Robust Digital Twin Compositions for Industry 4.0 Smart Manufacturing Systems

Industry 4.0 is an emerging business paradigm that is reaping the benefits of enabling technologies driving intelligent systems and environments. By acquiring, processing and acting upon various kinds of relevant context information, smart automated manufacturing systems can make well-informed decisions to adapt and optimize their production processes at runtime. To manage this complexity, the manufacturing world is proposing the ‘Digital Twin’ model to represent physical products in the real space and their virtual counterparts in the virtual space, with data connections to tie the virtual and real products together for an augmented view of the manufacturing workflow. The benefits of such representations are simplified process simulations and efficiency optimizations, predictions, early warnings, etc. However, the robustness and fidelity of digital twins are a critical concern, especially when independently developed production systems and corresponding digital twins interfere with one another in a manufacturing workflow and jeopardize the proper behavior of production systems. We therefore evaluate the addition of safeguards to digital twins for smart cyber-physical production systems (CPPS) in an Industry 4.0 manufacturing workflow in the form of feature toggles that are managed at runtime by software circuit breakers. Our evaluation shows how these improvements can increase the robustness of interacting digital twins by avoiding local errors from cascading through the distributed production or manufacturing workflow.status: publishe

Artificial Intelligent Enabled Supply Chains as a Competitive Advantage

The focus of this paper is on the topics of artificial intelligence and supply chain management and how artificial intelligence-enabled supply chains provide organizations with competitive advantages. The supply chain’s adoption of data collection technologies as part of digital transformation and movements of industry 4.0 creates a strong foundation for artificial intelligence analytics. Artificial intelligence has three branches sensing and interacting, decision-making, and learning. Each branch uses its algorithms and serves a different purpose for the business. Artificial intelligence-enabled supply chains create unique, inimitable competitive advantages that fit Michael Porter’s five forces

Reinforcement Learning for UAV Attitude Control

Autopilot systems are typically composed of an "inner loop" providing stability and control, while an "outer loop" is responsible for mission-level objectives, e.g. way-point navigation. Autopilot systems for UAVs are predominately implemented using Proportional, Integral Derivative (PID) control systems, which have demonstrated exceptional performance in stable environments. However more sophisticated control is required to operate in unpredictable, and harsh environments. Intelligent flight control systems is an active area of research addressing limitations of PID control most recently through the use of reinforcement learning (RL) which has had success in other applications such as robotics. However previous work has focused primarily on using RL at the mission-level controller. In this work, we investigate the performance and accuracy of the inner control loop providing attitude control when using intelligent flight control systems trained with the state-of-the-art RL algorithms, Deep Deterministic Gradient Policy (DDGP), Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO). To investigate these unknowns we first developed an open-source high-fidelity simulation environment to train a flight controller attitude control of a quadrotor through RL. We then use our environment to compare their performance to that of a PID controller to identify if using RL is appropriate in high-precision, time-critical flight control.Comment: 13 pages, 9 figure

Holistic Security and Safety for Factories of the Future

The accelerating transition of traditional industrial processes towards fully automated and intelligent manufacturing is being witnessed in almost all segments. This major adoption of enhanced technology and digitization processes has been originally embraced by the Factories of the Future and Industry 4.0 initiatives. The overall aim is to create smarter, more sustainable, and more resilient future-oriented factories. Unsurprisingly, introducing new production paradigms based on technologies such as machine learning (ML), the Internet of Things (IoT), and robotics does not come at no cost as each newly incorporated technique poses various safety and security challenges. Similarly, the integration required between these techniques to establish a unified and fully interconnected environment contributes to additional threats and risks in the Factories of the Future. Accumulating and analyzing seemingly unrelated activities, occurring simultaneously in different parts of the factory, is essential to establish cyber situational awareness of the investigated environment. Our work contributes to these efforts, in essence by envisioning and implementing the SMS-DT, an integrated platform to simulate and monitor industrial conditions in a digital twin-based architecture. SMS-DT is represented in a three-tier architecture comprising the involved data and control flows: edge, platform, and enterprise tiers. The goal of our platform is to capture, analyze, and correlate a wide range of events being tracked by sensors and systems in various domains of the factory. For this aim, multiple components have been developed on the basis of artificial intelligence to simulate dominant aspects in industries, including network analysis, energy optimization, and worker behavior. A data lake was also used to store collected information, and a set of intelligent services was delivered on the basis of innovative analysis and learning approaches. Finally, the platform was tested in a textile industry environment and integrated with its ERP system. Two misuse cases were simulated to track the factory machines, systems, and people and to assess the role of SMS-DT correlation mechanisms in preventing intentional and unintentional actions. The results of these misuse case simulations showed how the SMS-DT platform can intervene in two domains in the first scenario and three in the second one, resulting in correlating the alerts and reporting them to security operators in the multi-domain intelligent correlation dashboard.The present work has been developed under the EUREKA ITEA3 Project Cyber-Factory#1 (ITEA-17032) and Project CyberFactory#1PT (ANI—P2020 40124) co-funded by Portugal 2020. Furthermore, this work also received funding from the project UIDB/00760/2020.info:eu-repo/semantics/publishedVersio

Semantic-aware Digital Twin for Metaverse: A Comprehensive Review

To facilitate the deployment of digital twins in Metaverse, the paradigm with semantic awareness has been proposed as a means for enabling accurate and task-oriented information extraction with inherent intelligence. However, this framework requires all devices in the Metaverse environment to be directly linked with the semantic model to enable faithful interpretation of messages. In contrast, this article introduces the digital twin framework, considering a smart industrial application, which enables semantic communication in conjugation with the Metaverse enabling technologies. The fundamentals of this framework are demonstrated on an industrial shopfloor management use case with a digital twin so as to improve its performance through semantic communication. An overview of semantic communication, Metaverse, and digital twins is presented. Integration of these technologies with the basic architecture as well as the impact on future industrial applications is presented. In a nutshell, this article showcases how semantic awareness can be an effective candidate in the implementation of digital twins for Metaverse applications.Comment: 9 pages, 5 figures, 1 tabl

Understanding the Digital Companions of Our Future Generation

The main protagonist in Kazuo Ishiguro’s latest novel is Klara, an artificial friend whose existential goal is to be children’s companion. Some aspects of this fictional narrative have begun to gradually enter our daily lives. Products reminiscent of Klara are available abundantly on the market: smart toys, adaptive learning applications, and companion robots. Children can relate to these products and perform activities together with them. Preliminary research has shown fundamental differences between existing technologies and these emerging children’s digital companions. However, we still do not know much about their benefits and risks. This paper explores different and even contradicting perspectives on the phenomenon. We present the discussion from four perspectives - temporality, use, trust and ethics, and sociotechnical design - and conclude the paper with an agenda for interdisciplinary IS research. The agenda points to the needs for a psychological, medical, engineering, and temporal research community to understand this emerging sociotechnical phenomenon and design its future for the better

Digital Transformation

The amount of literature on Digital Transformation is staggering—and it keeps growing. Why, then, come out with yet another such document? Moreover, any text aiming at explaining the Digital Transformation by presenting a snapshot is going to become obsolete in a blink of an eye, most likely to be already obsolete at the time it is first published. The FDC Initiative on Digital Reality felt there is a need to look at the Digital Transformation from the point of view of a profound change that is pervading the entire society—a change made possible by technology and that keeps changing due to technology evolution opening new possibilities but is also a change happening because it has strong economic reasons. The direction of this change is not easy to predict because it is steered by a cultural evolution of society, an evolution that is happening in niches and that may expand rapidly to larger constituencies and as rapidly may fade away. This creation, selection by experimentation, adoption, and sudden disappearance, is what makes the whole scenario so unpredictable and continuously changing.The amount of literature on Digital Transformation is staggering—and it keeps growing. Why, then, come out with yet another such document? Moreover, any text aiming at explaining the Digital Transformation by presenting a snapshot is going to become obsolete in a blink of an eye, most likely to be already obsolete at the time it is first published. The FDC Initiative on Digital Reality felt there is a need to look at the Digital Transformation from the point of view of a profound change that is pervading the entire society—a change made possible by technology and that keeps changing due to technology evolution opening new possibilities but is also a change happening because it has strong economic reasons. The direction of this change is not easy to predict because it is steered by a cultural evolution of society, an evolution that is happening in niches and that may expand rapidly to larger constituencies and as rapidly may fade away. This creation, selection by experimentation, adoption, and sudden disappearance, is what makes the whole scenario so unpredictable and continuously changing

Digital Twins for Industry 4.0 in the 6G Era

Having the Fifth Generation (5G) mobile communication system recently rolled out in many countries, the wireless community is now setting its eyes on the next era of Sixth Generation (6G). Inheriting from 5G its focus on industrial use cases, 6G is envisaged to become the infrastructural backbone of future intelligent industry. Especially, a combination of 6G and the emerging technologies of Digital Twins (DT) will give impetus to the next evolution of Industry 4.0 (I4.0) systems. This article provides a survey in the research area of 6G-empowered industrial DT system. With a novel vision of 6G industrial DT ecosystem, this survey discusses the ambitions and potential applications of industrial DT in the 6G era, identifying the emerging challenges as well as the key enabling technologies. The introduced ecosystem is supposed to bridge the gaps between humans, machines, and the data infrastructure, and therewith enable numerous novel application scenarios.Comment: Accepted for publication in IEEE Open Journal of Vehicular Technolog

Digital Twins and the Future of their Use Enabling Shift Left and Shift Right Cybersecurity Operations

Digital Twins (DTs), optimize operations and monitor performance in Smart Critical Systems (SCS) domains like smart grids and manufacturing. DT-based cybersecurity solutions are in their infancy, lacking a unified strategy to overcome challenges spanning next three to five decades. These challenges include reliable data accessibility from Cyber-Physical Systems (CPS), operating in unpredictable environments. Reliable data sources are pivotal for intelligent cybersecurity operations aided with underlying modeling capabilities across the SCS lifecycle, necessitating a DT. To address these challenges, we propose Security Digital Twins (SDTs) collecting realtime data from CPS, requiring the Shift Left and Shift Right (SLSR) design paradigm for SDT to implement both design time and runtime cybersecurity operations. Incorporating virtual CPS components (VC) in Cloud/Edge, data fusion to SDT models is enabled with high reliability, providing threat insights and enhancing cyber resilience. VC-enabled SDT ensures accurate data feeds for security monitoring for both design and runtime. This design paradigm shift propagates innovative SDT modeling and analytics for securing future critical systems. This vision paper outlines intelligent SDT design through innovative techniques, exploring hybrid intelligence with data-driven and rule-based semantic SDT models. Various operational use cases are discussed for securing smart critical systems through underlying modeling and analytics capabilities.Comment: IEEE Submitted Paper: Trust, Privacy and Security in Intelligent Systems, and Application