The project has advanced its SME-driven experiments, showcasing AI's practical impact on optimizing manufacturing processes in real industrial settings. Significant progress has also been made with Didactic Factory experiments, focusing on rigorous testing of AI technologies within TERESA environments for broader manufacturing application. A key achievement is the first open call, which aimed to add new use cases for AI at the Edge, strengthen the pan-European AI-on-Demand platform, enhance European Digital Innovation Hubs, and conduct test-before-invest experiments in AI Didactic Factories.
In details, each Work Package reach specific goals in the first part of the project.
- The major achievements of WP1 include:
Successful Project Setup: Establishment of the project's framework, ensuring all consortium members are informed about their rights and duties, as well as reporting, financial, and contractual modalities.
Consortium Management Bodies: Election and launch of consortium management bodies.
Collaborative Infrastructure: Implementation of a collaborative infrastructure with templates, instructions, and regulations to ensure project quality and proper handling of project results and findings.
- Major achievements in WP2 include:
Development of Requirements Engineering Methodology: Creation of a harmonized approach for collecting information related to technical work packages, resulting in D2.1 AI REDGIO 5.0 Requirements Engineering Methodology.
Analysis of Scenarios and Requirements: Detailed analysis of the AI REDGIO 5.0 experiments' scenarios and requirements, as well as the applicable European and national legislation and ethical sources. This work is documented in D2.2 Platforms and Experiments AI Scenarios and D2.3 User Requirements Specification for Edge-AI Industry 5.0.
- WP3 key achievements include:
Creation of an EDIH Ecosystem: Establishment of an ecosystem of EDIHs active in AI-at-the-edge for Manufacturing Industry 5.0.
Development of METHODIH: Refinement of the methodology for DIHs, addressing service portfolio analysis, customer journeys, and collaboration corridors among DIHs.
AI-at-the-Edge Experimental Facilities: Materialization of a network of facilities for performing experiments.
Extension of DIHIWARE Platform: Integration of the DIHIWARE platform for Industry 5.0 EDIH networks.
- Achievements in WP4 include:
Distributed Data Processing: Establishment of a distributed architecture facilitating data processing across cloud and edge layers, ensuring real-time data understanding and isolated operation capability.
Database Solutions: Implementation of a database capable of distributing storage and workload across edge architecture tiers, with real-time replication and synchronization across instances.
- Key WP5 achievements include:
Collaborative Intelligence Platform: Definition, specifications, and initial designs of the platform.
AI Pipeline Lifecycle Solution: Development and interoperability with the AIoD platform.
Open Hardware: Initial designs and specifications for open hardware solutions.
- WP6 is the core of experimentation and validation. Achievements include:
Monitoring Methodology: Development of the methodology for monitoring experiments.
Trial Handbook: Completion of Chapter 1 and Chapter 2 by most experiments.
Experiment Deployment: All experiments started on time and are running, with As-IS and To-Be scenarios depicted.
- WP7 focuses on assessing the socio-economic impact and legal/ethical considerations. Achievements include:
Regulatory and Ethical Analysis: Analysis of relevant frameworks and identification of applicable standards and certifications.
Surveys: Design and deployment of surveys to collect AS-IS situation information from project partners.
Exploitation Plan Development: Initial activities for market and competition analysis, SWOT assessment, and strategic positioning.
- WP8 is dedicated to communication, dissemination, and stakeholder engagement; a dedicated section provides all the improvements about the WP.
