Data-driven smart manufacturing

被引：888
|
作者：
Tao, Fei [1 ]
Qi, Qinglin [1 ]
Liu, Ang [2 ]
Kusiak, Andrew [3 ]
机构：
[1] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China
[2] Univ New South Wales, Sch Mech & Mfg Engn, Sydney, NSW 2053, Australia
[3] Univ Iowa, Dept Mech & Ind Engn, Iowa City, IA USA
来源：
JOURNAL OF MANUFACTURING SYSTEMS | 2018年 / 48卷
基金：
中国国家自然科学基金;
关键词：
Big data; Smart manufacturing; Manufacturing data; Data lifecycle; BIG DATA; DATA-MANAGEMENT; ONLINE REVIEWS; CHALLENGES; ANALYTICS; DESIGN; IMPROVEMENT; GENERATION; FRAMEWORK; SELECTION;
D O I：
10.1016/j.jmsy.2018.01.006
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
The advances in the internet technology, internet of things, cloud computing, big data, and artificial intelligence have profoundly impacted manufacturing. The volume of data collected in manufacturing is growing. Big data offers a tremendous opportunity in the transformation of today's manufacturing paradigm to smart manufacturing. Big data empowers companies to adopt data-driven strategies to become more competitive. In this paper, the role of big data in supporting smart manufacturing is discussed. A historical perspective to data lifecycle in manufacturing is overviewed. The big data perspective is supported by a conceptual framework proposed in the paper. Typical application scenarios of the proposed framework are outlined. (C) 2018 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码：157 / 169
页数：13
相关论文
共 50 条
  • [31] Data-driven Sustainability in Manufacturing: Selected Examples
    Linke, Barbara S.
    Garcia, Destiny R.
    Kamath, Akshay
    Garretson, Ian C.
    SUSTAINABLE MANUFACTURING FOR GLOBAL CIRCULAR ECONOMY, 2019, 33 : 602 - 609
  • [32] Data-Driven Continuous Evolution of Smart Systems
    Bosch, Jan
    Olsson, Helena Holmstrom
    PROCEEDINGS OF 2016 IEEE/ACM 11TH INTERNATIONAL SYMPOSIUM ON SOFTWARE ENGINEERING FOR ADAPTIVE AND SELF-MANAGING SYSTEMS (SEAMS), 2016, : 28 - 34
  • [33] Data-driven energy prediction modeling for both energy efficiency and maintenance in smart manufacturing systems
    Bermeo-Ayerbe, Miguel Angel
    Ocampo-Martinez, Carlos
    Diaz-Rozo, Javier
    ENERGY, 2022, 238
  • [34] Smart systems and data-driven services in healthcare
    Izonin, Ivan
    Kutucu, Hakan
    Singh, Krishna Kant
    COMPUTERS IN BIOLOGY AND MEDICINE, 2023, 158
  • [35] Editorial: Data-Driven Solutions for Smart Grids
    Milano, F.
    Vaccaro, A.
    Manana, M.
    FRONTIERS IN BIG DATA, 2021, 4
  • [36] Data-driven Predictive Maintenance for Green Manufacturing
    Rodseth, Harald
    Schjolberg, Per
    PROCEEDINGS OF THE 6TH INTERNATIONAL WORKSHOP OF ADVANCED MANUFACTURING AND AUTOMATION, 2016, 24 : 36 - 41
  • [37] Data-Driven Intelligent 3D Surface Measurement in Smart Manufacturing: Review and Outlook
    Yang, Yuhang
    Dong, Zhiqiao
    Meng, Yuquan
    Shao, Chenhui
    MACHINES, 2021, 9 (01) : 1 - 33
  • [38] Hybrid physics-based and data-driven models for smart manufacturing: Modelling, simulation, and explainability
    Wang, Jinjiang
    Li, Yilin
    Gao, Robert X.
    Zhang, Fengli
    JOURNAL OF MANUFACTURING SYSTEMS, 2022, 63 : 381 - 391
  • [39] Design and Implementation of Smart Manufacturing Systems Through AR for Data-Driven Digital Twin System
    Ashok J.
    Kumar N.A.
    Raj D.W.P.
    Ashok J.
    Bhushan A.V.
    Edem S.
    SN Computer Science, 4 (5)
  • [40] Advanced Data Collection and Analysis in Data-Driven Manufacturing Process
    Ke Xu
    Yingguang Li
    Changqing Liu
    Xu Liu
    Xiaozhong Hao
    James Gao
    Paul GMaropoulos
    Chinese Journal of Mechanical Engineering, 2020, 33 (03) : 40 - 60
12345