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Frontiers of Mechanical Engineering

Front. Mech. Eng.    2018, Vol. 13 Issue (2) : 137-150
Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives
Pai ZHENG, Honghui WANG, Zhiqian SANG, Ray Y. ZHONG(), Yongkui LIU, Chao LIU, Khamdi MUBAROK, Shiqiang YU, Xun XU
Department of Mechanical Engineering, University of Auckland, Auckland, New Zealand
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Information and communication technology is undergoing rapid development, and many disruptive technologies, such as cloud computing, Internet of Things, big data, and artificial intelligence, have emerged. These technologies are permeating the manufacturing industry and enable the fusion of physical and virtual worlds through cyber-physical systems (CPS), which mark the advent of the fourth stage of industrial production (i.e., Industry 4.0). The widespread application of CPS in manufacturing environments renders manufacturing systems increasingly smart. To advance research on the implementation of Industry 4.0, this study examines smart manufacturing systems for Industry 4.0. First, a conceptual framework of smart manufacturing systems for Industry 4.0 is presented. Second, demonstrative scenarios that pertain to smart design, smart machining, smart control, smart monitoring, and smart scheduling, are presented. Key technologies and their possible applications to Industry 4.0 smart manufacturing systems are reviewed based on these demonstrative scenarios. Finally, challenges and future perspectives are identified and discussed.

Keywords Industry 4.0      smart manufacturing systems      Internet of Things      cyber-physical systems      big data analytics      framework     
Corresponding Authors: Ray Y. ZHONG   
Just Accepted Date: 27 December 2017   Online First Date: 24 January 2018    Issue Date: 16 March 2018
 Cite this article:   
Pai ZHENG,Honghui WANG,Zhiqian SANG, et al. Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives[J]. Front. Mech. Eng., 2018, 13(2): 137-150.
Fig.1  Conceptual framework of Industry 4.0 smart manufacturing systems
Fig.2  Conceptual framework of the proposed product development process
Fig.3  CPS-enabled smart machine tools
Fig.4  Energy-efficient manufacturing
Fig.5  Cloud-based smart control system
Fig.6  Machine scheduling in Industry 4.0
Fig.7  Smart 3D scanning for automated quality inspection
Scenarios Advantages Disadvantages
UX-based personalized smart wearable device • Users are actively involved in the co-creation process for personalization.
• User experience can be readily obtained/analyzed in a real-time design context.
• Product change can be rapidly prototyped for design innovation in a cyber-physical manner.
• The application scope of the model is limited to highly modularized or discreet manufacturing systems (e.g., automobile and bicycles), rather than integral or continuous processes (e.g., chemical process and natural gas).
CPS-based smart machine tools • Users can control the machine tool in real time by using cloud-based services.
• Real-time status can be reflected in the user interface.
• System reliability is based on the stability of communication networks.
• Information confidentiality is an issue on the part of end users.
Energy consumption monitoring • Energy consumption can be tracked and visualized in real time.
• Decision making/optimization can be based on energy consumption.
• Smart sensors should be equipped to machines.
• Data transmission relies on multiple channels.
Cloud-based numerical control • Control of the machine is servicelized.
• Highly sophisticated algorisms can be applied.
• Service is flexible and can be updated and upgraded easily.
• The process know-hows can be well protected.
• Concerns on cyber security and service availability may exist.
Machine scheduling in smart factories • Machines are optimally scheduled based on real-time information.
• Any disturbances can be tracked and traced in real time.
• Advanced decision-making models are required.
• Real-time data processing models are necessary.
Smart 3D scanning for automated quality inspection • Quality inspection can be automatically executed.
• Quality data can be visualized in real time for decision making.
• Data storage and processing may be an issue if the volume of real-time information is large.
Tab.1  Summary of demonstrative scenarios
AR Augmented reality
CAD Computer-aided design
CAM Computer-aided manufacturing
CCTV Closed-circuit television
CMM Coordinate measuring machine
CNC Computer numerical control
CPPS Cyber-physical production systems
CPS Cyber-physical systems
CSaaS Control system as a service
DML Deep machine learning
DNN Deep neural network
ICT Information and communication technology
IoT Internet of Things
ISO International Organization for Standardization
PHM Prognostics and health management
RFID Radio frequency identification
SMOs Smart manufacturing objects
UX User experience
VR Virtual reality
XML Extensible markup language
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