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 Construction of Intelligent Integrated Model Framework for the Workshop Manufacturing System Via Digital Twin

2021 ◽  
Author(s):  
Zhongyu Zhang ◽  
Zhenjie Zhu ◽  
Jinsheng Zhang ◽  
Jingkun Wang
Keyword(s):  
Real Time ◽  
Production System ◽  
Manufacturing System ◽  
System Optimization ◽  
Production Cycle ◽  
Model Framework ◽  
The Real ◽  
Digital Twin ◽  
Twin Model ◽  
Information Layer

Abstract With the drastic development of the globally advanced manufacturing industry, transition of the original production pattern from traditional industries to advanced intelligence is completed with the least delay possible, which are still facing new challenges. Because the timeliness, stability and reliability of them is significantly restricted due to lack of the real-time communication. Therefore, an intelligent workshop manufacturing system model framework based on digital twin is proposed in this paper, driving the deep inform integration among the physical entity, data collection, and information decision-making. The conceptual and obscure of the traditional digital twin is refined, optimized, and upgraded on the basis of the four-dimension collaborative model thinking. A refined nine-layer intelligent digital twin model framework is established. Firstly, the physical evaluation is refined into entity layer, auxiliary layer and interface layer, scientifically managing the physical resources as well as the operation and maintenance of the instrument, and coordinating the overall system. Secondly, dividing the data evaluation into the data layer and the processing layer can greatly improve the flexible response-ability and ensure the synchronization of the real-time data. Finally, the system evaluation is subdivided into information layer, algorithm layer, scheduling layer, and functional layer, developing flexible manufacturing plan more reasonably, shortening production cycle, and reducing logistics cost. Simultaneously, combining SLP and artificial bee colony are applied to investigate the production system optimization of the textile workshop. The results indicate that the production efficiency of the optimized production system is increased by 34.46%.

scholarly journals A Referenced Cyber Physical System for Compressor Manufacturing

2020 ◽  
Vol 306 ◽  
pp. 02005
Author(s):  
Jin Cao ◽  
Junliang Wang ◽  
Junqing Lu
Keyword(s):  
System Architecture ◽  
Manufacturing System ◽  
Physical Space ◽  
Virtual Space ◽  
Multidimensional Model ◽  
Digital Twin ◽  
Twin Model ◽  
Industrial Internet ◽  
Internet Platform ◽  
Model Layer

Compressor is a typical high-end discrete product，with the shortening of product life cycle and the enhancement of the degree of product customization, the traditional compressor manufacturing system architecture cannot meet the requirements of comprehensive digital management of compressor from body scheme design to parts production line, logistics management, operation and maintenance monitoring and evaluation. This paper presents a compressor manufacturing system architecture based on digital twinning, and establishes an Internet platform for compressor industry oriented to remote coordination from three aspects of compressor design, production, operation and maintenance. The platform includes industrial Internet infrastructure layer, physical space entity model layer, virtual space multidimensional model layer, physical space and virtual space multidimensional model correlation and mapping layer, big data intelligent analysis decision-making layer, and digital twin application layer. Through the establishment of the compressor product design and simulation model of digital twin, compressor production process digital twin model, compressor fault diagnosis and remote operations digital twin model, implementation is based on the number of compressor collaboration in manufacturing industrial Internet platform twin system, leading the transformation and upgrading of intelligent manufacturing industry, compressor industry sustainable development ability and international competitiveness.

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 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.

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.

Digital Twin of manufacturing systems: a case study on increasing the efficiency of reconfiguration

2020 ◽  
Vol 68 (6) ◽  
pp. 435-444 ◽  
Author(s):  
Behrang Ashtari Talkhestani ◽  
Michael Weyrich
Keyword(s):  
Manufacturing Systems ◽  
Manufacturing System ◽  
Added Value ◽  
Future Research ◽  
System Availability ◽  
Qualitative And Quantitative ◽  
Digital Twin ◽  
System A ◽  
Set Up

AbstractThe added value of a Digital Twin for reconfiguring manufacturing systems promises an increase in system availability, a reduction in set-up and conversion times and enables the manufacturing of customer-specific products. To evaluate this claim, this paper selects an architecture of the Digital Twin and realizes it on the basis of an application scenario for a cyber-physical manufacturing system. A case study is used to test the reconfiguration of a manufacturing system by comparing two different methods, one without and one with use of the Digital Twin. In this paper, the process steps of both reconfigurations are described and discussed in detail and a qualitative and quantitative evaluation of the reconfiguration results is presented. Finally, this paper gives an outlook on future research on intelligent automation of manufacturing systems using the Digital Twin.

Effective Applications of Optimization Methods in the Manufacturing Environment in Turkey

2016 ◽  
pp. 319-335 ◽  
Author(s):  
Omer Faruk Yilmaz ◽  
Hikmet Erbiyik
Keyword(s):  
Manufacturing Systems ◽  
Manufacturing System ◽  
Optimization Methods ◽  
Hybrid Manufacturing ◽  
Manufacturing Environment ◽  
Literature Research ◽  
The Real ◽  
Extant Literature ◽  
Functional Layout ◽  
Manufacturing Environments

In today's manufacturing environment both used equipment and worker resources have become more crucial. Both resource must be used in an effective and appropriate way. Therefore, studies in conjuction with manufacturing environment are actualized under dual resource constrained (DRC). In the extant literature, DRC manufacturing environments place importance on certain dimensions which are surveyed in detail in this study. This literature research is conducted for manufacturing environments where worker planning and product scheduling topics are studied frequently. Our observations reveal that the systems of single conducted do not reflect the real manufacturing environment; hence, hybrid manufacturing systems which consist of functional layout and cells are investigated. The efficiency of hybrid manufacturing systems in the DRC environment are revealed by searching through literature. Therefore, the more effective way of usage of optimization methods are proposed by examining the studies regarding hybrid manufacturing system in terms of usage of optimization methods.

Virtual Fusion: Hybrid Process Simulation and Emulation-in-the-Loop

2008 ◽  
Author(s):  
William S. Harrison ◽  
Dawn Tilbury
Keyword(s):  
Simulation Model ◽  
Process Simulation ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Virtual Machines ◽  
Hybrid Process ◽  
University Of Michigan ◽  
The Real ◽  
Test And Evaluation ◽  
The University

When developing a new manufacturing system or reconfiguring an existing system, it is desirable to have a simulation model for test and evaluation. However, there is often a disconnect between the real system and the simulation model; it is difficult for them to have exactly equivalent behavior. The highest-fidelity “model” is always the system itself. In this paper we propose a framework in which modular models of the manufacturing system components (robots, machines, conveyors, controllers) can be interchanged with their real counterparts, forming a hybrid process. We focus on both the connections between components and the most pertinent aspects of the processed parts. The transfer of parts between the real and virtual domains is particularly challenging; we describe how parts can transition between real and virtual without making substantial changes to the system itself. We discuss how the proposed hybrid process simulation can be used for the design of new manufacturing systems. As the new machines and components are built and installed, they can be “swapped” in for the virtual machines, and testing can be done incrementally. We also discuss how the proposed hybrid process simulation can be used for the upgrade or reconfiguration of existing manufacturing systems. When a new machine or cell is added, or the part flow is reconfigured, the relevant new parts of the system can first be built in simulation and tested as part of the hybrid process, with the new machines. A case study describing the implementation of the hybrid process simulation on the Reconfigurable Factory Testbed at the University of Michigan is presented.

A digital-twin visualized architecture for Flexible Manufacturing System

2021 ◽  
Vol 60 ◽  
pp. 176-201
Author(s):  
Yepeng Fan ◽  
Jianzhong Yang ◽  
Jihong Chen ◽  
Pengcheng Hu ◽  
Xiaoyu Wang ◽  
...  
Keyword(s):  
Flexible Manufacturing ◽  
Manufacturing System ◽  
Flexible Manufacturing System ◽  
Digital Twin

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.

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