scholarly journals Design of a Smart Manufacturing System With the Application of Multi-Access Edge Computing and Blockchain Technology

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 28659-28667 ◽  
Author(s):  
C. K. M. Lee ◽  
Y. Z. Huo ◽  
S. Z. Zhang ◽  
K. K. H. Ng
Keyword(s):  
Manufacturing System ◽  
Edge Computing ◽  
Smart Manufacturing ◽  
Blockchain Technology ◽  
Multi Access

scholarly journals Improving Transactional Data System Based on an Edge Computing–Blockchain–Machine Learning Integrated Framework

Processes ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 92
Author(s):  
Zeinab Shahbazi ◽  
Yung-Cheol Byun
Keyword(s):  
Machine Learning ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Edge Computing ◽  
Smart Manufacturing ◽  
Learning Approaches ◽  
Blockchain Technology ◽  
Timely Response ◽  
Machine Learning Approach ◽  
Smart Manufacturing Systems

The modern industry, production, and manufacturing core is developing based on smart manufacturing (SM) systems and digitalization. Smart manufacturing’s practical and meaningful design follows data, information, and operational technology through the blockchain, edge computing, and machine learning to develop and facilitate the smart manufacturing system. This process’s proposed smart manufacturing system considers the integration of blockchain, edge computing, and machine learning approaches. Edge computing makes the computational workload balanced and similarly provides a timely response for the devices. Blockchain technology utilizes the data transmission and the manufacturing system’s transactions, and the machine learning approach provides advanced data analysis for a huge manufacturing dataset. Regarding smart manufacturing systems’ computational environments, the model solves the problems using a swarm intelligence-based approach. The experimental results present the edge computing mechanism and similarly improve the processing time of a large number of tasks in the manufacturing system.

Evolving Multi-Access Edge Computing to Support Enhanced IoT Deployments

2019 ◽  
Vol 3 (2) ◽  
pp. 26-34 ◽  
Author(s):  
Lanfranco Zanzi ◽  
Flavio Cirillo ◽  
Vincenzo Sciancalepore ◽  
Fabio Giust ◽  
Xavier Costa-Perez ◽  
...  
Keyword(s):  
Edge Computing ◽  
Multi Access

scholarly journals Driving forces for Multi-Access Edge Computing (MEC) IoT integration in 5G

ICT Express ◽  
2021 ◽  
Author(s):  
Madhusanka Liyanage ◽  
Pawani Porambage ◽  
Aaron Yi Ding ◽  
Anshuman Kalla
Keyword(s):  
Driving Forces ◽  
Edge Computing ◽  
Multi Access

Implementation of MQTT protocol based network architecture for smart factory

2021 ◽  
pp. 095440542110144
Author(s):  
Chia-Shin Yeh ◽  
Shang-Liang Chen ◽  
I-Ching Li
Keyword(s):  
Machine Tool ◽  
Gesture Recognition ◽  
Manufacturing Process ◽  
Network Architecture ◽  
Information Management System ◽  
Edge Computing ◽  
Smart Factory ◽  
Smart Manufacturing ◽  
Industrial Iot ◽  
Computing Module

The core concept of smart manufacturing is based on digitization to construct intelligent production and management in the manufacturing process. By digitizing the production process and connecting all levels from product design to service, the purpose of improving manufacturing efficiency, reducing production cost, enhancing product quality, and optimizing user experience can be achieved. To digitize the manufacturing process, IoT technology will have to be introduced into the manufacturing process to collect and analyze process information. However, one of the most important problems in building the industrial IoT (IIoT) environment is that different industrial network protocols are used for different equipment in factories. Therefore, the information in the manufacturing process may not be easily exchanged and obtained. To solve the above problem, a smart factory network architecture based on MQTT (MQ Telemetry Transport), IoT communication protocol, is proposed in this study, to construct a heterogeneous interface communication bridge between the machine tool, embedded device Raspberry Pi, and website. Finally, the system architecture is implemented and imported into the factory, and a smart manufacturing information management system is developed. The edge computing module is set up beside a three-axis machine tool, and a human-machine interface is built for the user controlling and monitoring. Users can also monitor the system through the dynamically updating website at any time and any place. The function of real-time gesture recognition based on image technology is developed and built on the edge computing module. The gesture recognition results can be transmitted to the machine controller through MQTT, and the machine will execute the corresponding action according to different gestures to achieve human-robot collaboration. The MQTT transmission architecture developed here is validated by the given edge computing application. It can serve as the basis for the construction of the IIoT environment, assist the traditional manufacturing industry to prepare for digitization, and accelerate the practice of smart manufacturing.

scholarly journals Sustainable, Smart, and Sensing Technologies for Cyber-Physical Manufacturing Systems: A Systematic Literature Review

2021 ◽  
Vol 13 (10) ◽  
pp. 5495
Author(s):  
Mihai Andronie ◽  
George Lăzăroiu ◽  
Roxana Ștefănescu ◽  
Cristian Uță ◽  
Irina Dijmărescu
Keyword(s):  
Decision Making ◽  
Internet Of Things ◽  
Literature Review ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Production Networks ◽  
Production Logistics ◽  
Time Production

With growing evidence of the operational performance of cyber-physical manufacturing systems, there is a pivotal need for comprehending sustainable, smart, and sensing technologies underpinning data-driven decision-making processes. In this research, previous findings were cumulated showing that cyber-physical production networks operate automatically and smoothly with artificial intelligence-based decision-making algorithms in a sustainable manner and contribute to the literature by indicating that sustainable Internet of Things-based manufacturing systems function in an automated, robust, and flexible manner. Throughout October 2020 and April 2021, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “Internet of Things-based real-time production logistics”, “sustainable smart manufacturing”, “cyber-physical production system”, “industrial big data”, “sustainable organizational performance”, “cyber-physical smart manufacturing system”, and “sustainable Internet of Things-based manufacturing system”. As research published between 2018 and 2021 was inspected, and only 426 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, we selected 174 mainly empirical sources. Further developments should entail how cyber-physical production networks and Internet of Things-based real-time production logistics, by use of cognitive decision-making algorithms, enable the advancement of data-driven sustainable smart manufacturing.

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