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.
• 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
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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