Deep Reinforcement Learning-Based Smart Manufacturing Plants with a Novel Digital Twin Training Model

Author(s):  
Minghong She
Author(s):  
Fei Tao ◽  
Yongping Zhang ◽  
Ying Cheng ◽  
Jiawei Ren ◽  
Dongxu Wang ◽  
...  

Author(s):  
Maja Bärring ◽  
Björn Johansson ◽  
Goudong Shao

Abstract The manufacturing sector is experiencing a technological paradigm shift, where new information technology (IT) concepts can help digitize product design, production systems, and manufacturing processes. One of such concepts is Digital Twin and researchers have made some advancement on both its conceptual development and technological implementations. However, in practice, there are many different definitions of the digital-twin concept. These different definitions have created a lot of confusion for practitioners, especially small- and medium-sized enterprises (SMEs). Therefore, the adoption and implementation of the digital-twin concept in manufacturing have been difficult and slow. In this paper, we report our findings from a survey of companies (both large and small) regarding their understanding and acceptance of the digital-twin concept. Five supply-chain companies from discrete manufacturing and one trade organization representing suppliers in the automotive business were interviewed. Their operations have been studied to understand their current digital maturity levels and articulate their needs for digital solutions to stay competitive. This paper presents the results of the research including the viewpoints of these companies in terms of opportunities and challenges for implementing digital twins.


2018 ◽  
Vol 57 (12) ◽  
pp. 3920-3934 ◽  
Author(s):  
Jinjiang Wang ◽  
Lunkuan Ye ◽  
Robert X. Gao ◽  
Chen Li ◽  
Laibin Zhang

Industry 4.0 ◽  
2020 ◽  
pp. 77-122 ◽  
Author(s):  
Shohin Aheleroff ◽  
Jan Polzer ◽  
Huiyue Huang ◽  
Zexuan Zhu ◽  
David Tomzik ◽  
...  

2020 ◽  
Vol 7 (3) ◽  
pp. 323-336 ◽  
Author(s):  
Soonhung Han

Abstract Standards will allow interoperability among stakeholders in the upcoming super-connected world. A smart manufacturing reference model (SMRM) is under development inside JWG21 between ISO and IEC. Based on a dimensionality analysis and the skeleton meta-model, the eight proposed SMRMs are reviewed and compared. The SMRMs are classified according to the number of lifecycle axes and the number of dimensional axes. Also, how the concept of a digital twin can be accommodated in an SMRM is investigated.


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