scholarly journals A Five-Step Approach to Planning Data-Driven Digital Twins for Discrete Manufacturing Systems

2021 ◽  
Vol 11 (8) ◽  
pp. 3639
Author(s):  
Matevz Resman ◽  
Jernej Protner ◽  
Marko Simic ◽  
Niko Herakovic
Keyword(s):  
Manufacturing Systems ◽  
Manufacturing System ◽  
Multiple Scales ◽  
Building Blocks ◽  
Heterogeneous Data ◽  
Digital Model ◽  
Data Driven ◽  
Digital Models ◽  
Digital Twin ◽  
Digital Twins

A digital twin of a manufacturing system is a digital copy of the physical manufacturing system that consists of various digital models at multiple scales and levels. Digital twins that communicate with their physical counterparts throughout their lifecycle are the basis for data-driven factories. The problem with developing digital models that form the digital twin is that they operate with large amounts of heterogeneous data. Since the models represent simplifications of the physical world, managing the heterogeneous data and linking the data with the digital twin represent a challenge. The paper proposes a five-step approach to planning data-driven digital twins of manufacturing systems and their processes. The approach guides the user from breaking down the system and the underlying building blocks of the processes into four groups. The development of a digital model includes predefined necessary parameters that allow a digital model connecting with a real manufacturing system. The connection enables the control of the real manufacturing system and allows the creation of the digital twin. Presentation and visualization of a system functioning based on the digital twin for different participants is presented in the last step. The suitability of the approach for the industrial environment is illustrated using the case study of planning the digital twin for material logistics of the manufacturing system.

scholarly journals Pipeline for ontology-based modeling and automated deployment of digital twins for planning and control of manufacturing systems

2021 ◽  
Author(s):  
Amon Göppert ◽  
Lea Grahn ◽  
Jonas Rachner ◽  
Dennis Grunert ◽  
Simon Hort ◽  
...  
Keyword(s):  
Data Exchange ◽  
Manufacturing Systems ◽  
Digital Model ◽  
Use Case ◽  
Digital Twin ◽  
Planning And Control ◽  
Digital Twins ◽  
Definition Of ◽  
Fully Connected ◽  
And Control

AbstractThe demand for individualized products drives modern manufacturing systems towards greater adaptability and flexibility. This increases the focus on data-driven digital twins enabling swift adaptations. Within the framework of cyber-physical systems, the digital twin is a digital model that is fully connected to the physical and digital assets. A digital model must follow a standardization for interoperable data exchange. Established ontologies and meta-models offer a basis in the definition of a schema, which is the first phase of creating a digital twin. The next phase is the standardized and structured modeling with static use-case specific data. The final phase is the deployment of digital twins into operation with a full connection of the digital model with the remaining cyber-physical system. In this deployment phase communication standards and protocols provide a standardized data exchange. A survey on the state-of-the-art of these three digital twin phases reveals the lack of a consistent workflow from ontology-driven definition to standardized modeling. Therefore, one goal of this paper is the design of an end-to-end digital twin pipeline to lower the threshold of creating and deploying digital twins. As the task of establishing a communication connection is highly repetitive, an automation concept by providing structured protocol data is the second goal. The planning and control of a line-less assembly system with manual stations and a mobile robot as resources and an industrial dog as the product serve as exemplary digital twin applications. Along this use-case the digital twin pipeline is transparently explained.

scholarly journals Sustainable, Smart, and Sensing Technologies for Cyber-Physical Manufacturing Systems: A Systematic Literature Review

2021 ◽  
Vol 13 (10) ◽  
pp. 5495
Author(s):  
Mihai Andronie ◽  
George Lăzăroiu ◽  
Roxana Ștefănescu ◽  
Cristian Uță ◽  
Irina Dijmărescu
Keyword(s):  
Decision Making ◽  
Internet Of Things ◽  
Literature Review ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Production Networks ◽  
Production Logistics ◽  
Time Production

With growing evidence of the operational performance of cyber-physical manufacturing systems, there is a pivotal need for comprehending sustainable, smart, and sensing technologies underpinning data-driven decision-making processes. In this research, previous findings were cumulated showing that cyber-physical production networks operate automatically and smoothly with artificial intelligence-based decision-making algorithms in a sustainable manner and contribute to the literature by indicating that sustainable Internet of Things-based manufacturing systems function in an automated, robust, and flexible manner. Throughout October 2020 and April 2021, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “Internet of Things-based real-time production logistics”, “sustainable smart manufacturing”, “cyber-physical production system”, “industrial big data”, “sustainable organizational performance”, “cyber-physical smart manufacturing system”, and “sustainable Internet of Things-based manufacturing system”. As research published between 2018 and 2021 was inspected, and only 426 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, we selected 174 mainly empirical sources. Further developments should entail how cyber-physical production networks and Internet of Things-based real-time production logistics, by use of cognitive decision-making algorithms, enable the advancement of data-driven sustainable smart manufacturing.

AI Assisted High Fidelity Multi-Physics Digital Twin of Industrial Gas Turbines

2021 ◽  
Author(s):  
Senthil Krishnababu ◽  
Omar Valero ◽  
Roger Wells
Keyword(s):  
Gas Turbine ◽  
Data Storage ◽  
Gas Turbines ◽  
Building Blocks ◽  
Data Driven ◽  
High Fidelity ◽  
Test Field ◽  
Operating Characteristics ◽  
Digital Twin ◽  
The Impact

Abstract Data driven technologies are revolutionising the engineering sector by providing new ways of performing day to day tasks through the life cycle of a product as it progresses through manufacture, to build, qualification test, field operation and maintenance. Significant increase in data transfer speeds combined with cost effective data storage, and ever-increasing computational power provide the building blocks that enable companies to adopt data driven technologies such as data analytics, IOT and machine learning. Improved business operational efficiency and more responsive customer support provide the incentives for business investment. Digital twins, that leverages these technologies in their various forms to converge physics and data driven models, are therefore being widely adopted. A high-fidelity multi-physics digital twin, HFDT, that digitally replicates a gas turbine as it is built based on part and build data using advanced component and assembly models is introduced. The HFDT, among other benefits enables data driven assessments to be carried out during manufacture and assembly for each turbine allowing these processes to be optimised and the impact of variability or process change to be readily evaluated. On delivery of the turbine and its associated HFDT to the service support team the HFDT supports the evaluation of in-service performance deteriorations, the impact of field interventions and repair and the changes in operating characteristics resulting from overhaul and turbine upgrade. Thus, creating a cradle to grave physics and data driven twin of the gas turbine asset. In this paper, one branch of HFDT using a power turbine module is firstly presented. This involves simultaneous modelling of gas path and solid using high fidelity CFD and FEA which converts the cold geometry to hot running conditions to assess the impact of various manufacturing and build variabilities. It is shown this process can be executed within reasonable time frames enabling creation of HFDT for each turbine during manufacture and assembly and for this to be transferred to the service team for deployment during field operations. Following this, it is shown how data driven technologies are used in conjunction with the HFDT to improve predictions of engine performance from early build information. The example shown, shows how a higher degree of confidence is achieved through the development of an artificial neural network of the compressor tip gap feature and its effect on overall compressor efficiency.

Collaborative Platform for Remote Manufacturing Systems Using Industrial Internet and Digital Twin in the COVID-19 Era

2021 ◽  
Author(s):  
Jay Lee ◽  
Xiaodong Jia ◽  
Qibo Yang ◽  
Keyi Sun ◽  
Xiang Li
Keyword(s):  
Manufacturing Systems ◽  
Manufacturing System ◽  
The Real ◽  
Digital Twin ◽  
Manufacturing Efficiency ◽  
Industrial Internet ◽  
Industrial Operations ◽  
Promising Solution ◽  
Technology Group ◽  
Collaborative Platform

Abstract In the wake of COVID-19, significant influence on the manufacturing industries has been observed in the past year due to the restrictions of in-person communications and interactions. As a consequence, manufacturing efficiency has reduced remarkably all over the world. Despite the great harm to the industrial operations under the pandemic, the opportunities for remote collaborative manufacturing system also arise. Effective and efficient remote manufacturing systems for the real industries have been highly demanded. Through the integration of industrial internet and digital twin systems, the remote manufacturing system can be largely facilitated. This paper proposes a general framework for the remote manufacturing system during the COVID-19 era. The concept of the intelligent collaborative remote manufacturing system is firstly reviewed, as well as discussions of the current pandemic situation and its influence on the industries. The current commercial platforms of the systems are also presented. A case study on the lighthouse factories at the Foxconn Technology Group is finally presented for understanding the implementation of the proposed strategy. The effectiveness of the framework has been validated in the real industrial scenarios, and great economic and operational benefits have been obtained. The proposed framework offers a promising solution for the remote manufacturing system under the current pandemic.

scholarly journals Investigating Data-Driven Systems as Digital Twins: Numerical Behavior of Ho–Kalman Method for Order Estimation

Processes ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 431
Author(s):  
Alexios Papacharalampopoulos
Keyword(s):  
System Identification ◽  
Time Domain ◽  
Data Driven ◽  
Driven Systems ◽  
Control Techniques ◽  
Digital Twin ◽  
Order Estimation ◽  
Digital Twins ◽  
Initial Stage ◽  
System Order

System identification has been a major advancement in the evolution of engineering. As it is by default the first step towards a significant set of adaptive control techniques, it is imperative for engineers to apply it in order to practice control. Given that system identification could be useful in creating a digital twin, this work focuses on the initial stage of the procedure by discussing simplistic system order identification. Through specific numerical examples, this study constitutes an investigation on the most “natural” method for estimating the order from responses in a convenient and seamless way in time-domain. The method itself, originally proposed by Ho and Kalman and utilizing linear algebra, is an intuitive tool retrieving information out of the data themselves. Finally, with the help of the limitations of the methods, the potential future outlook is discussed, under the prism of forming a digital twin.

A Digital Twin Concept for Manufacturing Systems

2018 ◽  
Author(s):  
Wesley Ellgass ◽  
Nathan Holt ◽  
Hector Saldana-Lemus ◽  
Julian Richmond ◽  
Ali Vatankhah Barenji ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Real Time ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Virtual Space ◽  
Smart Manufacturing ◽  
Shop Floor ◽  
Digital Twin ◽  
Physical Resources ◽  
Virtual Resources

With the developments and applications of the advanced information technologies such as cloud computing, internet of thing, artificial intelligence and virtual reality, industry 4.0 and smart manufacturing era are coming. In this respect, one of the specific challenges is to achieve a connection of physical resources on the shop floor with virtual resources, for real-time response, real time process optimization, and simulation, which is merged by big data problem. In this respect, Digital Twins (DT) concept is introduced as a key technology, which includes physical resources, virtual resources, service system, and digital twin data. DT considers current condition of physical resource and prediction of future events to make a responsive decision. However, due to the complexity of building a digital equivalent in virtual space to its physical counterpart, very little applications have been developed with this purpose, especially in the industrial manufacturing area. Therefore, the types of data and technology required to build the DT for a manufacturing system are presented in this work, trying to develop a framework of DT based manufacturing system, which is supported by the virtual reality for virtualization of physical resources.

scholarly journals Discovering multiscale and self-similar structure with data-driven wavelets

2020 ◽  
Vol 118 (1) ◽  
pp. e2021299118
Author(s):  
Daniel Floryan ◽  
Michael D. Graham
Keyword(s):  
Multiple Scales ◽  
Spatial Scales ◽  
Building Blocks ◽  
Biological Tissues ◽  
Data Driven ◽  
Model Free ◽  
Starting Point ◽  
Wide Range ◽  
Direct Model ◽  
Self Similar

Many materials, processes, and structures in science and engineering have important features at multiple scales of time and/or space; examples include biological tissues, active matter, oceans, networks, and images. Explicitly extracting, describing, and defining such features are difficult tasks, at least in part because each system has a unique set of features. Here, we introduce an analysis method that, given a set of observations, discovers an energetic hierarchy of structures localized in scale and space. We call the resulting basis vectors a “data-driven wavelet decomposition.” We show that this decomposition reflects the inherent structure of the dataset it acts on, whether it has no structure, structure dominated by a single scale, or structure on a hierarchy of scales. In particular, when applied to turbulence—a high-dimensional, nonlinear, multiscale process—the method reveals self-similar structure over a wide range of spatial scales, providing direct, model-free evidence for a century-old phenomenological picture of turbulence. This approach is a starting point for the characterization of localized hierarchical structures in multiscale systems, which we may think of as the building blocks of these systems.

Digital Factory: Simulation Enhancing Production and Engineering Process

2018 ◽  
Author(s):  
Angella Thomas ◽  
David A. Guerra-Zubiaga ◽  
John Cohran
Keyword(s):  
System Integration ◽  
Flexible Manufacturing ◽  
Manufacturing Systems ◽  
Manufacturing Industry ◽  
Manufacturing System ◽  
Flexible Manufacturing System ◽  
Engineering Process ◽  
Research Activity ◽  
Digital Twins ◽  
Enhancing Production

Manufacturing system integration is an important industrial and research activity to explore Next Generation Automated Systems (NGAS). Manufacturing systems has been incorporating flexible, reconfigurable, smart and intelligent features. Advances in technology and trends such Industry 4.0 will revolutionize the manufacturing industry tremendously. Important subjects in this direction are Digital Twins, Internet of Things, and Collaborative Robots among others, are integral to continue the progression to create smart and reliable manufacturing processes. This paper aims to implement a method applying these concepts in a Flexible Manufacturing System (FMS) by providing a broad view of NGAS.

scholarly journals Event-Driven Online Machine State Decision for Energy-Efficient Manufacturing System Based on Digital Twin Using Max-Plus Algebra

2019 ◽  
Vol 11 (18) ◽  
pp. 5036 ◽  
Author(s):  
Junfeng Wang ◽  
Yaqin Huang ◽  
Qing Chang ◽  
Shiqi Li
Keyword(s):  
Energy Saving ◽  
Energy Efficient ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Production Data ◽  
Physical Systems ◽  
Digital Twin ◽  
Saving Decisions ◽  
Efficient Manufacturing ◽  
Energy Efficient Manufacturing

Energy-efficient manufacturing is an important aspect of sustainable development in current society. The rapid development of sensing technologies can collect real-time production data from shop floors, which provides more opportunities for making energy saving decisions about manufacturing systems. In this paper, a digital twin-based bidirectional operation framework is proposed to realize energy-efficient manufacturing systems. The data view, model view, and service view of a digital twin manufacturing system are formulated to describe the physical systems in virtual space, to perform simulation analysis, to make decisions, and to control the physical systems for various energy-saving purposes. For online energy-saving decisions about machines in serial manufacturing systems, an event-driven estimation method of an energy-saving window based on Max-plus Algebra is presented to put the target machine to sleep, considering real-time production data of a system segment. A practical, simplified automotive production line is used to illustrate the effectiveness of the proposed method by simulation experiments. Our method has no restriction on machine failure mode and predefined parameters for energy-saving decision of machines. The proposed approach has potential use in synchronous and asynchronous manufacturing systems.

The DIM Approach for Digital Twin

2021 ◽  
pp. 153-171
Author(s):  
Matteo Del Giudice
Keyword(s):  
Information And Communication Technologies ◽  
Communication Technologies ◽  
Heterogeneous Data ◽  
Digital Twin ◽  
Innovative Methods ◽  
Digital Twins ◽  
Modelling Methodology ◽  
Information And Communication ◽  
Virtual City ◽  
Definition Of

In the era of connections and information and communication technologies, the building industry is facing the challenge of digitization at the building and urban scale. Several researches have been carried out to generate virtual city models to manage and represent a variety of data to reach the smart city concept. Therefore, the development of building/urban digital twins is directly linked to the definition of innovative methods and tools that are able to collect, organize, query heterogeneous data to make it available for the various involved actors. This chapter aims at presenting the district information modelling methodology that is strictly related to the digital twin concept, starting with data domains, arriving at the various tools developed to reach the users' needs.

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