Digital-Twins towards Cyber-Physical Systems: A Brief Survey

Cyber-Physical Systems (CPS) are integrations of computation and physical processes. Physical processes are monitored and controlled by embedded computers and networks, which frequently have feedback loops where physical processes affect computations and vice versa. To ease the analysis of a system, the costly physical plants can be replaced by the high-fidelity virtual models that provide a framework for Digital-Twins (DT). This paper aims to briefly review the state-of-the-art and recent developments in DT and CPS. Three main components in CPS, including communication, control, and computation, are reviewed. Besides, the main tools and methodologies required for implementing practical DT are discussed by following the main applications of DT in the fourth industrial revolution through aspects of smart manufacturing, sixth wireless generation (6G), health, production, energy, and so on. Finally, the main limitations and ideas for future remarks are talked about followed by a short guideline for real-world application of DT towards CPS

COOCK project Smart Port 2025 D3.1: "To Twin Or Not To Twin"

This document is a result of the COOCK project "Smart Port 2025: improving and accelerating the operational efficiency of a harbour eco-system through the application of intelligent technologies". It reports on the needs of companies for modelling and simulation and AI-based techniques, with twinning systems in particular. This document categorizes the purposes and Properties of Interest for the use of Digital Twins. It further illustrates some of the twinning usages, and touches on some of the potential architectural compositions for twins. This last topic will be further elaborated in a followup report

Industrial internet of things: What does it mean for the bioprocess industries?

Industrial Internet of Things (IIoT) is a system of interconnected devices that, via the use of various technologies, such as soft sensors, cloud computing, data analytics, machine learning and artificial intelligence, provides real-time insight into the operations of any industrial process from product conceptualisation, process optimisation and manufacturing to the supply chain. IIoT enables wide-scope data collection and utilisation, and reduces errors, increases efficiency, and provides an improved understanding of the process in return. While this novel solution is the pillar of Industry 4.0, the inherent operational complexity of bioprocessing arising from the involvement of living systems or their components in manufacturing renders the sector a challenging one for the implementation of IIoT. A large segment of the industry comprises the manufacturing of biopharmaceuticals and advanced therapies, some of the most valuable biotechnological products available, which undergo tight regulatory evaluations and scrutinization from product conceptualisation to patient delivery. Extensive process understanding is what biopharmaceutical industry strives for, however, the complexity of transition into a new mode of operation, potential misalignment of priorities, the need for substantial investments to facilitate transition, the limitations imposed by the downtime required for transition and the essentiality of regulatory support, render it challenging for the industry to adopt IIoT solutions to integrate with biomanufacturing operations. There is currently a need for universal solutions that would streamline the implementation of IIoT and overcome the widespread reluctance observed in the sector, which will recommend accessible implementation strategies, effective employee training and offer valuable insights in return to advance any processing and manufacturing operation within their respective regulatory frameworks

Collaborative approaches in sustainable and resilient manufacturing

Publisher Copyright: © 2022, The Author(s).In recent years, the manufacturing sector is going through a major transformation, as reflected in the concept of Industry 4.0 and digital transformation. The urge for such transformation is intensified when we consider the growing societal demands for sustainability. The notion of sustainable manufacturing has emerged as a result of this trend. Additionally, industries and the whole society face the challenges of an increasing number of disruptive events, either natural or human-caused, that can severely affect the normal operation of systems. Furthermore, the growing interconnectivity between organizations, people, and physical systems, supported by recent developments in information and communication technologies, highlights the important role that collaborative networks can play in the digital transformation processes. As such, this article analyses potential synergies between the areas of sustainable and resilient manufacturing and collaborative networks. The work also discusses how the responsibility for the various facets of sustainability can be distributed among the multiple entities involved in manufacturing. The study is based on a literature survey, complemented with the experience gained from various research projects and related initiatives in the area, and is organized according to various dimensions of Industry 4.0. A brief review of proposed approaches and indicators for measuring sustainability from the networked manufacturing perspective is also included. Finally, a set of key research challenges are identified to complement strategic research agendas in manufacturing.publishersversionpublishe

Evaluating different strategies to achieve the highest geometric quality in self-adjusting smart assembly lines

Digital twin-driven productions have opened great opportunities to increase the efficiency and quality of production processes. Smart assembly lines are one of these opportunities in which the effects of geometric variations of the mating parts on the assemblies can be minimized. These assembly lines utilize different techniques, including selective assembly and locator adjustments, to improve the geometric quality. This paper signifies that the achievable improvements through these techniques are highly dependent on the utilized fixture layout for the assembly process. Hence, different design methods and productions that can be followed in a smart assembly line are discussed. Furthermore, different scenarios are applied to two industrial sample cases from the automotive industry. The aptest design strategy for each improvement technique is determined. Moreover, the strategy that can result in the highest geometric quality of assemblies through a smart assembly line is defined

Designing a Blockchain-Based Digital Twin for Cyber-Physical Production Systems

Trust in all processes on the shopfloor is crucial for the success of a production process, especially in cross company scenarios such as shared manufacturing, in which independent parties interact with each other. A cyber-physical production system (CPPS) contributes to the vision of a decentralized, self-configuring and flexible production. Digital twins (DTs) can visualize the material, information and financial flows in real time and improve the process transparency of such production systems. The efficiency of digital twins depends on the integrity of the provided data, especially if data is shared across company borders. Due to its characteristics such as immutability and transparency, blockchain technology (BCT) provides a basis for establishing the desired trust in the systems on the shopfloor. This paper proposes the design of a BCT-based DT in CPPS. The design is demonstrated by a prototype including smart contracts attached to a CPPS simulation model visualizing the information and material flow. Tasks are decentrally allocated, deployed and safely documented via blockchain. The demonstrator is revealing supplementary benefits in terms of transparency provided by the BCT. This paper further examines whether BCT can enrich existing solutions and provide a reliable information basis for profound data and process analysis

Combining symbiotic simulation systems with enterprise data storage systems for real-time decision-making

[EN] A symbiotic simulation system (S3) enables interactions between a physical system and its computational model representation. To support operational decisions, an S3 uses real-time data from the physical system, which is gathered via sensors and saved in an enterprise data storage system (EDSS). Both real-time and historical data are then used as inputs to the different components of an S3. This paper proposes a generic system architecture for an S3 and discusses its integration within EDSSs. The paper also reviews the literature on S3 and analyses how these systems can be used for real-time decision-making.This work has been partially funded by the Staff Mobility programme from the Erasmus+ (2020-2021).Onggo, B.; Corlu, CG.; Juan, AA.; Monks, T.; Torre-Martínez, MRDL. (2021). Combining symbiotic simulation systems with enterprise data storage systems for real-time decision-making. Enterprise Information Systems. 15(2):230-247. https://doi.org/10.1080/17517575.2020.177758723024715

Demystifying Digital Twin Buzzword: A Novel Generic Evaluation Model

Despite the growing popularity of digital twin (DT) developments, there is a lack of common understanding and definition for important concepts of DT. It is needed to address this gap by building a shared understanding of DT before it becomes an obstacle for future work. With this challenge in view, the objective of our study is to assess the existing DT from various domains on a common basis and to unify the knowledge and understanding of DT developers and stakeholders before practice. To achieve this goal, we conducted a systematic literature review and analyzed 25 selected papers to identify and discuss the characteristics of existing DT's. The review shows an inconsistency and case-specific choices of dimensions in assessing DT. Therefore, this article proposes a four-dimensional evaluation framework to assess the maturity of digital twins across different domains, focusing on the characteristics of digital models. The four identified dimensions in this model are Capability, Cooperability, Coverage, and Lifecycle. Additionally, a weight mechanism is implemented inside the model to adapt the importance of each dimension for different application requirements. Several case studies are devised to validate the proposed model in general, industrial and scientific cases.Comment: This is a draft of the article that subject to future change and correctio