National Start date: 01/03/2022
International Start date: 01/03/2022

National End date: 30/06/2023
International End date: 28/02/2025

Main Objective – F4iTECH aims to build a federated learning platform for industrial automation that offers solutions leveraging systems engineering for artificial intelligence (AI). This will be achieved by building AI models on decentralized data using a balanced approach to the learning process between centralized and distributed processing and using a blockchain approach to disseminate data. This strategy will empower data accuracy and privacy, as well as potentially create new business opportunities related to data sales. Therefore this project will provide great benefit to the manufacturing and transportation industries, which will benefit from the efficient incorporation of AI into the production or operation line to resolve and eliminate some invisible and internalized problems that are highly costly. Also, a smart contract system will be implemented on the blockchain, storing all the relevant events and transactions of stakeholders in the Industry 4.0 ecosystem, which will allow industries stakeholders and end consumers to verify the data in an intuitive way. 

As a way of validating and demonstrate the efficiency of the developed federated learning platform, 3 distinct pilots will be conducted:

#1 (INOSENS & TORUN METAL)

CNC machines are one of the most commonly used machines in the metal component manufacturing industry’s production line. These machines include numerical control units and mechanical components and during production processes, fluid is used to form the produce components with relatively low power consumption and produce components with dimensional accuracy. Since these machines produce very precious components with tight tolerances for various industries including heating, cooling, automotive, molding and die industries, the repeatability and sensitivity of such machines is extremely important. FL based AI models fed by distributed sensing system data will detect anomalies such as Remaining Useful Lifetime (RUL) of CNC machines and excessive machine heating of the related machine and will ensure uninterrupted 24/7 production. In this application, TORUN and INO will be main contributors for this pilot application.

#5 (SIDONIOS MALHAS S.A &. ISEP& SISTRADE)

Sidónios Malhas, SA is a textile company with near 40 years of history. It is specialized in the production of textile meshes in circular looms. At the F4itech, it will be lending a subset of their equipments and provide production and equipment condition data so that federated learning platform can be developed and prototyped. This federated learning platform should help the company in improving energy efficiency of its operations, reduce maintenance costs through predictive maintenance and improve trust downstream the value chain by using blockchain technology. Sidónios will be closely assisted by Sistrade and ISEP, national partners in the project, and contribute to the international project results exploitation.

#6 (INOSENS & SAMM TECHNOLOGY)

SAMM Teknoloji is a Turkish company that was founded in 2003. The federated learning platform, which will be developed within the scope of the project, is planned to be used in the distributed fiber optic acoustic sensors (DAS) developed by our company. In this context, a pilot application will be made on distributed fiber optic acoustic sensors installed in Gebze Organized Industrial Zone, Informatics Valley Technology Development Zone and Camlica TV Tower regions in Turkey. Different application areas cause different threat factors. In addition, acoustic data with different characteristics are obtained from each system. With the F4iTECH federated learning system, it will be possible to improve system modeling with data collected from sensors used for different security purposes. SAMM and INO will be main contributors and work together for this application. With the federated learning platform, the distributed fiber optic acoustic sensor system will be improved and solutions that can make artificial intelligence-based alarm classification for critical areas such as border security, military base security, infrastructure security, airport security will be developed.

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Total eligible cost: 748 456,74 €

National granted funding: 485 883,60 €