponding Service Sets are not supported for session-less clients. In this paper, we will present how Subscriptions can be introduced for session-less clients. Furthermore, we outline how Group-Subscriptions can be introduced for session-less as well as session-based clients. Finally, our evaluation shows that Group-Subscriptions significantly outperform standard Subscriptions if the Subscription can be shared by several clients.

The communication protocol OPC UA is one of the most important IIoT enablers within the automation domain. OPC UA not only aims to provide interoperability on the transport layer, but also interoperability of the semantic layer shall be addressed based on so-called Companion Specifications. However, the lack of OPC UA formal semantics makes automatic validation of OPC UA data models impossible. Another drawback is the shortage of available tools for OPC UA, such as an implementation of the query engine for the specified OPC UA query language. In this paper we provide a formal translation of OPC UA models to the Semantic Web standard OWL, thus making OPC UA implicit semantics, that is described in the documentation, explicit, by means of OWL axioms. Moreover, we outline how this mapping can be used to offer validation and querying of OPC UA data models based on already existing Semantic Web technology.

Increasing competition in the automotive industry makes cost-saving integration of more and more vehicle derivatives and variants such as sedans, coupes, cabriolets as well as electrical and combustion engine models into existing production systems necessary. At the same time, the production planners face a major challenge while integrating vehicle bodies-in-white. In contrast to the original concept and rough planning state, the automated production plants are continuously optimized during the detail planning phase as well as after the start of production as a result of improved processes and model upgrading. For fast integration of new vehicles, a current digital image of the real production plant - the so-called Digital Twin - is groundbreaking. This Digital Twin of a factory consists of a current bill of resources for cost calculation and a current layout planning state. The paper describes a concept for creating a Digital Twin of a body-in-white production system for the concept and rough planning projects. In the internal concept planning phase, planners do cost calculations and layouts for ordering factory suppliers. However, for integration planning, the original concept and rough planning project have to be updated. Therefore, a new concept has been developed which uses current information from the cyber-physical system and a current 3D scan to update the bill of resources and the layout planning on demand.

Global trends in manufacturing urge existing machines to become more flexible, as the production environment becomes increasingly dynamic. In order to attain this “flexibilization”, existing machines have to be adapted during their operational phase, i.e. mechanical changes, changes of sensors and actuators, software changes, or a combination thereof have to be performed. The adaptation of existing machines is a complicated and time-consuming process, as various interdependencies have to be considered. Therefore, a systematic approach for generating adaptation options is crucial for a successful adaptation of machines. State-of-the-art approaches primarily focus on the adaptation of products and, thus, are not appropriate for manufacturing machines. Accordingly, this paper presents an approach for the generation of adaptation options to support the adaptation of manufacturing machines. Furthermore, a use case study is presented and discussed, where the approach is applied to an industrial manufacturing machine. Here, the results demonstrated that the approach is applicable for an industrial manufacturing machine and can automatically generate valid adaptation options.

The environment of todays automated manufacturing machines is highly dynamic. Unpredictable changes in customer demands and shorter product lifecycles pose challenges for automated manufacturing systems which usually are designed for a static context. Thus, flexibility and changeability are discussed in order to face the challenges that arise from the dynamic production environment. This paper presents the results of two literature reviews on flexibility indicators and changeability indicators and discusses the determined indicators as well as their usage in the literature over time. A classification of the indicators based on their frequency of usage is also given.

Ope UA is one of the most important communication protocols for Industry 4.0 applications in the automation domain. However, to really reach the status of an Internet-of-Things protocol, OPC UA also has to give some answers to upcoming cross-domain questions. Only if OPC UA is able to bridge the gap between several different domains, it will be accepted as real Internet-of-Things protocol and therefore supported even better by the APIs of the big IT players. One technology which already interconnects nearly every domain is the REST architecture. A lot of research already focused on the combination of these two technologies. However, in this paper we will show the first approach which covers all OPC UA requirements and therefore enables also use cases like server reconfiguration. We will also give some insights into our concept, which was contributed to the OPC UA specification as part of the Vl.04 release.

Due to current trends in manufacturing, existing machines need to become more flexible to cope with a more dynamical production environment. To achieve this “flexibilization”, existing machines have to undergo adaptation processes during their operational phase, i.e. mechanical changes, changes of sensors and actuators, software changes, or a combination thereof. A fundamental part of these adaptation processes is the generation of adaptation options. Here, many constraints and interrelations must be considered, which complicate these processes and may result in suboptimal or error-prone results. Therefore, a systematical and efficient approach for generating adaptation options is indispensable for a successful adaptation process. State-of-the-art approaches mostly focus on the adaptation of products and are not appropriate for manufacturing machines. Accordingly, in this contribution an approach for the generation of adaptation options is proposed to support the adaptation of manufacturing machines. The approach is based on the interdependencies between products, processes, and resources. The application of the approach is demonstrated by an illustrative example of a pick-and-place unit.

Internet-of-Things-(IoT)-systems will pose new challenges for the simulation of such systems, like the dynamic entering of components during runtime and the huge heterogeneity of the components. Various existing approaches for the simulation of IoT-systems are described. Additionally a concept is presented, which meets the described challenges. To satisfy the heterogeneity each component is modelled individually in separate modelling tools. The concept utilizes a multi-agentsystem to simulate the communication between the different models and to enable them to be integrated during simulation time. With the presented concept it is also possible to integrate real components into the simulation.

Automated manufacturing machines need to evolve in order to compete in nowadays turbulent manufacturing environment. This evolution process is achieved by performing different adaptations of the system, i.e. mechanical changes, changes of sensors and actuators, software changes, or a combination thereof. Currently, this adaptation process is mostly based on the experience of experts and is time-consuming and error-prone. This contribution proposes an approach that integrates tool support in the adaptation process to assist the engineer in the overall process. The approach uses a multi agent system in order to analyze production change requests and to generate and evaluate possible adaptation options for the machine based on a mechatronic model of the system including interdependencies between products, processes and resources on parameter-level. Thus, adaptation options can be generated on a fine-grained level. An illustrative example for the concept is given by the application on a modular production system.

Modern manufacturing systems based on cyber-physical systems with a growing amount of software allow frequent updates and reconfigurations to adapt the systems to volatile usage scenarios in the production. A diverse system environment arises even for similar or equal subsystems based on the same platform used at different locations. A major challenge for such systems is the regression test after changes or updates. The resources for the regression test, in a dedicated test environment or deployed to the assembly lines, are limited. To plan the test in the best possible way, a lot of dependencies, relationships and experiences from former tests and tests from other locations have to be considered. This paper describes an assistance system which supports the planning of the regression test in such distributed manufacturing scenarios by combining manual modeling with automated data processing. Therefore the system calculates a cross-location test progress and suggests a prioritized test case sequence.

In this document we describe an approach to interconnect existing industrial automation systems with cooperation networks, like the internet of things, even if the industrial automation system uses a different communication protocol. To realize this, we have developed the concept of connector which provides system-dependent interfaces for different industrial automation and a universal interface for the cooperation network. Between these interfaces the messages are processed and translated by services, which act as a representation of the industrial automation system.

Existing production systems need to become more flexible and reconfigurable in order for manufacturing companies to stay competitive in nowadays highly dynamic markets. To achieve this goal, existing machines have to undergo various kinds of adaptation processes. As currently there is no standardized procedure, adaptations are mostly individually performed which results in a time-consuming and error-prone process. A systematization of the adaptation process could guide engineers through this complex task and may reduce errors and execution time. Thus, this paper is aimed at proposing a systematization approach for the adaptation of manufacturing machines. The application of the approach is illustrated by an adaptation scenario of a testing machine.

Future automated systems are connected to each other in a production network with the goal to manage, schedule and operate the manufacturing process of individual products autonomously. Therefore a semantic language is necessary to empower the systems to fulfil their tasks. In this content eCl@ss is often mentioned as applicable to describe products. In this paper we conduct a study to describe an intelligent barrel with eCl@ss in terms of completeness in objective and functional description to evaluate if it is sufficient for the needs of cyber physical systems (CPS).

The increasing complexity of software in production systems leads to a growing relevance of testing. This trend will continue due to the flexibilization, and IT networking of production systems. This paper proposes a discussion about the requirements of future production systems which will be reconfigurable, decentralized controlled and will consist of autonomous components. From these predicted requirements, new challenges for functional testing are derived. To elucidate these challenges, three use cases illustrate when testing measures will be necessary within the operation phase of production systems.

With growing complexity of premium cars, the end-of-line test systems also increase in complexity. The test systems have to provide more and more functionality like flashing of electronic control units (ECUs) and sensor calibration. Current end-of-line test systems evolved to complex networked IT-systems, which consist of various components and subsystems from different suppliers. Automotive production maintenance engineers are challenged to keep the availability of the test system on a high level to not cause production delays. In a case study with automotive test experts, we considered fault diagnosis and test case selection as two major tasks to maintain a high system availability. The experts combine their knowledge and experience about fault-prone system parts and former faults to optimize fault diagnosis and test case selection for regression testing. To support the experts to manage the growing complexity, we propose a combined fault diagnosis and test case selection assistance system. The combination of both techniques enables synergy effects by supporting the fault diagnosis with test case selection and by considering fault data in regression testing. This paper presents the concept of that combined assistant system and describes a prototypical realization used in an exemplary scenario.

This paper provides a discussion on the coming technological changes in process automation of networked production systems, which will change the testing procedure. In the smart factory of the future there will be no possibility to reach a test coverage of 100%, assuming a flexible automation with continuous reconfiguration and dynamic changes during runtime. Consequently, large amounts of test cases and powerful algorithms for their prioritization are needed in order to certify the correct functionality of the production systems in the network. A concept is presented on how to analyze and prioritize the enormous amount of test cases resulting from the changes during runtime. The proposed approach for test case selection utilizes information of the product, the process and the status of the production machine for the prioritization and selection.

Machine learning has been applied in image processing system for object recognition, inspection and measurement. It assumes that the provided training objects are representative enough to the real objects. However in real application, new (unlearned) objects always emerge over time, which may deviate from the trained (learned) objects. The conventional image processing system using machine learning is not able to learn and then recognize these new objects. In this paper, an incremental learning based image processing system is presented. The overall system consists of three layers: execution, learning and user. The conventional image processing system is constructed in execution layer. In learning layer, adviser and incremental learning are applied to generate a new classifier. The incremental learning is differentiated into different methodologies: data accumulation and ensemble learning. Through the adviser, a proper methodology can be recommended. User is able to interact with the system via user layer. Comparing to the conventional image processing system, the proposed system is robust in industrial applications, since it deals with the classification problems dynamically.

Industrial plants are multi-disciplinary systems that are operated for multiple decades. Changes in these systems are consequently indispensable, making appropriate mechanisms for managing co-evolution of engineering documentation necessary. In this paper, a co-evolution model for control and simulation software is introduced. Typical evolution categories and modification strategies for enabling co-evolution of automation control and simulation software are derived and formally defined. Using description logics, the identification of these complex modification strategies based on atomic modification actions is made possible.

This paper presents a new design concept for spherical-joint supported gripper that can be easily installed on industrial robots. The purpose of this joint design is to increase the flexibility of the robots' movements by adding three additional degrees-of-freedom (d.o.f) in one single compact joint. In doing so, the trajectory can be enhanced or more simple ones be used. In addition, this design does neither require extra links nor modification of industrial robots structures since it is being installed as an end-effector. The theoretical analysis and the model of the spherical-joint gripper will be exemplified by a prototype that was built to study the efficiency of this approach.

Due to the demand of small lot sizes in production, manufacturing systems have to become more flexible to minimize machine set up time. To realize such a flexibility tools must provide the ability of adaptation to varying conditions. A new tool for example has to be adapted and configured autonomous in sense of “plug & produce” without an operator in action. In this paper an approach is presented which focuses on the existing data structure and information flow concerning robotic handling processes. Based on a scenario for handling varying products, an architecture is presented which enables auto configuration behavior of new and adjustable robotic tools. This architecture forms the fundament for developing an auto configurable system consisting of a robot, a robotic tool and a work piece.

Unlike assembly, disassembly attracts much less attention in terms of automatization. Therefore, the paper contributes to research automation technologies for the application of disassembly. In this paper, an architecture design of intelligent vision-based system is proposed. Employing the disassembly system based on the architecture, components of electronic waste (for example, traction batteries) are detected and localized. The obtained information is then integrated in a database to determine a disassembly plan, which involves space constraint and relation between the individual components. Relying on the plan, disassembly can be implemented. The contribution of this paper is to outline the main framework of developing a vision-based intelligent disassembly cell.

This paper reports research results on developing a machine vision system to assess the quality of row crop plants. Comparing to the prevalent machine vision system employed in agricultural industry for weed-crops classification as well as plant density evaluation, the proposed machine vision system is able to detect the location of plants (weed / crops) and calculate the leaves' area for plant quality assessment, even if the leaves are overlapped with each other. The developed machine vision system involves a camera system and an image processing system. The camera system uses a coaxial camera constructed by a RGB sensor and near infrared (NIR) sensor, which cooperate with a white front lighting and NIR front lighting respectively. Plants are firstly captured by the coaxial camera. The plants are segmented from background on RGB image; the overlapping edges of leaves are detected on NIR image. Afterwards the overlapping leaves are separated and assigned to the assessed stem position of plants. At last, based on the assigned leaves, the plants are separated, and the area of plant canopy is calculated. A set of experiments have been made to prove the feasibility of the proposed machine vision system.

The concept of the manufacturing system is defined in the basic engineering phase. Thus, the productivity and cost of the system are also determined. The approach of the Assisted Engineering aims to integrate experience and knowledge of existing manufacturing systems in the engineering process. For this purpose, these systems are modularized and systematically integrated into a knowledge base. This knowledge base allows a more efficient engineering process by reusing mechatronic modules. Thereby, the performance of a manufacturing system can be substantiated at an early stage of engineering. The approach described in this paper aims at increasing the reusability of such modules. For this, the modularization of existing systems is optimized so that the modules are not too specific for reuse in the development of a new manufacturing system.

Engineering of automated manufacturing systems requires a lot of effort and therefore creates a bottleneck. In this paper we present a methodology of designing the concept of systems based on mechatronic objects. The methodology proposed enables the identification of objects, which take mechatronic aspects into consideration. This means we are able to create a holistic concept design of the mechanics, electrics and electronics and software. The approach of identifying mechatronic objects is novel because the overall blueprint of the system can be optimized across the various disciplines. The methodology is demonstrated based on an example of special machinery systems, whereby the functional evaluation of the concept is discussed throughout the detail-engineering phase.

Novel manufacturing processes and machine designs can be developed by means of mechatronic modules and their adaptation to the specific manufacturing case. The complexity of these modules must be carefully chosen so that a reuse for different application processes becomes possible. If a mechatronic module is too specific or complex, it cannot be engaged for other machine designs, as it is made only for that specific case. The methodology of axiomatic design is introduced and adapted towards the engineering of mechatronic modules in machine development for special manufacturing processes. The presented approach of systematic modular design allows for the engineering of mechatronic modules and the identification of the required links between modules.

In this paper a modularization approach is presented with an example of special purpose machinery and demonstrated by a multi spindle lath machine tool. Thereby a methodology for identification of mechatronic units based on a distinction between different machinery modules is given. The methodology is based on the typical tradeoff between engineering flexibility and standardized manufacturing. The use case supports the analysis on the competing requirements of flexibility in functionality for customized solutions and therefore optimized production processes. The developed methodology helps in decision making during the identification process of the mechatronic units.