Cloud-based manufacturing process monitoring for smart diagnosis services

ABSTRACT

A cloud-based manufacturing process monitoring framework for online smart diagnosis services has been developed with the aim of performing tool condition monitoring during machining of difficult-to-machine materials. The proposed architecture allows to share process monitoring tasks between different resources, which can be geographically dislocated and managed by actors with different competences and functions. Distributed resources with enhanced computation and data storage capability allow to improve the efficiency of tool condition diagnosis and enable more robust decision-making, exploiting large information and knowledge sharing. Diagnosis on tool conditions is offered as a cloud service, using an architecture where the computing resources in the cloud are connected to the physical manufacturing system realising a complex cyber-physical system using sensor and network communication. Based on sensorial data acquired at the factory level, smart online diagnosis on consumed tool life and tool breakage occurrence is carried out through knowledge-based algorithms and cognitive pattern recognition paradigms. On the basis of the cloud diagnosis, the local server activates the proper corrective action to be taken, such as tool replacement, process halting or parameters change, sending the right command to the machine tool control.

KEYWORDS:

• Cloud manufacturing
• machining
• tool condition monitoring
• sensors
• cyber-physical system
• Industry 4.0

Acknowledgements

The research results presented in this paper are based on the activities carried out in the framework of the project CLOUD MODE ‘CLOUD Manufacturing for On-Demand manufacturing sErvices’ (000011–ALTRI_DR_3450_2016_RICERCA_ ATENEO-CAGGIANO) funded by the University of Naples Federico II within the ‘Programma per il finanziamento della ricerca di Ateneo’ (2016–2018).

The Fraunhofer Joint Laboratory of Excellence on Advanced Production Technology (Fh-J_LEAPT Naples) at the Department of Chemical, Materials and Industrial Production Engineering, University of Naples Federico II, is gratefully acknowledged for its contribution and support to this research activity.

Disclosure statement

No potential conflict of interest was reported by the author.

Additional information

Funding

This work was supported by the Programma per il finanziamento della ricerca di Ateneo, University of Naples Federico II [000011--ALTRI_DR_3450_2016_RICERCA_ATENEO-CAGGIANO].