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Towards Industrial Internet of Things in Steel Manufacturing: A Multiple-Factor-based Detection System of Longitudinal Surface Cracks

Conference Paper


Abstract


  • An intelligent industrial system is demanded for the development of Industry 4.0, which aims at providing efficient and intelligent computing service to increase the productivity. In-ternet of things become critical to achieve this goal by employing the sensors and connecting the objects over internet. In this study, we firstly investigate how the intelligent industrial service will be realised by constructing a five-layer framework based on our comprehensive field experiences. In detail, how the IoT sensor data are connected with the system and how the computational model is designed to improve the efficiency of the manufacturing system are discussed. Particularly, in this paper, the task of the defect identification of the steel is selected as our application on field. Since the longitudinal surface crack on the steel slab is a crucial indication suggesting the quality of continuous casting slab, how to discover the longitudinal surface crack on the slab in an early stage is of great significance. Traditional methods to detect the longitudinal surface crack have different drawbacks. Given the benefit of numerous IoT sensor data, we have proposed a novel computational model to incorporate the multiple factors of steel manufacturing system to improve the detection. Experiment evaluation has shown the efficiency and effectiveness of the model. In summary, we anticipate this work will contribute to an intelligent steel manufacturing system based on industrial IoT in building viable solutions, which benefit from the early stage identification and prediction of poor quality productions.

Publication Date


  • 2020

Citation


  • Li, F., Yang, A., Chen, H., Sun, G., Wang, F., Xie, Y., . . . Shen, J. (2020). Towards Industrial Internet of Things in Steel Manufacturing: A Multiple-Factor-based Detection System of Longitudinal Surface Cracks. In Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020 (pp. 4627-4635). doi:10.1109/BigData50022.2020.9378300

Scopus Eid


  • 2-s2.0-85103839127

Start Page


  • 4627

End Page


  • 4635

Abstract


  • An intelligent industrial system is demanded for the development of Industry 4.0, which aims at providing efficient and intelligent computing service to increase the productivity. In-ternet of things become critical to achieve this goal by employing the sensors and connecting the objects over internet. In this study, we firstly investigate how the intelligent industrial service will be realised by constructing a five-layer framework based on our comprehensive field experiences. In detail, how the IoT sensor data are connected with the system and how the computational model is designed to improve the efficiency of the manufacturing system are discussed. Particularly, in this paper, the task of the defect identification of the steel is selected as our application on field. Since the longitudinal surface crack on the steel slab is a crucial indication suggesting the quality of continuous casting slab, how to discover the longitudinal surface crack on the slab in an early stage is of great significance. Traditional methods to detect the longitudinal surface crack have different drawbacks. Given the benefit of numerous IoT sensor data, we have proposed a novel computational model to incorporate the multiple factors of steel manufacturing system to improve the detection. Experiment evaluation has shown the efficiency and effectiveness of the model. In summary, we anticipate this work will contribute to an intelligent steel manufacturing system based on industrial IoT in building viable solutions, which benefit from the early stage identification and prediction of poor quality productions.

Publication Date


  • 2020

Citation


  • Li, F., Yang, A., Chen, H., Sun, G., Wang, F., Xie, Y., . . . Shen, J. (2020). Towards Industrial Internet of Things in Steel Manufacturing: A Multiple-Factor-based Detection System of Longitudinal Surface Cracks. In Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020 (pp. 4627-4635). doi:10.1109/BigData50022.2020.9378300

Scopus Eid


  • 2-s2.0-85103839127

Start Page


  • 4627

End Page


  • 4635