Sensor Network for Intelligent Predictive Enterprise

Sensor Network for Intelligent Predictive Enterprise

The project's goal is the development of a Smart Monitoring infrastructure which will improve the 4.0 industry, using the high-predictability mechanism to collect and analyse data.

The solution

SNIPE proposes an AI-based decision support system for predictive maintenance and monitoring of operation performance for better product quality and production capacity. The project has created a smart monitoring infrastructure made by an industrial-grade wireless sensor network solution plus an IoT Gateway with several connectivity options, which complies with state-of -art cybersecurity requirements; on top of this architecture an AI-based decision support system collects machine process data (temperature, engine vibration, etc.) from a IoT sensor network and then predicts maintenance on specific key processes.


Due to the very long service life of the machines, the foundry and casting industry with its predominantly medium-sized structure is characterised by a low degree of automation. Maintenance is a key area that can drive major cost savings and production value around the world. Most foundries still use the traditional time-based maintenance, which is why failures keep occurring. Machine down-time and process interruption have two consequences within the typical Foundry enterprise which are the energy waste and the decrease of productivity level (OEE reduction) and increase of process instability, in addition to complex procedures to restore correct working conditions.


The market segment of metal casting and Foundry consists of approximately 4.700 companies in Europe, for a global value of casting products of 43 billion euros within this market, 70% are small enterprises (with fewer than 50 employees) therefore with a limited possibility with access to benefits of Industry 4.0 digital revolution.
The SNIPE project has been focused in technological solutions for the reduction of down-time on the
1) melting process,
2) the conveyor and belt transfer, and
3) the green sand production.
These are recognised as the most critical scenarios in terms of energy and economic impact for the foundry industry. In the case of down-time, they have direct implications on other production process.

Facts and figures

  • Technology Area:


  • End User:

    Foundry and casting industry, manufacturing companies with big production plants and machinery

  • Start Date - End Date:

    01/07/2020 - 30/06/2021

  • Duration:

    12 months

  • FSTP Funding:

    296 625,00 €

  • TRL Level at Start:


  • TRL Level at End:


  • Number of early adopters raised:


  • Project results
    "The introduction of Artificial Intelligence and IoT in the foundry market can be a key factor for the future of the sector, enabling huge financial savings thanks to the implementation of a predictive maintenance approach." 

    Attilio Scandella, Plant Manager

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