Digital twin-based designing of the configuration, motion, control, and optimization model of a flow-type smart manufacturing system

2020 ◽  
Author(s):  
Qiang Liu ◽  
Jiewu Leng ◽  
Douxi Yan ◽  
Ding Zhang ◽  
Lijun Wei ◽  
...  
Keyword(s):  
Motion Control ◽  
Optimization Model ◽  
Manufacturing System ◽  
Smart Manufacturing ◽  
Digital Twin ◽  
Flow Type

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 Toward digital validation for rapid product development based on digital twin: a framework

2022 ◽  
Author(s):  
Sihan Huang ◽  
Guoxin Wang ◽  
Dong Lei ◽  
Yan Yan
Keyword(s):  
Product Development ◽  
Manufacturing System ◽  
New Product ◽  
Virtual Manufacturing ◽  
Virtual Space ◽  
Smart Manufacturing ◽  
Digital Twin ◽  
Design Scheme ◽  
Rapid Product Development ◽  
Digital Prototype

AbstractProduct development should cover product design, validation, and manufacturing. In traditional product development, physical validation based on physical trial manufacturing is the key step to confirm the design scheme before physical manufacturing. However, physical validation is costly and inefficient, which could be the main obstacle to achieving rapid product development. The emergence of digital twin provides an opportunity to accelerate product development by eliminating physical validation toward digital validation in the smart manufacturing era. Therefore, a framework of rapid product development based on digital twin is proposed in this paper. During product development, the new product is designed according to the new requirements in the virtual space, in which the existing digital twins of products can be referenced. Then, an ultrahigh-fidelity virtual manufacturing system is constructed for digital trial manufacturing based on the digital twin of the manufacturing system and the design scheme of the new product. An ultrahigh-fidelity digital prototype can be obtained from digital trial manufacturing for digital validation. The new product validation is executed on the digital prototype to test its performance. The digital validation results can be used to improve the design scheme of the new product and boost the corresponding manufacturing processes. In addition, the core characteristics and key technologies of rapid product development based on digital twin are discussed. Finally, a case study is presented to implement the proposed framework and to show the effectiveness of accelerating product development.

scholarly journals Application framework of digital twin-driven product smart manufacturing system: A case study of aeroengine blade manufacturing

2019 ◽  
Vol 16 (5) ◽  
pp. 172988141988066
Author(s):  
Xuqian Zhang ◽  
Wenhua Zhu
Keyword(s):  
Manufacturing System ◽  
Hot Spot ◽  
Industrial Applications ◽  
Smart Manufacturing ◽  
Application Framework ◽  
Digital Twin ◽  
Mapping Technology ◽  
Key Enabling Technologies ◽  
New Research ◽  
Machining Parameter

In the wake of the continuous deepening of the application of new generation information technology in the manufacturing field, digital twin, as a most new active factors for smart manufacturing, has become a new research hot spot. Based on such a background, the article proposes a novel application framework of digital twin-driven product smart manufacturing system and analyzes its operation mechanism. Key enabling technologies such as digital twin mapping technology with manufacturing entity, twinning of cyber and physical manufacturing system, as well as twining data-driven machining parameter optimization are also illustrated in detail. Finally, a case of the aeroengine fan blade manufacturing is given to demonstrate the feasibility and effectiveness of the implementation method mentioned above. Meanwhile, potential industrial applications and limitations are discussed as well to provide valuable insights to aeroengine blade manufacturers.

Digital twin and blockchain enhanced smart manufacturing service collaboration and management

2020 ◽  
Author(s):  
Fei Tao ◽  
Yongping Zhang ◽  
Ying Cheng ◽  
Jiawei Ren ◽  
Dongxu Wang ◽  
...  
Keyword(s):  
Smart Manufacturing ◽  
Digital Twin ◽  
Service Collaboration

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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