Smart Manufacturing and Intelligent Manufacturing:A Comparative Review

Abstract

The application of intelligence to manufacturing has emerged as a compelling topic for researchers and industries around the world. However, different terminologies, namely smart manufacturing (SM) and intelligent manufacturing (IM), have been applied to what may be broadly characterized as a similar paradigm by some researchers and practitioners. While SM and IM are similar, they are not identical. From an evolutionary perspective, there has been little consideration on whether the definition, thought, connotation, and technical development of the concepts of SM or IM are consistent in the literature. To address this gap, the work performs a qualitative and quantitative investigation of research literature to systematically compare inherent differences of SM and IM and clarify the relationship between SM and IM. A bibliometric analysis of publication sources, annual publication numbers, keyword frequency, and top regions of research and development establishes the scope and trends of the currently presented research. Critical topics discussed include origin, definitions, evolutionary path, and key technologies of SM and IM. The implementation architecture, standards, and national focus are also discussed. In this work, a basis to understand SM and IM is provided, which is increasingly important because the trend to merge both terminologies rises in Industry 4.0 as intelligence is being rapidly applied to modern manufacturing and human–cyber–physical systems.

Publication DOI: https://doi.org/10.1016/j.eng.2020.07.017
Divisions: College of Engineering & Physical Sciences > School of Engineering and Technology > Mechanical, Biomedical & Design
Funding Information: This work was supported by the International Postdoctoral Exchange Fellowship Program ( 20180025 ), National Natural Science Foundation of China ( 51703180 ), China Postdoctoral Science Foundation ( 2018M630191 and 2017M610634 ), Shaanxi Postdoctoral Scie
Additional Information: © 2021 THE AUTHORS. Published by Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company. This is an open access article under the CC BY-NC-ND license Funding Information: This work was supported by the International Postdoctoral Exchange Fellowship Program ( 20180025 ), National Natural Science Foundation of China ( 51703180 ), China Postdoctoral Science Foundation ( 2018M630191 and 2017M610634 ), Shaanxi Postdoctoral Science Foundation ( 2017BSHEDZZ73 ), and Fundamental Research Funds for the Central Universities ( xpt012020006 and xjj2017024 ).
Uncontrolled Keywords: Human–cyber–physical system (HCPS),Industry 4.0,Intelligent manufacturing,Smart manufacturing,General Computer Science,Environmental Engineering,General Chemical Engineering,Materials Science (miscellaneous),Energy Engineering and Power Technology,General Engineering
Publication ISSN: 2095-8099
Last Modified: 11 Jul 2024 07:08
Date Deposited: 08 Mar 2022 12:32
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://www.sci ... 2502?via%3Dihub (Publisher URL)
PURE Output Type: Review article
Published Date: 2021-06
Published Online Date: 2020-09-20
Accepted Date: 2020-07-20
Authors: Wang, Baicun
Tao, Fei
Fang, Xudong
Liu, Chao (ORCID Profile 0000-0001-7261-3832)
Liu, Yufei
Freiheit, Theodor

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