Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing

Zhou, Guanghui; Zhang, Chao; Zhi, Li; Ding, Kai; Wang, Chuang

doi:10.1080/00207543.2019.1607978

Cited by 269 publications

(83 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, big data solutions can be very diverse and they can affect value in different ways (Ardito et al, 2018). Production domain is exemplar in this diversity, as it is at the forefront to exploit big data (Tan et al, 2017;Zhou et al, 2019). Indeed, manufacturing is an intensive user of big data and stores more data than any other sector, such as: to discover new patterns, perform simulations, pilot industry 4.0 (Zhou et al, 2019), manage complex systems in real-time (van der Spoel et al, 2017), enhance production yields (Baily and Manyika, 2013;, and transform supply chains (Baryannis et al, 2019;Gunasekaran et al, 2017;Hofmann, 2017;Ivanov et al, 2019;Pan et al, 2017;G.…”

Section: Introductionmentioning

confidence: 99%

Big data analytics business value and firm performance: linking with environmental context

Vitari

Raguseo

2019

International Journal of Production Research

View full text Add to dashboard Cite

HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

show abstract

Section: Introductionmentioning

confidence: 99%

Big data analytics business value and firm performance: linking with environmental context

Vitari

Raguseo

2019

International Journal of Production Research

View full text Add to dashboard Cite

show abstract

“…In previous literature, humans were limited to being individual actors, such as an operator in a manufacturing cell [ 12 ]. The power of the crowd was emphasized by researchers, including such topics as crowdsourcing, collaboration and collective intelligence.…”

Section: Related Workmentioning

confidence: 99%

Understanding Data-Driven Cyber-Physical-Social System (D-CPSS) Using a 7C Framework in Social Manufacturing Context

Yin

Ming

Zhang

2020

Sensors

View full text Add to dashboard Cite

The trend towards socialization, personalization and servitization in smart manufacturing has attracted the attention of researchers, practitioners and governments. Social manufacturing is a novel manufacturing paradigm responding to this trend. However, the current cyber–physical system (CPS) merges only cyber and physical space; social space is missing. A cyber–physical–social system (CPSS)-based smart manufacturing is in demand, which incorporates cyber space, physical space and social space. With the development of the Internet of Things and social networks, a large volume of data is generated. A data-driven view is necessary to link tri-space. However, there is a lack of systematical investigation on the integration of CPSS and the data-driven view in the context of social manufacturing. This article proposes a seven-layered framework for a data-driven CPSS (D-CPSS) along the data–information–knowledge–wisdom (DIKW) pyramid under a social manufacturing environment. The evolution, components, general model and framework of D-CPSS are illustrated. An illustrative example is provided to explain the proposed framework. Detailed discussion and future perspectives on implementation are also presented.

show abstract

“…Ding et al [18] presented the framework reference model of a DT-based Cyber-Physical Production System (DT-CPPS) and discussed in detail its configuring mechanism, operating mechanism, and real-time data-driven operations management. Zhou et al [19] proposed a general framework for a knowledge-driven DT manufacturing cell towards intelligent manufacturing, which could support autonomous manufacturing by an intelligent perceiving, simulating, understanding, predicting, optimizing, and controlling strategy. Zhuang et al [20] presented a detailed implementation process of the proposed framework of the DT-based smart production management and control approach in a satellite assembly shop-floor scenario.…”

Section: Literature Reviewmentioning

confidence: 99%

Implementation of Digital Twin for Engine Block Manufacturing Processes

et al. 2020

View full text Add to dashboard Cite

The digital twin (DT) is undergoing an increase in interest from both an academic and industrial perspective. Although many authors proposed and described various frameworks for DT implementation in the manufacturing industry context, there is an absence of real-life implementation studies reported in the available literature. The main aim of this paper is to demonstrate feasibility of the DT implementation under real conditions of a production plant that is specializing in manufacturing of the aluminum components for the automotive industry. The implementation framework of the DT for engine block manufacturing processes consists of three layers: physical layer, virtual layer and information-processing layer. A simulation model was created using the Tecnomatix Plant Simulation (TPS) software. In order to obtain real-time status data of the production line, programmable logic control (PLC) sensors were used for raw data acquisition. To increase production line productivity, the algorithm for bottlenecks detection was developed and implemented into the DT. Despite the fact that the implementation process is still under development and only partial results are presented in this paper, the DT seems to be a prospective real-time optimization tool for the industrial partner.

show abstract

Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing

Cited by 269 publications

References 39 publications

Big data analytics business value and firm performance: linking with environmental context

Big data analytics business value and firm performance: linking with environmental context

Understanding Data-Driven Cyber-Physical-Social System (D-CPSS) Using a 7C Framework in Social Manufacturing Context

Implementation of Digital Twin for Engine Block Manufacturing Processes

Contact Info

Product

Resources

About