scholarly journals Enterprise Service Remote Assistance Guidance System Based on Digital Twin Drive

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Dan Long ◽  
Rui Xu ◽  
Jia Liu ◽  
Wanghong Yu ◽  
Lei Xu
Keyword(s):  
Real Time ◽  
Industrial Revolution ◽  
Production Systems ◽  
Guidance System ◽  
Data Mapping ◽  
Digital Twin ◽  
Twin System ◽  
Control Optimization ◽  
Proposed Model ◽  
Remote Assistance

In the fourth industrial revolution to develop new products and processes, the digital twin, virtual copies of the system that can interact with the physical counterparts in a bidirectional way, seem to be promising enablers to replicate production systems in real time and analyze them. They aim to solve insufficient guidance methods in the existing enterprise service remote assistance guidance system. In this paper, a digital twin-driven enterprise service remote assistance guidance system is proposed. The digital twin system is designed to carry out different all-around analyses of the remote internal system. The digital and physical spaces of the enterprise service system are reset according to the data query results. The proposed model achieves the internal data mapping effect of the enterprise service and analyzes the internal data of the system. Based on the realization of real-time mapping and a large amount of twin data generated by virtual and real interaction, the data are visualized and stored in a database for the upper layers. The proposed model has been simulated, and the test results show its potential benefits for enterprise control, optimization, and forecasting and can provide essential support for realizing the twin’s optimized control of entities.

scholarly journals Digital Twin for Operation of Microgrid: Optimal Scheduling in Virtual Space of Digital Twin

Energies ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 5504
Author(s):  
Hyang-A Park ◽  
Gilsung Byeon ◽  
Wanbin Son ◽  
Hyung-Chul Jo ◽  
Jongyul Kim ◽  
...  
Keyword(s):  
Real Time ◽  
Industrial Revolution ◽  
Storage System ◽  
Research Field ◽  
Optimization Techniques ◽  
Virtual Space ◽  
Machine Learning Techniques ◽  
Time Data ◽  
Digital Twin ◽  
Learning Techniques

Due to the recent development of information and communication technology (ICT), various studies using real-time data are now being conducted. The microgrid research field is also evolving to enable intelligent operation of energy management through digitalization. Problems occur when operating the actual microgrid, causing issues such as difficulty in decision making and system abnormalities. Using digital twin technology, which is one of the technologies representing the fourth industrial revolution, it is possible to overcome these problems by changing the microgrid configuration and operating algorithms of virtual space in various ways and testing them in real time. In this study, we proposed an energy storage system (ESS) operation scheduling model to be applied to virtual space when constructing a microgrid using digital twin technology. An ESS optimal charging/discharging scheduling was established to minimize electricity bills and was implemented using supervised learning techniques such as the decision tree, NARX, and MARS models instead of existing optimization techniques. NARX and decision trees are machine learning techniques. MARS is a nonparametric regression model, and its application has been increasing. Its performance was analyzed by deriving performance evaluation indicators for each model. Using the proposed model, it was found in a case study that the amount of electricity bill savings when operating the ESS is greater than that incurred in the actual ESS operation. The suitability of the model was evaluated by a comparative analysis with the optimization-based ESS charging/discharging scheduling pattern.

scholarly journals The Digital Twin: Demonstrating the Potential of Real Time Data Acquisition in Production Systems

2017 ◽  
Vol 9 ◽  
pp. 113-120 ◽  
Author(s):  
Thomas H.-J. Uhlemann ◽  
Christoph Schock ◽  
Christian Lehmann ◽  
Stefan Freiberger ◽  
Rolf Steinhilper
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Production Systems ◽  
Time Data ◽  
Digital Twin ◽  
Real Time Data

INDUSTRIAL REAL-TIME DIGITAL TWIN SYSTEM FOR REMOTE TEACHING USING NODE-RED

2021 ◽  
Author(s):  
Marenice Melo de Carvalho ◽  
Isaías Valente de Bessa ◽  
Guido Soprano Machado ◽  
Renan Landau Paiva de Medeiros ◽  
Vicente Ferreira de Lucena Jr
Keyword(s):  
Real Time ◽  
Digital Twin ◽  
Twin System

scholarly journals An Enabling Open-Source Technology for Development and Prototyping of Production Systems by Applying Digital Twinning

Processes ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 21
Author(s):  
Robert Kazała ◽  
Sławomir Luściński ◽  
Paweł Strączyński ◽  
Albena Taneva
Keyword(s):  
Open Source ◽  
Production Systems ◽  
Communication Technologies ◽  
Digital Twin ◽  
Twin System ◽  
Digital Twins ◽  
First Choice ◽  
Automation And Control ◽  
And Control

This article presents the most valuable and applicable open-source tools and communication technologies that may be employed to create models of production processes by applying the concept of Digital Twins. In recent years, many open-source technologies, including tools and protocols, have been developed to create virtual models of production systems. The authors present the evolution and role of the Digital Twin concept as one of the key technologies for implementing the Industry 4.0 paradigm in automation and control. Based on the presented structured review of valuable open-source software dedicated to various phases and tasks that should be realised while creating the whole Digital Twin system, it was demonstrated that the available solutions cover all aspects. However, the dispersion, specialisation, and lack of integration cause this software to usually not be the first choice to implement DT. Therefore, to successfully create full-fledged models of Digital Twins by proceeding with proposed open-source solutions, it is necessary to make additional efforts due to integration requirements.

Data Flow and Communication Framework Supporting Digital Twin for Geometry Assurance

2017 ◽  
Author(s):  
Robert Bohlin ◽  
Jonas Hagmar ◽  
Kristofer Bengtsson ◽  
Lars Lindkvist ◽  
Johan S. Carlson ◽  
...  
Keyword(s):  
Real Time ◽  
Production Systems ◽  
Virtual Manufacturing ◽  
Real Time Control ◽  
Digital Twin ◽  
Sheet Metal Parts ◽  
Time Control ◽  
Project Smart ◽  
Smart Assembly ◽  
Individualized Production

Faster optimization algorithms, increased computer power and amount of available data, can leverage the area of simulation towards real-time control and optimization of products and production systems. This concept — often referred to as Digital Twin — enables real-time geometry assurance and allows moving from mass production to more individualized production. To master the challenges of a Digital Twin for Geometry Assurance the project Smart Assembly 4.0 gathers Swedish researchers within product development, automation, virtual manufacturing, control theory, data analysis and machine learning. The vision of Smart Assembly 4.0 is the autonomous, self-optimizing robotized assembly factory, which maximizes quality and throughput, while keeping flexibility and reducing cost, by a sensing, thinking and acting strategy. The concept is based on active part matching and self-adjusting equipment which improves geometric quality without tightening the tolerances of incoming parts. The goal is to assemble products with higher quality than the incoming parts. The concept utilizes information about individual parts to be joined (sensing), selects the best combination of parts (thinking) and adjust locator positions, clamps, weld/rivet positions and sequences (acting). The project is ongoing, and this paper specifies and highlights the infrastructure, components and data flows necessary in the Digital Twin in order to realize Smart Assembly 4.0. The framework is generic, but the paper focuses on a spot weld station where two robots join two sheet metal parts in an adjustable fixture.

scholarly journals Sustainable Industry 4.0 Wireless Networks, Machine Learning Algorithms, and Internet of Things-based Real-Time Production Logistics in Digital Twin-driven Smart Manufacturing

2021 ◽  
Vol 129 ◽  
pp. 04003
Author(s):  
Elvira Nica ◽  
Gheorghe H. Popescu ◽  
George Lăzăroiu
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Big Data ◽  
Internet Of Things ◽  
Real Time ◽  
Industry 4.0 ◽  
Production Systems ◽  
Smart Manufacturing ◽  
Digital Twin ◽  
Cyber Physical Production Systems

Research background: The aim of this paper is to synthesize and analyze existing evidence on artificial intelligence-based decision-making algorithms, industrial big data, and Internet of Things sensing networks in digital twin-driven smart manufacturing. Purpose of the article: Using and replicating data from Altair, Catapult, Deloitte, DHL, GAVS, PwC, and ZDNet we performed analyses and made estimates regarding cyber-physical system-based real-time monitoring, product decision-making information systems, and artificial intelligence data-driven Internet of Things systems in digital twin-based cyber-physical production systems. Methods: From the completed surveys, we calculated descriptive statistics of compiled data when appropriate. The data was weighted in a multistep process that accounts for multiple stages of sampling and nonresponse that occur at different points in the survey process. The precision of the online polls was measured using a Bayesian credibility interval. To ensure high-quality data, data quality checks were performed to identify any respondents showing clear patterns of satisficing. Test data was populated and analyzed in SPSS to ensure the logic and randomizations were working as intended before launching the survey. An Internet-based survey software program was utilized for the delivery and collection of responses. The sample weighting was accomplished using an iterative proportional fitting process that simultaneously balanced the distributions of all variables. The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau’s American Community Survey to reflect reliably and accurately the demographic composition of the United States. Confirmatory factor analysis was employed to test for the reliability and validity of measurement instruments. Findings & Value added: The way Internet of Things-based decision support systems, artificial intelligence-driven big data analytics, and robotic wireless sensor networks configure digital twin-driven smart manufacturing and cyber-physical production systems in sustainable Industry 4.0.

scholarly journals THE ROLE OF DIGITAL INFORMATION MODELS FOR HORIZONTAL AND VERTICAL INTERACTION IN INTELLIGENT PRODUCTION

2019 ◽  
Vol 17 (3) ◽  
pp. 397 ◽  
Author(s):  
Pancho Tomov ◽  
Lubomir Dimitrov
Keyword(s):  
Real Time ◽  
Industrial Revolution ◽  
Production Systems ◽  
Global Network ◽  
Business Practices ◽  
Digital Information ◽  
Time Data ◽  
Information Models ◽  
Service Oriented ◽  
Mandatory Standard

Intelligent production is the future of industrial production. It is the leading way to a new industrial era and it best defines the concept of the Fourth Industrial Revolution. Getting the real-time data on quality, resources and costs it provides significant advantages over classical production systems. Intelligent production must be built on sustainable and service-oriented technological and business practices. They are characterized by flexibility, adaptability and self-learning, resilience to failures, and risk management. The high levels of automation, on the other hand, become a mandatory standard for them, which is possible thanks to a flexible network of production-based systems that automatically monitor the production processes. Flexible systems and models that are capable of responding in real time allow internal processes to be radically optimized. Production benefits are not limited to one-off production conditions, and the capabilities include optimization through a global network of adaptive and self-regulating manufacturing components belonging to more than one operator.

Edge Computing Enhanced Digital Twins for Smart Manufacturing

2021 ◽  
Author(s):  
Huiyue Huang ◽  
Xun Xu
Keyword(s):  
Real Time ◽  
Information And Communication Technologies ◽  
Research Work ◽  
Edge Computing ◽  
Smart Manufacturing ◽  
Data Mapping ◽  
Digital Twin ◽  
Mapping Strategy ◽  
Digital Twins ◽  
Automation Systems

Abstract Digital Twin is one of the key enabling technologies for smart manufacturing in the context of Industry 4.0. The combination with advanced data analytics and information and communication technologies allows Digital Twins to perform real-time simulation, optimization and prediction to their physical counterparts. Efficient bi-directional data exchange is the foundation for Digital Twin implementation. However, the widely mentioned cloud-based architecture has disadvantages, such as high pressure on bandwidth and long latency time, which limit Digital Twins to provide real-time operating responses in dynamic manufacturing processes. Edge computing has the characteristics of low connectivity, the capability of immediate analysis and access to temporal data for real-time analytics, which makes it a fit-for-purpose technology for Digital Twin development. In this paper, the benefits of edge computing to Digital Twin are first explained through the reviews of the two technologies. The Digital Twin functions to be performed at the edge are then elaborated. After that, how the data model will be used in the edge for data mapping to realize the Digital Twin is illustrated and the data mapping strategy based on the EXPRESS schemas is discussed. Finally, a case study is carried out to verify the data mapping strategy based on EXPRESS schema. This research work refers to ISO/DIS 23247 Automation systems and integration — Digital Twin framework for manufacturing.

Emerging Industrial Revolution: Symbiosis of Industry 4.0 and Circular Economy: The Role of Universities

2020 ◽  
Vol 25 (3) ◽  
pp. 505-525 ◽  
Author(s):  
Seeram Ramakrishna ◽  
Alfred Ngowi ◽  
Henk De Jager ◽  
Bankole O. Awuzie
Keyword(s):  
Economic Growth ◽  
Circular Economy ◽  
Industry 4.0 ◽  
Business Models ◽  
Industrial Revolution ◽  
New Technologies ◽  
Production Systems ◽  
Fourth Industrial Revolution ◽  
New Jobs

Growing consumerism and population worldwide raises concerns about society’s sustainability aspirations. This has led to calls for concerted efforts to shift from the linear economy to a circular economy (CE), which are gaining momentum globally. CE approaches lead to a zero-waste scenario of economic growth and sustainable development. These approaches are based on semi-scientific and empirical concepts with technologies enabling 3Rs (reduce, reuse, recycle) and 6Rs (reuse, recycle, redesign, remanufacture, reduce, recover). Studies estimate that the transition to a CE would save the world in excess of a trillion dollars annually while creating new jobs, business opportunities and economic growth. The emerging industrial revolution will enhance the symbiotic pursuit of new technologies and CE to transform extant production systems and business models for sustainability. This article examines the trends, availability and readiness of fourth industrial revolution (4IR or industry 4.0) technologies (for example, Internet of Things [IoT], artificial intelligence [AI] and nanotechnology) to support and promote CE transitions within the higher education institutional context. Furthermore, it elucidates the role of universities as living laboratories for experimenting the utility of industry 4.0 technologies in driving the shift towards CE futures. The article concludes that universities should play a pivotal role in engendering CE transitions.

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