Towards Industrial Internet of Things in Steel Manufacturing: A Multiple-Factor-based Detection System of Longitudinal Surface Cracks
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.
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IIoT implementation in I4.0 manufacturing plants provides numerous benefits in process optimization, cost reduction, enhancing product and control quality, and overall functionality.
“There are numerous benefits in process optimization, cost reduction, enhancing product and control quality [19], [20], and overall functionality from IIoT implementation in the I4.0 manufacturing plants leveraging multiple information sources of interconnected devices and their computational capabilities.”
From SCADA Systems With Focus on Continuous Manufacturing and Steel Industry: A Survey on Architectures, Standards, Challenges and Industry 5.0 (2022) · cites this paper as [19]
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“There are numerous benefits in process optimization, cost reduction, enhancing product and control quality [18], [19], and overall functionality from IIoT implementation in the I4.”
Background
- Authors
- Li, F., Yang, A., Chen, H., Sun, G., Wang, F., Xie, Y., Li, J., & Shen, J.
- Affiliations
- University of Wollongong (AU)
- Chongqing College of Humanities, Science and Technology (CN)
- Nanjing University of Posts and Telecommunications (CN)
- Venue
- 2020 IEEE International Conference on Big Data (Big Data)
- Publisher
- IEEE
- Published
- 2020-12-10
- Pages
- 4627-4635
- OpenAlex
- W3139000340
- Cited by
- 4
- References
- 32
- Citations by year
- 2025: 1 · 2024: 1 · 2022: 1 · 2021: 1
- FWCI
- 0.71
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE International Conference on Big Data (BigData) (publishedVersion)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Engineering › Mechanical Engineering › Non-Destructive Testing Techniques
- Keywords
- Industrial Internet of Things, steel industry, longitudinal surface cracks, continuous casting, Industry 4.0, IoT sensor data, smart manufacturing, quality prediction
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