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Introduction
Data-Centric Engineering - an open-access journal published by Cambridge University Press at the interface of data science and all areas of engineering - is pleased to partner with the Industrial Big Data & Intelligent Systems Branch of the Chinese Mechanical Engineering Society to publish a special collection (issue) of papers on High-performance Intelligent Manufacturing Driven by Industrial Big Data, arising in part from its conference in Dalian, China, which took place on July 5 to 7 2024. Authors who did not participate in the Conference are also welcome to submit.
Scope
Big data-driven intelligent manufacturing makes factory operation transparent, workshop management accurate, product quality consistent, production line efficiency optimized, and equipment operation smooth, and this promotes the collaborative optimization of the whole production life
Big data in the manufacturing process merged with the characteristics of "3V-3M", 3V (scale, diversity, high speed) 3M (multi-dimensional, multi-scale, multi-noise), and the big data-driven manufacturing mode is a current hot topic research of intelligent manufacturing systems. However, the theoretical system of "big data-driven" scientific research paradigm is not yet complete, and the enabling technology of industrial big data is not yet mature, and it still needs to be continuously enriched and improved in industrial application scenarios. The new generation of artificial intelligence technology plays an important role in promoting the application of industrial big data in intelligent manufacturing systems, helping to create a new generation of intelligent manufacturing models driven by big data, and promoting the transformation, upgrading and high-quality development of China's manufacturing industry.2018.
Since 2018, jointly sponsored by Huazhong University of Science and Technology, Shanghai Jiao Tong University, Donghua University and Guizhou University, six academic conferences on big data-driven intelligent manufacturing have been successfully held in Shanghai, Wuhan, Hangzhou, Guiyang and Zhengzhou. In order to bring together scholars in the field of industrial big data and intelligent systems and promote academic progress and engineering practice, in 2020, under the guidance of the Chinese Mechanical Engineering Society, scholars in this field jointly established the Industrial Big Data and Intelligent Systems Branch of the Chinese Mechanical Engineering Society.
This special collection aims to further exchange and discussion on the latest research and application progress industrial big data related technologies. Experts and scholars engaged in industrial big data, industrial Internet, intelligent systems and other related fields are welcome to submit.
Key Dates
- July 5-7 2024, Conference takes place in Dalian, China
- January 31 2025, final submission deadline of papers to DCE
Authors not participating in the conference are also welcome to submit to DCE. Authors are encouraged to submit when ready, even if this ahead of the deadline. Articles sent to review typically take 90 days from submission to decision. Articles will be published as soon as possible after acceptance. At a later date a special collection page for the articles will constructed and introduced with an editorial reflecting upon all the contributions.
Why Submit to DCE?
✔ Publish in a dedicated venue for the transformative impact of data science in all areas of engineering;
✔ Gain peer review feedback from experts with data science and application area expertise;
✔ Welcomes research and translational articles from authors, whether they are based in academia or industry;
✔ Well-cited with with a 2023 Impact Factor of 2.4 (Journal Citation Reports) and a 2023 CiteScore of 4.0 (Scopus)
✔ Open Access with support for authors who do not have funding or Transformative Agreements (i.e. no need for authors to pay an article processing charge).
How to Submit to DCE
Authors should choose one of the following categories for their work:
- Research articles using data science methods and models for improving the reliability, resilience, safety, efficiency and usability of engineered systems.
- Translational papers demonstrating the downstream benefits of data-intensive engineering - and the underlying data science principles, techniques and technologies - to wider society, economy, environment, health and way of life. For some more detailed instructions, see this guide to translational papers. (Typically 6,000 words or less).
- Data papers that describe in a structured way, with a narrative and accompanying metadata, important and re-usable data sets in open repositories with potential for re-use in engineering research and practice. These papers promote data transparency and data re-use.
- Survey papers providing a detailed, balanced and authoritative current account of the existing literature concerning data-intensive methods in a particular facet of engineering sciences.
- Tutorial reviews providing an introduction and overview of an important topic of relevance to the journal readership. The topic should be of relevance to both students and researchers who are new to the field as well as experts and provide a good introduction to the development of a subject, its current state and indications of future directions the field is expected to take
- Position papers providing an overview of an important issue for this emerging field. (Typically 6,000 words or less).
Templates
Authors have the option of using the following templates:
- DCE LaTeX template files
- Overleaf (a LaTeX-based collaborative authoring tool; read about benefits of this tool)
- DCE Word template
Note that authors should provide both an abstract that summarizes the paper (250 words or less) and beneath it an impact statement (120 words describing the significance of the findings in language that can be understood by a wide audience).
Competing interest, funding and data availability statements should be provided at the end of the main text above the references (see disclosure statements).
Submission system
Articles should be submitted through the DCE ScholarOne system, but if you use the Overleaf tool you can submit directly into the system without having to re-upload your files. In response to the question, ‘Are you submitting to a Special Collection’, authors should select ‘High-performance Intelligent Manufacturing Driven by Industrial Big Data’.
Open Access
DCE is supported by a grant from the Lloyd’s Register Foundation; all authors accepted for publication can publish on an open access basis without a requirement to pay a publishing charge.
Guest Editors
- Xinyu Li (School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China). E-Mail: [email protected]. Research Interests: Intelligent Manufacturing Systems.
- Yiping Gao (School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China). E-Mail: [email protected]. Research Interests: Deep Learning and Vision-based Defect Recognition.
- Junliang Wang (Institute of Artificial Intelligence, Donghua University, Shanghai, China). E-Mail: [email protected]. Research Interests: Big Data Analytics.
- Pai Zheng (Department of Industrial and Systems Engineering at The Hong Kong Polytechnic University). E-Mail: [email protected]. Research Interests: Human-Robot Collaboration, Smart Product-Service Systems, Industrial AI.