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Development of a supervisory internet of things (IoT) system for factories of the future

Authors :
Kara, Sami, School of Mechanical and Manufacturing Engineering, Engineering, UNSW
Chan, KC, School of Mechanical and Manufacturing Engineering, Engineering, UNSW
Cui, Yesheng, School of Mechanical and Manufacturing Engineering, Engineering, UNSW
Kara, Sami, School of Mechanical and Manufacturing Engineering, Engineering, UNSW
Chan, KC, School of Mechanical and Manufacturing Engineering, Engineering, UNSW
Cui, Yesheng, School of Mechanical and Manufacturing Engineering, Engineering, UNSW
Publication Year :
2021

Abstract

Big data is of great importance to stakeholders, including manufacturers, business partners, consumers, government. It leads to many benefits, including improving productivity and reducing the cost of products by using digitalised automation equipment and manufacturing information systems. Some other benefits include using social media to build the agile cooperation between suppliers and retailers, product designers and production engineers, timely tracking customers’ feedbacks, reducing environmental impacts by using Internet of Things (IoT) sensors to monitor energy consumption and noise level. However, manufacturing big data integration has been neglected. Many open-source big data software provides complicated capabilities to manage big data software for various data-driven applications for manufacturing.In this research, a manufacturing big data integration system, named as Data Control Module (DCM) has been designed and developed. The system can securely integrate data silos from various manufacturing systems and control the data for different manufacturing applications. Firstly, the architecture of manufacturing big data system has been proposed, including three parts: manufacturing data source, manufacturing big data ecosystem and manufacturing applications. Secondly, nine essential components have been identified in the big data ecosystem to build various manufacturing big data solutions. Thirdly, a conceptual framework is proposed based on the big data ecosystem for the aim of DCM. Moreover, the DCM has been designed and developed with the selected big data software to integrate all the three varieties of manufacturing data, including non-structured, semi-structured and structured. The DCM has been validated on three general manufacturing domains, including product design and development, production and business. The DCM cannot only be used for the legacy manufacturing software but may also be used in emerging areas such as digital twin and digital thread.

Details

Database :
OAIster
Notes :
English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1288201858
Document Type :
Electronic Resource