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

A conceptual framework for smart production planning and control in Industry 4.0

[EN] This article aims to introduce the challenge (i.e., integration of new collaborative models and tools) posed by the automation and collaboration of industrial processes in Industry 4.0 (I4.0) smart factories. Small- and medium-sized enterprises (SMEs) are particularly confronted with new technological and organisational changes, but a conceptual framework for production planning and control (PPC) systems in the I4.0 context is lacking. The main contributions of this article are to: (i) identify the functions making up traditional PPC and smart production planning and control in I4.0 (SPPC 4.0); (ii) analyse the impact of I4.0 technologies on PPC systems; (iii) propose a conceptual framework that provides the systematic structuring of how a PPC system operates in the I4.0 context, dubbed SPPC 4.0. Thus SPPC 4.0 is proposed by adopting the axes of the RAMI 4.0 reference architecture model, which compiles and contains the main concepts of PPC systems and I4.0. It also provides the technical description, organisation and understanding of each aspect, which can provide a guide for academic research and industrial practitioners to transform PPC systems towards I4.0 implementations. Finally, theoretical implications and research gaps are provided.The research leading to these results received funding from the European Union H2020 Program with grant agreements No. 958205 "Industrial Data Services for Quality Control in Smart Manufacturing (i4Q)" and No. 825631 "Zero-Defect Manufacturing Platform (ZDMP)"; the "Industrial Production and Logistics Optimization in Industry 4.0" (i4OPT) (Ref. PROMETEO/2021/065) project granted by the Valencian Regional Government; and the PAI-12-21 open-access support from the Universitat Politecnica de Valencia.Cañas, H.; Mula, J.; Campuzano-Bolarín, F.; Poler, R. (2022). A conceptual framework for smart production planning and control in Industry 4.0. Computers & Industrial Engineering. 173:1-12. https://doi.org/10.1016/j.cie.2022.10865911217

Study applying simulation to improve a real production process in the context of Industry 4.0

During the thesis development, simulation theories and techniques will be applied to a part of the production process of an Italian manufacturing company. A simulation model of the steaming and washing phases will be developed to outline the automated and manual procedures that are performed in the AS-IS state. Several what-if scenarios will be then envisioned and simulated to analyze how the production activities could be re-engineered in the light of the new technological advancements, such as the introduction of full traceability

Simulation-based Digital Twin for Distributed Manufacturing Control Systems

The fast pace at which industrial revolutions have been taking place depicts the importance of production systems for human beings. The increasing need to improve products’ quality and production lines’ efficiency without disregarding the human factor has compelled engineers to come up with innovative solutions. Consequently, the ongoing Industrial Revolution is leading to the emergence of new concepts, amongst which Digital Twins (DTs) stand out. Given their early stage, the already existing implementations are far from standardised, meaning that each practical case has to be analysed on its own and solutions are often created from scratch. Despite their current lack of modularity, the powerful predictive and monitoring capabilities featured in such implementations make DTs a rather interesting subject. This work suggests an architecture which allows the integration between a previously programmed manufacturing unit simulator and its DT, implemented and calibrated within the scope of this project. In spite of the physical asset itself being represented by a simulator, the suggested solution is equally applicable to a real-life scenario with a full-size conveyor network. Moreover, a thorough validation process is carried out, allowing the broadening of the knowledge on the physical system’s performance traits. Nevertheless, its somewhat unforeseeable behaviour hinders the modelling, thus making room for future work on how to make the DT’s predictions more accurate.A celeridade com que se têm verificado revoluções industriais retratam a importância dos sistemas de produção para os seres humanos. A crescente necessidade de melhorar a qualidade dos produtos e a eficiência das linhas de produção sem ignorar o fator humano tem obrigado os engenheiros a pensar em soluções inovadoras. Consequentemente, a Revolução Industrial que se encontra em curso está a levar ao aparecimento de novos conceitos, entre os quais se destacam os Gémeos Digitais. Dada a sua fase embrionária, as implementações já existentes estão longe de padronizadas, pelo que cada caso prático tem de ser analisado por si só e as soluções são muitas vezes criadas de raiz. Apesar da sua atual falta de modularidade, as poderosas capacidades de previsão e monitorização apresentadas por tais implementações tornam os Gémeos Digitais num tópico bastante interessante. Este trabalho sugere uma arquitetura que permite a integração entre um simulador de uma unidade de manufatura, previamente programado, e o seu Gémeo Digital, implementado e calibrado no âmbito deste projeto. Apesar de o próprio sistema físico ser representado por um simulador, a solução sugerida é igualmente aplicável a um cenário com uma rede de transporte à escala real. Adicionalmente, é descrito um processo de validação minucioso, que permite alargar o conhecimento sobre o desempenho geral do sistema físico. Não obstante, o seu comportamento algo imprevisível impõe obstáculos à modelação, algo que poderá vir a ser melhorado, futuramente, de modo a que as previsões feitas pelo Gémeo Digital sejam mais exatas

Mist and Edge Computing Cyber-Physical Human-Centered Systems for Industry 5.0: A Cost-Effective IoT Thermal Imaging Safety System

While many companies worldwide are still striving to adjust to Industry 4.0 principles, the transition to Industry 5.0 is already underway. Under such a paradigm, Cyber-Physical Human-centered Systems (CPHSs) have emerged to leverage operator capabilities in order to meet the goals of complex manufacturing systems towards human-centricity, resilience and sustainability. This article first describes the essential concepts for the development of Industry 5.0 CPHSs and then analyzes the latest CPHSs, identifying their main design requirements and key implementation components. Moreover, the major challenges for the development of such CPHSs are outlined. Next, to illustrate the previously described concepts, a real-world Industry 5.0 CPHS is presented. Such a CPHS enables increased operator safety and operation tracking in manufacturing processes that rely on collaborative robots and heavy machinery. Specifically, the proposed use case consists of a workshop where a smarter use of resources is required, and human proximity detection determines when machinery should be working or not in order to avoid incidents or accidents involving such machinery. The proposed CPHS makes use of a hybrid edge computing architecture with smart mist computing nodes that processes thermal images and reacts to prevent industrial safety issues. The performed experiments show that, in the selected real-world scenario, the developed CPHS algorithms are able to detect human presence with low-power devices (with a Raspberry Pi 3B) in a fast and accurate way (in less than 10 ms with a 97.04% accuracy), thus being an effective solution that can be integrated into many Industry 5.0 applications. Finally, this article provides specific guidelines that will help future developers and managers to overcome the challenges that will arise when deploying the next generation of CPHSs for smart and sustainable manufacturing.Comment: 32 page

Digital twin inception in the Era of industrial metaverse

Digital Twins, as a technological pillar of Industry 4.0, correspond to the virtual representation and bi-fold a real-time communication of a digital counterpart of a process or a physical object. As the industrial and manufacturing landscape is shifting towards Industry 5.0, huge investments focusing on enhancing interactions between Operators and Cyber-Physical Systems (CPS) occur. Yet, Metaverse strengthens these interactions as it enables human immersion into a virtual world. Furthermore, it examines the very promising relationships between the CPS, through the digital twins of these CPS. Therefore, this short review presents the concept of the Digital Twin inception in Industrial Metaverse. Additionally, a service-oriented digital twin architecture with Metaverse-enabled platforms for added value creation and interactions with CPS towards achieving Industry 5.0 challenges and beyond is proposed

Digital Twin Technology

Digital twin technology is considered to be the core technology of realizing Cyber-Physical System (CPS). It is the simulation technology that integrates multidisciplinary, multiphysical quantity, multiscale and multi probability by making full use of physical model, sensor update, operation history and other data. It is the mapping technology for the whole lifecycle process of physical equipment in virtual space. It is the basic technology of Industrial 4.0. This chapter mainly introduces: (1) the generation of digital twin technology; (2) the definition and characteristics of digital twin technology; (3) the relationship between digital twin and digital thread; (4) the implementation of the product digital twin model; and (5) the research progress and application of digital twin research