“…Modelling the production system level includes the production system behaviour analysis. Note: Such models are also used to optimize the load of mechanical production [15]. Figure 3Digital model of a robotic injection moulding cell.…”
Section: Resultsmentioning
confidence: 99%
“…The development of digital twins of the production systems and processes includes modelling and simulation of objects and processes, organization of the collection, processing and storage of data, as well as identification and traceability of production objects [14]. In this sense, it is interesting to address that in [15] it was noted that a digital twin of a production system is understood as a multiscale digital layout that allows modelling processes to occur in a real system and collection and display of the real-time status of objects obtained from the PLC and sensors installed in the production system. Moreover, in [16], it is noted that the transition of traditional production systems to smart production cannot be carried out without preceding complex digitalization.…”
Section: Methodsmentioning
confidence: 99%
“…In the development of the simulation model of the injection moulding process, the production system is considered on three levels: technological operation level, manufacturing process level and production process level [15].…”
Section: (B) Integrated Multiscale Modelling and Simulation For The Injection Moulding Processmentioning
The article describes an industrial cyber-physical platform for small series production using digital twins under development at ITMO University (Saint Petersburg, Russia). The platform is based on the following approaches: group technology, adaptive and selective assembling, and digital twin of production systems and processes. The article presents a mechanism for constructing a unified manufacturing process, and results of an integrated multiscale simulation of an injection moulding process. The issues of ensuring identification and monitoring of objects of the industrial cyber-physical platform are considered. Specific service applications required to implement the smart product concept are discussed. The combination of the considered technologies is used to create digital twins of production system objects. All humans that have different roles in the product value stream can interact with the industrial cyber-physical platform at the three levels, receiving support in performing their tasks.
This article is part of the theme issue ‘Towards symbiotic autonomous systems’.
“…Modelling the production system level includes the production system behaviour analysis. Note: Such models are also used to optimize the load of mechanical production [15]. Figure 3Digital model of a robotic injection moulding cell.…”
Section: Resultsmentioning
confidence: 99%
“…The development of digital twins of the production systems and processes includes modelling and simulation of objects and processes, organization of the collection, processing and storage of data, as well as identification and traceability of production objects [14]. In this sense, it is interesting to address that in [15] it was noted that a digital twin of a production system is understood as a multiscale digital layout that allows modelling processes to occur in a real system and collection and display of the real-time status of objects obtained from the PLC and sensors installed in the production system. Moreover, in [16], it is noted that the transition of traditional production systems to smart production cannot be carried out without preceding complex digitalization.…”
Section: Methodsmentioning
confidence: 99%
“…In the development of the simulation model of the injection moulding process, the production system is considered on three levels: technological operation level, manufacturing process level and production process level [15].…”
Section: (B) Integrated Multiscale Modelling and Simulation For The Injection Moulding Processmentioning
The article describes an industrial cyber-physical platform for small series production using digital twins under development at ITMO University (Saint Petersburg, Russia). The platform is based on the following approaches: group technology, adaptive and selective assembling, and digital twin of production systems and processes. The article presents a mechanism for constructing a unified manufacturing process, and results of an integrated multiscale simulation of an injection moulding process. The issues of ensuring identification and monitoring of objects of the industrial cyber-physical platform are considered. Specific service applications required to implement the smart product concept are discussed. The combination of the considered technologies is used to create digital twins of production system objects. All humans that have different roles in the product value stream can interact with the industrial cyber-physical platform at the three levels, receiving support in performing their tasks.
This article is part of the theme issue ‘Towards symbiotic autonomous systems’.
“…Since the first-ever definition published by NASA, different authors have described DT in their own terms and based on its application. The definitions in the literature have referred to DT as a virtual or digital model [21,23,[27][28][29][30][31][32][33][34][35], layout [36], counterpart [7,9,35,37], doppelganger [38], clone [39], footprint [40], software analogue [41], representation [42][43][44][45][46], information constructs [2,47], or simulation [20,26,[48][49][50][51] of its physical counterpart. The first few definitions of DT that came out described DT only in respect to the aerospace/aeronautics industry which then ventured into manufacturing, which is why the words such as 'aircraft' [23,25,30], 'vehicle' [20,26,48], or 'airframe' [25,29] were replaced by 'system' [2,7,21,22,…”
Section: Digital Twin Description In Literaturementioning
Digital Twin (DT) refers to the virtual copy or model of any physical entity (physical twin) both of which are interconnected via exchange of data in real time. Conceptually, a DT mimics the state of its physical twin in real time and vice versa. Application of DT includes real-time monitoring, designing/planning, optimization, maintenance, remote access, etc. Its implementation is expected to grow exponentially in the coming decades. The advent of Industry 4.0 has brought complex industrial systems that are more autonomous, smart, and highly interconnected. These systems generate considerable amounts of data useful for several applications such as improving performance, predictive maintenance, training, etc. A sudden influx in the number of publications related to ‘Digital Twin’ has led to confusion between different terminologies related to the digitalization of industries. Another problem that has arisen due to the growing popularity of DT is a lack of consensus on the description of DT as well as so many different types of DT, which adds to the confusion. This paper intends to consolidate the different types of DT and different definitions of DT throughout the literature for easy identification of DT from the rest of the complimentary terms such as ‘product avatar’, ‘digital thread’, ‘digital model’, and ‘digital shadow’. The paper looks at the concept of DT since its inception to its predicted future to realize the value it can bring to certain sectors. Understanding the characteristics and types of DT while weighing its pros and cons is essential for any researcher, business, or sector before investing in the technology.
“…This contributes to the creation of smart cities which use ICT to facilitate information sharing with the public. However, while they offer numerous benefits, there are also a number of associated challenges and chief among these is the security threat (Demkovich and Yablochnikov, 2018). At present, there are distinct challenges when attempting to install cybersecurity into digital twins that will be utilised in applications designed for the built environment.…”
Recent technological developments in the construction industry are seeking to create smart cities by using Cyber-Physical Systems (CPSs) to enhance information models such as BIM. Currently, BIM models are commonly adopted to work with IoT-based systems and embrace smart technologies that offer interoperability in the communication layer. In future, it is envisioned that digital twins will provide new possibilities for cyber-physical systems via monitoring and simulation. However, rarely in this rapidly developing field is security fully considered. This paper reviews the relevant literature regarding the use of the IoT in the built environment and analyses current practices. It also presents examples of cities that use the IoT to improve construction and the lived experience. Finally, it reviews how digital twins factor in multiple layers defined in CPSs, from physical objects to information models. Based on this review, recommendations are provided documenting how BIM specifications can be expanded to become IoT compliant, enhancing standards to support cybersecurity, and ensuring digital twin and city standards can be fully integrated in future secure smart cities.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
