scholarly journals The Application of Cognitive IoT for Smart Manufacturing

2020 ◽  
Vol 9 (3) ◽  
pp. 788-792
Keyword(s):  
Paradigm Shift ◽  
Manufacturing Systems ◽  
Smart Manufacturing ◽  
Major Goal ◽  
Knowledge Accumulation ◽  
Learning Techniques ◽  
Manufacturing Sectors ◽  
Smart Manufacturing Systems ◽  
Cognitive Internet Of Things ◽  
Open Issues

In recent times, novel paradigms based on cognitive manufacturing services are evolving. This paradigm shift is conveyed by integrating manufacturing assets with the latest and enhanced methods and technologies. However, today’s manufacturing systems are facing various challenges, that is the researcher believed that existing technologies and tools specifically, IoT, existing learning techniques and learning systems, lack enough cognitive based intelligence and cannot achieve the expected enhancements and smart manufacturing developments. In Light of this assessing the advances in manufacturing sectors was the major goal of this work and to identify the cuuent issues and challenches in manufacturing systems being Cognitive and or smart Afterward, we assessed the research challenges and open issues and facilitate knowledge accumulation in efficiently in the applications of Cognitive Internet of Things (CIoT) for smart manufacturing systems.

scholarly journals Integration of Blockchain, IoT and Machine Learning for Multistage Quality Control and Enhancing Security in Smart Manufacturing

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1467 ◽  
Author(s):  
Zeinab Shahbazi ◽  
Yung-Cheol Byun
Keyword(s):  
Machine Learning ◽  
Quality Control ◽  
Manufacturing Systems ◽  
True Positive Rate ◽  
Machine Learning Techniques ◽  
Smart Manufacturing ◽  
Control Approach ◽  
Learning Techniques ◽  
Positive Rate ◽  
Smart Manufacturing Systems

Smart manufacturing systems are growing based on the various requests for predicting the reliability and quality of equipment. Many machine learning techniques are being examined to that end. Another issue which considers an important part of industry is data security and management. To overcome the problems mentioned above, we applied the integrated methods of blockchain and machine learning to secure system transactions and handle a dataset to overcome the fake dataset. To manage and analyze the collected dataset, big data techniques were used. The blockchain system was implemented in the private Hyperledger Fabric platform. Similarly, the fault diagnosis prediction aspect was evaluated based on the hybrid prediction technique. The system’s quality control was evaluated based on non-linear machine learning techniques, which modeled that complex environment and found the true positive rate of the system’s quality control approach.

scholarly journals Smart Systems and Collaborative Innovation Networks for Productivity Improvement in SMEs

2020 ◽  
Vol 7 (1) ◽  
pp. 3
Author(s):  
Andrew Thomas ◽  
Wyn Morris ◽  
Claire Haven-Tang ◽  
Mark Francis ◽  
Paul Byard
Keyword(s):  
Manufacturing Systems ◽  
Contextual Information ◽  
Detailed Examination ◽  
Collaborative Networks ◽  
Smart Manufacturing ◽  
Research Approach ◽  
Manufacturing Companies ◽  
Structured Interviews ◽  
Manufacturing Sectors ◽  
Smart Manufacturing Systems

The adoption of Smart Manufacturing Systems in manufacturing companies is often seen as a strategy towards achieving improvements in productivity. However, there is little evidence to indicate that UK manufacturing SMEs are prepared for the implementation of such systems. Through the employment of a triangulation research approach involving the detailed examination of 36 UK manufacturing SMEs from three manufacturing sectors, this study investigates the level of awareness and understanding within SMEs of Smart Manufacturing Systems. The development of a profiling tool is shown and is subsequently used to audit company awareness and understanding of the key technologies, collaborative networks and systems of SMS. Further information obtained from semi-structured interviews and observations of manufacturing operations provide further contextual information. The findings indicate that whilst the priority technologies and systems differ between manufacturing sectors, the key issues around the need for developing appropriate collaborative networks and knowledge management systems are common to all sectors.

scholarly journals A Model for Working Environment Monitoring in Smart Manufacturing

2021 ◽  
Vol 11 (6) ◽  
pp. 2850
Author(s):  
Dalibor Dobrilovic ◽  
Vladimir Brtka ◽  
Zeljko Stojanov ◽  
Gordana Jotanovic ◽  
Dragan Perakovic ◽  
...  
Keyword(s):  
Open Source ◽  
Student Teaching ◽  
Manufacturing Systems ◽  
Working Environment ◽  
Smart Manufacturing ◽  
Environment Monitoring ◽  
Application Development ◽  
Testing Platform ◽  
Proposed Model ◽  
Smart Manufacturing Systems

The growing application of smart manufacturing systems and the expansion of the Industry 4.0 model have created a need for new teaching platforms for education, rapid application development, and testing. This research addresses this need with a proposal for a model of working environment monitoring in smart manufacturing, based on emerging wireless sensor technologies and the message queuing telemetry transport (MQTT) protocol. In accordance with the proposed model, a testing platform was developed. The testing platform was built on open-source hardware and software components. The testing platform was used for the validation of the model within the presented experimental environment. The results showed that the proposed model could be developed by mainly using open-source components, which can then be used to simulate different scenarios, applications, and target systems. Furthermore, the presented stable and functional platform proved to be applicable in the process of rapid prototyping, and software development for the targeted systems, as well as for student teaching as part of the engineering education process.

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