VISION: By 2030, Europe will lead the worldwide competition for an ethical and sustainable adoption of AI in Manufacturing, by integrating regional Digital Innovation Hubs and pan-EU open Digital Manufacturing Platforms via a cross-border network of SME-driven Industrial Experiments and Didactic Experimental Facilities. General Data Protection Regulations (GDPR) and Data Sovereignty will drive the European AI strategy for personal and non-personal Data Sharing Spaces.
The AI REGIO project aims at filling 3 major gaps currently preventing AI-driven DIHs from implementing fully effective digital transformation pathways for their Manufacturing SMEs: at policy level the Regional vs. EU gap; at technological level the Digital Manufacturing vs. Innovation Collaboration Platform gap; at business level the Innovative AI (Industry 5.0) vs Industry 4.0 gap.
POLICY. Regional smart specialization strategies for Efficient Sustainable Manufacturing and Digital Transformation (VANGUARD initiative for Industrial Modernisation) are so far insufficiently coordinated and integrated at crossregional and pan-EU level. SME-driven >AI innovations cannot scale up to become pan-EU accessible in global marketplaces as well as SME-driven experiments remain trapped into a too local dimension without achieving a large scale dimension. (Regional vs. EU Gap)
TECHNOLOGY. Digital Manufacturing Platforms DMP and Digital Innovation Hubs DIH play a fundamental role in the implementation of the Digital Single Market and Digitsing European Industry directives to SMEs, but so far such initiatives, communities, innovation actions are running in a quite independent if not siloed way, where very often Platform-related challenges are not of interest for DIHs and Socio-Business impact not of interest for DMP. (DMP vs. DIH Gap)
BUSINESS. Many Industrial Data Platforms based on IOT Data in Motion and Analytics Data at Rest have been recently developed to implement effective Industry 4.0 pilots (I4MS Phase III platforms). The AI revolution and the new relationship between autonomous systems and humans (Industry 5.0) has not been properly addressed in I4MS so far. (AI I5.0 vs. I4.0 Gap)
AI REGIO is following the 4 steps for VANGUARD innovation strategy (learn-connect-demonstrate-commercialize) by constantly aligning its methods with the AI DIH Network initiative and its assets with I4MS/DIH BEinCPPS Phase II and MIDIH / L4MS Phase III projects.
AI REGIO: Industry 5.0 for SMEs
Descriptive and predictive analytics for manufacturing legacy and operational data related to maintenance, delivering insights on critical manufacturing operations.
Image recognition and localization of objects for the update of a manufacturing digital twin using RGB-D information processed in distinct computation layers (Cloud, Edge, Local).
A Jupyter Notebook demo to demonstrate the partial content of FZI asset "Process integrated feedback management" which uses the historical multivariant time series data from a simulative robot manipulator to predict data in the future time steps based on ...
Application of a Neural Network trained with Reinforcement Learning method aimed to assign production resources to tasks in a production schedule during its execution.
A flexible tool able to identify and localize in Real-time the best object to pick in scene with a multitude of overlapped identical objects.
CUSUM RLS filter contains a change detection algorithm for multiple sensors, using the Recursive Least Squares (RLS) and Cumulative Sum (CUSUM) methods [F. Gustafsson. Adaptive Filtering and Change Detection. John Willey & Sons, LTD 2000].
Data preprocessing, Feature Engineering, Model Development, Evaluation & Selection for Predictive Maintenance in Manufacturing. Part of the development of this asset was supported by the AI REGIO project, which has received funding from the European U...
This repository includes a jupyter notebook that presents a complete pipeline for: EDA on image data Data preparation and augmentation Deep learning (CNN) models development for image classification with TensorFlow Models evaluation Model interpret...
A Digital Twin solution that, leveraging a middleware and data analytics module, processes all the data received from shopfloor to identify potential faults.
A flexible and extensible synthetic data generation engine based on mainstream statistics distributions and on timeseries generative AI techniques.
The AI4CNC experiment is designed to develop a Federated Learning System Platform for CNC machines to estimate tool wear through AI models and secure data sharing. The main objective is to use data from TEKNOPAR's CNC machines and sensors to estimate tool...
The DIDA (Digital Industry Data Analytics) Platform is an OSS Digital Manufacturing Platform, aiming to become a reference implementation for any Industry 4.0 Data Analytics need, enabling the development of applications in several industrial domains.
