Digital twin-based optimiser for self-organised collaborative cyber-physical production systems

Cyber-physical systems are increasingly developed for highly-automated purposes, such as industrial production plants. Not only the level of autonomy of these cyber-physical production systems, but also their potential lifespan, and the expectation that the system adapts throughout its life introduces challenges for the development process. To develop such systems in a structured manner, consideration of their context is essential. Suitable abstractions and perspectives must be employed to highlight different aspects of the context, such as structural or functional concerns. To ensure longevity and adaptability, the development process must account for changes in the context at runtime. This introduces a great deal of uncertainty. Current uncertainty modeling approaches cannot be applied right away, as they do not explicitly consider the multiple aspects of context analysis, which need to be documented in different artifacts. To aid the engineering of collaborative cyber-physical production systems, this paper presents a model-based approach to document context uncertainty. We propose “Orthogonal Uncertainty Models”, which closely integrate with context models from different perspectives. The approach has been evaluated using a case example from the industry automation domain, which is also presented herein.

The Internet of Things (IoT) era facilitates new possibilities at the interface between the lifecycle of the product and the manufacturing disciplines allowing to add value and meet growing legal requirements. This work presents use cases from the automobile industry which are analyzed in order to derive requirements for data exchange. The analysis of the use cases shows clearly that we need to focus on the instance level for this purpose. This means that both, a specific unit of the product and the production system, must be considered. As a solution approach, a communication loop is proposed bringing the Digital Twin concept in a new field of usage. The key aspect of the proposed solution is the direct intercommunication between the Digital Twins of the product being produced as a Cyber-Physical System (CPS) and the production resources as Cyber-Physical Production Systems (CPPS). This solution enables the aggregation of production-related data within the products Digital Twin and therefore promotes maximal transparency throughout engineering disciplines, phases of the product lifecycle and the whole supply chain.

Threat modeling is a critical proactive security technique for identifying threats and determining mitigations. However, traditional approaches often fall short for Industrial Control Systems (ICS), which automate operations in domains like manufacturing and energy and are a subset of Cyber-Physical Systems (CPS). CPS integrates computation, networking, and physical processes, with ICS requiring specialized cybersecurity approaches due to its operational and safety-critical nature. This study explores the use of digital twin technology as a promising cybersecurity tool for ICS, enabling testing and analysis without disrupting operations. By examining the capabilities of digital twins in analysis, simulation, and replication, the research evaluates their potential to enhance threat modeling across the CPS life-cycle. Insights from the European Cyber Security Organisation (ECSO) Technical Paper on Cybersecurity Scenarios and Digital Twins guide the exploration of their role in threat modeling. The study addresses four research questions: 1. What purposes do digital twins serve in cybersecurity? 2. What benefits do digital twins offer in cybersecurity? 3. How can digital twin technology be leveraged for threat modeling? 4. What advantages can the use of digital twins bring to threat modeling? Our findings reveal that digital twins enhance ICS threat modeling by enabling continuous, dynamic, and autonomous assessment, offering valuable insights for advancing cybersecurity strategies in ICS, CPS, and related domains.

Digital twins emerge in many disciplines to support engineering, monitoring, controlling, and optimizing cyber-physical systems, such as airplanes, cars, factories, medical devices, or ships. There is an increasing demand to create digital twins as representation of cyber-physical systems and their related models, data traces, aggregated data, and services. Despite a plethora of digital twin applications, there are very few systematic methods to facilitate the modeling of digital twins for a given cyber-physical system. Existing methods focus only on the construction of specific digital twin models and do not consider the integration of these models with the observed cyber-physical system. To mitigate this, we present a fully model-driven method to describe the software of the cyber-physical system, its digital twin information system, and their integration. The integration method relies on MontiArc models of the cyber-physical system's architecture and on UML/P class diagrams from which the digital twin information system is generated. We show the practical application and feasibility of our method on an IoT case study. Explicitly modeling the integration of digital twins and cyber-physical systems eliminates repetitive programming activities and can foster the systematic engineering of digital twins.

Digital Twins are part of the vision of Industry 4.0 to represent, control, predict, and optimize the behavior of Cyber-Physical Production Systems (CPPSs). These CPPSs are long-living complex systems deployed to and configured for diverse environments. Due to specific deployment, configuration, wear and tear, or other environmental effects, their behavior might diverge from the intended behavior over time. Properly adapting the configuration of CPPSs then relies on the expertise of human operators. Digital Twins (DTs) that reify this expertise and learn from it to address unforeseen challenges can significantly facilitate self-adaptive manufacturing where experience is very specific and, hence, insufficient to employ deep learning techniques. We leverage the explicit modeling of domain expertise through case-based reasoning to improve the capabilities of Digital Twins for adapting to such situations. To this effect, we present a modeling framework for self-adaptive manufacturing that supports modeling domain-specific cases, describing rules for case similarity and case-based reasoning within a modular Digital Twin. Automatically configuring Digital Twins based on explicitly modeled domain expertise can improve manufacturing times, reduce wastage, and, ultimately, contribute to better sustainable manufacturing.

Digital equivalence is the main objective of Digital Twin (DT)s, and simulation is an integral part. DTs reach beyond traditional simulation with the help of real-time synchronization through Industrial Internet of Things (IIoT) technologies. Simulation supports off-line experimentations and planning, while DTs offer synchronous execution and modification. DTs help to understand “what may happen”. Also, they present “what is happening” and its management methodologies. In this paper, we present building aspects and an integration approach for a Digital Twin based Discrete-Event Simulation model. Our approach utilizes a data-driven agent-based simulation within a DT framework. It presents an integration layer that provides two essential features: reconfiguration and state initialization. It gives simulation models configurability and integrity that are required for operating within a DT. Our proposed approach is presented through a use case of a semiconductor manufacturing system. The proposed integration layer extends the usability of current Discrete-Event Simulation (DES) for a DT within a Cyber-Physical Production System.

• The objectives of this study match the CIE content as it provides academcians and practioners with innovative and helpful information on advanced computing technology in the industry. • Foundation models are large-scale machine learning models that have been trained on large amounts of data to perform a variety of tasks. • FMs are used to support management and operations in supply chain and logistics management and operations. • The paper is based on research conducted by the authors with several Italian and European organizations under a grant from the European Union's Next GenerationUE initiative. • The paper concludes with a real-world industrial case study to further substantiate these claims. The organizational environment is subject to constant change, driven by rapid technological progress and the advent of Industry 5.0. This dynamic scenario compels authors and practitioners in the fields of logistics and supply chain management and operations (LSCM) to explore the transformative impact of foundation models (FMs) and artificial intelligence (AI) on these areas. The integration of AI FMs, coupled with digital twins (DT) and cyber-physical systems (CPS), will not only optimize current processes but also fundamentally reshape the future of digital engineering (DE) in LSCM. Understanding the opportunities and challenges with FMs is critical to the strategic positioning in an ever-evolving marketplace where Industry 5.0 technologies drive innovation and efficiency. Organizations can gain a competitive advantage by analyzing the opportunities and risks of integrating FMs into LSCM operations and leveraging their ability to improve decision making and enhance predictive analytics and real-time monitoring through DT and CPS. This paper serves as a comprehensive and informative guide for evaluating the transformative potential of FMs in redesigning LSCM operations in the context of Industry 5.0, highlighting critical applications, exploring potential benefits and challenges, and pointing to upcoming innovations such as AI-driven DT and smart cyber-physical systems that are revolutionizing supply chain (SC) processes. The detailed analysis based on the Supply Chain Operations Reference (SCOR) components shows that FM-based solutions, supported by AI and Industry 5.0 technologies, can significantly improve LSCM business processes. This paper presents an integrated functional framework and technical architecture for implementing FM in LSCM, emphasizing the role of DT and CPS in enabling smart, adaptive, and resilient SC. The paper concludes with an industrial case study that further substantiates these claims and shows the practical application of FM in a shipbuilding organization. The case study describes successful approaches to implementing and effectively utilizing AI and DT. It provides a practical, functional, and technical framework for applying the transformative impact of FMs on LSCM within the Industry 5.0 paradigm. This integration highlights the synergy between FM, AI, DT, and CPS and paves the way. It paves the way for a more innovative, sustainable, and competitive management of supply networks and their operations.

Autonomous driving and e-mobility are swiftly becoming not only the work of science fiction or popular science, but a reality. A key focus of manufacturers and suppliers in the automotive domain is of course to specify systems that implement this reality. Often, scenarios at type-level are used throughout the development process to specify system behavior and interaction within the car, as scenario models are comparatively easy to understand and can easily be subjected to manual validation. However, autonomous driving and e-mobility require interaction not just of systems within the same car, but collaboration between multiple cars as well as between cars and miscellaneous road infrastructure (e.g., smart road signs). The car becomes a Cyber-Physical System that dynamically forms collaborating networks at runtime with other Cyber-Physical System to create functionality that goes beyond the scope of the individual vehicle (e.g., resolve a traffic jam). Consequently, a plethora of possible compositions of such a network exist and must be specified and validated completely to assure their adequate and safe execution at runtime. Doing this at type-level with scenario models becomes prohibitively tedious, error prone, and likely results in unrealistic development cost. To combat this issue, we investigate the use of multi-level Message Sequence Charts to allow for specifying interaction scenarios between collaborative Cyber-Physical System in a network of collaborating automotive Cyber-Physical System. To assist the developer in systematically defining multi-level Message Sequence Charts, we propose two processes. The resulting diagrams use a mixture of type and instance-level abstractions within one conceptual diagram. This allows reducing the required effort to manually validate the adequacy of scenarios to a manageable amount because information within the scenarios can be validated in batches. At the same time, instance-level defects become more obvious. Evaluation results from a controlled experiment show that multi-level Message Sequence Charts contribute to effectiveness and efficiency of manual validation for collaborative automotive Cyber-Physical System.

When cyber-physical systems (CPS) are connected to perform smart manufacturing, we have a cyber-physical production system (CPPS). In such systems, CPS collaborate in an intelligent way to obtain and maintain the optimum of the manufacturing process, handle disturbances and adapt to changing conditions. This work, based on a systematic literature review, shows current trends in cyber-physical production systems with a special focus on the role of connectivity and control systems in production. We are looking for the enablers of CPPS focusing on the current evolution of control systems in the smart factory of the future. As a result, a conceptual framework as well as a CPPS control system architecture for the introduction and organisation of enablers for CPPS is presented to support practitioners in the realisation of smart manufacturing control concepts.

PurposeDespite the growing attention on the relevance of improved building management systems with cognition in recent years in the architecture, engineering, construction and operation (AECO) community, no review has been conducted to understand the human-environment interaction features of cyber-physical systems (CPS) and digital twins (DTs) in developing the concept of a cognitive building (CB). Thus, this paper aims to review existing studies on CPS and DTs for CB to propose a comprehensive system architecture that considers human-environment interactions.Design/methodology/approachScientometric analysis and content analysis were adopted for this study.FindingsThe scientometric analysis of 1,042 journal papers showed the major themes of CPS/DTs for CB, and these can be categorized into three key technologies to realize CB in the AECO community: CPS, DTs and cognitive computing (CC). Content analysis of 44 relevant publications in the built environment assisted in understanding and evidently confirming the claim of this study on the integration of CPS and DTs for CB in construction by also involving the CC. It is found and confirmed that CB can be realized with CPS and DTs along with the CC. A CB system architecture (CBSA) is proposed from the three key technologies considering the human-environment interactions in the loop. The study discovered the potential applications of the CBSA across the building lifecycle phases, including the design, construction and operations and maintenance, with the potential promise of endowing resilience, intelligence, greater efficiency and self-adaptiveness. Based on the findings of the review, four research directions are proposed: human-environment interactions, CB for sustainable building performance, CB concept for modular buildings and moving beyond CB.Originality/valueThis study stands out for comprehensively surveying the intellectual core and the landscape of the general body of knowledge on CPS/DTs for CB in the built environment. It makes a distinctive contribution to knowledge as it does not only propose CBSA by integrating CPS and DTs along with CC but also suggests some potential practical applications. These may require expert judgments and real case examples to enhance reproducibility and validation.

Using open-source microcontrollers to enable digital twin communication for smart manufacturing

The significance of digital twins in mechanical and plant engineering companies is becoming increasingly important in the course of Industry 4.0 as a cyber-physical system. Digital twins represent a real object in virtual space. The properties of the real object are simulated using structural and behavior models, so that the digital twin represents a spitting image of the real object in virtual space. The structural models and behavioral models are created by modeling the digital twin in different modeling and simulation domains. A common form of the digital twin in mechanical and plant engineering is a digital twin, which is based on a kinematic multibody simulation. The kinematic digital twin can be coupled with industrial controllers and is often used for the virtual commissioning of plants/machines. These digital twins usually represent the kinematic behavior of the plan/machine in an idealized way and neglect the physically determined system dynamics. By extending the digital twin by the generation of state variables of a multiphysical dynamic simulation and the use of these state variables by industrial programmable logic controller (PLCs), advantages arise in the use of the digital twin with respect to virtual commissioning, fault analysis, condition monitoring and simulative optimization of machines and plants. Thus, it is necessary to complement the kinematic digital twin by a multiphysical dynamic simulation and to connect this extended model of the digital twin to PLCs.

Digital Twins (DTs), being the virtual replicas of their physical counterparts, share valuable knowledge of the underlying physical processes and act as data acquisition and dissemination sources to Cyber–Physical System (CPS). Moreover, without obstructing the ongoing operations, DTs also provide an assessment platform for evaluating the operational behavior and security of the CPS. Therefore, they become a potential source of data breaches and a broad attack surface for attackers to launch covert attacks. To detect and mitigate security loopholes in DTs, one of the potential solutions is to leverage a gamification approach that can assess the security level of DTs while providing security analysts with a controlled and supportive virtual training environment. Artificial Intelligence/Machine Learning (AI/ML)-based approaches can complement the idea of security orchestration and automation in the gamification approach. However, AI/ML-based DTs security solutions are generally constrained by the lack of transparency of AI operations, which results in less confidence in the decisions made by the AI models. To address the explainable security challenges of DTs, this article proposes a gamification approach called sEcuriNg dIgital twins through GaMification Approach (ENIGMA). While leveraging DTs as an offensive security platform, ENIGMA provides gaming scenarios to assess DTs’ security and train security analysts. The game players within ENIGMA are humans (the attacker team) and AI agents (the defender team). Furthermore, ENIGMA is supported by an eXplainable AI (XAI)-based DT security assessment model that explains the decisions made based on the SHAP values by the AI model on attack vectors for the defender team, i.e., the AI agent. The SHAP values illustrate the contribution of different features towards predicting the outcome of attack vectors. This explanation can help security analysts to take security measures based on reasoned and trustworthy decisions. Finally, experimental validation has been carried out to demonstrate the viability of ENIGMA.

Nowadays, one important challenge in cyber-physical production systems is updating dynamic production schedules through an automated decision-making performed while the production is running. The condition of the manufacturing equipment may in fact lead to schedule unfeasibility or inefficiency, thus requiring responsiveness to preserve productivity and reduce the operational costs. In order to address current limitations of traditional scheduling methods, this work proposes a new framework that exploits the aggregation of several digital twins, representing different physical assets and their autonomous decision-making, together with a global digital twin, in order to perform production scheduling optimization when it is needed. The decision-making process is supported on a fuzzy inference system using the state or conditions of different assets and the production rate of the whole system. The condition of the assets is predicted by the condition-based monitoring modules in the local digital twins of the workstations, whereas the production rate is evaluated and assured by the global digital twin of the shop floor. This paper presents a framework for decentralized and integrated decision-making for re-scheduling of a cyber-physical production system, and the validation and proof-of-concept of the proposed method in an Industry 4.0 pilot line of assembly process. The experimental results demonstrate that the proposed framework is capable to detect changes in the manufacturing process and to make appropriate decisions for re-scheduling the process.

Digital Twins are emerging as a transformative technology within Cyber-Physical Systems (CPS), offering enhanced optimization, predictive maintenance, and real-time monitoring. However, their integration also introduces significant security challenges. These include vulnerabilities such as data breaches, unauthorized access, and cyber-attacks that disrupt real-time data flow between their physical and digital components. The involvement of IoT devices, sensors, and complex networked environments expands the attack surface, making Digital Twins susceptible to threats like Distributed Denial-of-Service (DDoS) attacks, malware infiltration, and insider sabotage. Effective risk management and assessment are crucial in identifying vulnerabilities, evaluating risks, and implementing mitigation strategies. Securing Digital Twins ensures data integrity, system reliability, and the continued functionality of the physical assets they represent. This paper aims to classify the various security threats associated with Digital Twins and propose structured risk management approaches to enhance their security within CPS. By addressing these challenges, organizations can ensure the dependability and trustworthiness of Digital Twin implementations across industries such as manufacturing, healthcare, smart cities, and IoT ecosystems.