SNIPE will create an AI enabled IoT system for machinery revamping in foundries to monitor performance and enable predictive maintenance with an effective control framework for cybersecurity compliance.
SNIPE is a 12-month use case demonstrator addressing the Foundry and Casting industry. Granted by the project “TRINITY”, which received funding by the European Commission’s programme “Robotics – Digital Innovation Hubs”, SNIPE aims to create an infrastructure for the monitoring, collection, and analysis of non-digitalised foundry processes, which, thanks to customised predictive algorithms, will be accordingly monitored and maintained. Over the past few years, in fact, the foundry and casting industry has progressively focused on digital transformations able to optimise safety and productivity. Being in line with this need, SNIPE tailor-made solutions deal with those foundry processes deemed most critical such as the casting, the belt transport and the production of green sand.
A smart monitoring infrastructure made by an industrial-grade wireless sensor network solution and an IoT Gateway with several connectivity options that are compliant with state-of-art cybersecurity requirements
AI-based decision support system which collects machine process data (temperature, engine vibration, etc.) from the sensor network and predicts maintenance on specific key processes
A wearable smart device to inform operators with a code colour or vibration on the status of the machine/process.
Duration
12 months
Kick off meeting
4/08/2020
Sensing network and wearable device development
01/2021
Monitoring and evaluation of the acquired data
05/2021
Final Event
21/07/2021
End of the Project
31/07/2021
Kilometro Rosso will be in charge of project communication and technology transfer of the SNIPE solution in the manufacturing sector.
This project has indirectly received funding from the European Union’s Horizon 2020 research and innovation programme, via an Open Call issued and executed under project TRINITY (grant agreement No 825196).
