All the features and components of the Internet of Things and Industrial Internet of Things are driving the transition to the Industry 4.0. New Enabling Technologies, Big Data, Sensor Networks, Embedded Computing, Machine Learning are changing industries, company processes and people. For companies it is important to be flexible to perceive these changes and be ready to upgrade their structure and process. In this paper we propose a retrofitting methodology based on Design Thinking in a steel mill plant, and highlight how the retrofitting activity is important to allow even more companies to migrate to Industry 4.0, reducing the gap between SMEs and Large Industries for participation in the 4th Industrial Revolution. The solutions implemented will be described mostly in regard to machinery interfacing by OPC for data acquisition, image processing for anomaly detection, and information visualization for human-machine interaction. This paper stresses the need for cooperation between the operators in the design process and the synergy between machineries-things-people inside the production plant. The main contribution of this work makes evident how a XX century production machinery can be upgraded by solving specific problems, how this can be done with a viable financial investment and with an improvement in the experience and involvement of workers. The final upgrade will allow the plant to increase efficiency and decrease machinery fault, which was causing a loss in terms of human experience and financials costs.

Burresi, G., Ermini, S., Bernabini, D., Lorusso, M., Gelli, F., Frustace, D., et al. (2020). Smart Retrofitting by Design Thinking Applied to an Industry 4.0 Migration Process in a Steel Mill Plant. In 2020 9th Mediterranean Conference on Embedded Computing, MECO 2020 (pp.1-6). Institute of Electrical and Electronics Engineers Inc. [10.1109/MECO49872.2020.9134210].

Smart Retrofitting by Design Thinking Applied to an Industry 4.0 Migration Process in a Steel Mill Plant

Burresi Giovanni.
Membro del Collaboration Group
;
Ermini Sara.
Membro del Collaboration Group
;
Bernabini Dario.
Membro del Collaboration Group
;
Lorusso Martino.
Membro del Collaboration Group
;
Rizzo Antonio.
Membro del Collaboration Group
2020-01-01

Abstract

All the features and components of the Internet of Things and Industrial Internet of Things are driving the transition to the Industry 4.0. New Enabling Technologies, Big Data, Sensor Networks, Embedded Computing, Machine Learning are changing industries, company processes and people. For companies it is important to be flexible to perceive these changes and be ready to upgrade their structure and process. In this paper we propose a retrofitting methodology based on Design Thinking in a steel mill plant, and highlight how the retrofitting activity is important to allow even more companies to migrate to Industry 4.0, reducing the gap between SMEs and Large Industries for participation in the 4th Industrial Revolution. The solutions implemented will be described mostly in regard to machinery interfacing by OPC for data acquisition, image processing for anomaly detection, and information visualization for human-machine interaction. This paper stresses the need for cooperation between the operators in the design process and the synergy between machineries-things-people inside the production plant. The main contribution of this work makes evident how a XX century production machinery can be upgraded by solving specific problems, how this can be done with a viable financial investment and with an improvement in the experience and involvement of workers. The final upgrade will allow the plant to increase efficiency and decrease machinery fault, which was causing a loss in terms of human experience and financials costs.
2020
978-1-7281-6949-1
Burresi, G., Ermini, S., Bernabini, D., Lorusso, M., Gelli, F., Frustace, D., et al. (2020). Smart Retrofitting by Design Thinking Applied to an Industry 4.0 Migration Process in a Steel Mill Plant. In 2020 9th Mediterranean Conference on Embedded Computing, MECO 2020 (pp.1-6). Institute of Electrical and Electronics Engineers Inc. [10.1109/MECO49872.2020.9134210].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11365/1121602