scispace - formally typeset
Open AccessJournal ArticleDOI

Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison

Qinglin Qi, +1 more
- 15 Jan 2018 - 
- Vol. 6, pp 3585-3593
Reads0
Chats0
TLDR
The similarities and differences between big data and digital twin are compared from the general and data perspectives and how they can be integrated to promote smart manufacturing are discussed.
Abstract
With the advances in new-generation information technologies, especially big data and digital twin, smart manufacturing is becoming the focus of global manufacturing transformation and upgrading. Intelligence comes from data. Integrated analysis for the manufacturing big data is beneficial to all aspects of manufacturing. Besides, the digital twin paves a way for the cyber-physical integration of manufacturing, which is an important bottleneck to achieve smart manufacturing. In this paper, the big data and digital twin in manufacturing are reviewed, including their concept as well as their applications in product design, production planning, manufacturing, and predictive maintenance. On this basis, the similarities and differences between big data and digital twin are compared from the general and data perspectives. Since the big data and digital twin can be complementary, how they can be integrated to promote smart manufacturing are discussed.

read more

Citations
More filters
Journal ArticleDOI

Digital Twin in Industry: State-of-the-Art

TL;DR: This paper thoroughly reviews the state-of-the-art of the DT research concerning the key components of DTs, the current development ofDTs, and the major DT applications in industry and outlines the current challenges and some possible directions for future work.
Journal ArticleDOI

Characterising the Digital Twin: A systematic literature review

TL;DR: A characterisation of the Digital Twin is provided, identification of gaps in knowledge, and required areas of future research are identified: Perceived Benefits; Digital Twin across the Product Life-Cycle; Use-Cases; Technical Implementations; Levels of Fidelity; Data Ownership; and Integration between Virtual Entities; each of which are required to realise the Digital twin.
Journal ArticleDOI

The future of manufacturing industry: a strategic roadmap toward Industry 4.0

TL;DR: In this paper, the authors conduct a systematic and content-centric review of literature based on a six-stage approach to identify key design principles and technology trends of Industry 4.0.
Journal ArticleDOI

Digital Twin: Enabling Technologies, Challenges and Open Research

TL;DR: Digital twins as discussed by the authors is an emerging concept that has become the centre of attention for industry and, in recent years, academia and a review of publications relating to Digital Twins is performed, producing a categorical review of recent papers.
Journal ArticleDOI

Industry 4.0, digitization, and opportunities for sustainability

TL;DR: In this paper, the authors present a systematic analysis of the sustainability functions of Industry 4.0, including energy sustainability, harmful emission reduction, and social welfare improvement, and show that sophisticated precedence relationships exist among various sustainability functions.
References
More filters
Journal ArticleDOI

Beyond the hype

TL;DR: The need to develop appropriate and efficient analytical methods to leverage massive volumes of heterogeneous data in unstructured text, audio, and video formats is highlighted and the need to devise new tools for predictive analytics for structured big data is reinforced.
Journal ArticleDOI

Big Data: A Survey

TL;DR: The background and state-of-the-art of big data are reviewed, including enterprise management, Internet of Things, online social networks, medial applications, collective intelligence, and smart grid, as well as related technologies.
Journal ArticleDOI

The rise of big data on cloud computing

TL;DR: The definition, characteristics, and classification of big data along with some discussions on cloud computing are introduced, and research challenges are investigated, with focus on scalability, availability, data integrity, data transformation, data quality, data heterogeneity, privacy, legal and regulatory issues, and governance.
Journal ArticleDOI

Digital twin-driven product design, manufacturing and service with big data

TL;DR: In this paper, a new method for product design, manufacturing, and service driven by digital twin is proposed, and three cases are given to illustrate the future applications of digital twin in three phases of a product respectively.
Related Papers (5)