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CORDIS

Data and decentralized Artificial intelligence for a competitive and green European metallurgy industry

CORDIS provides links to public deliverables and publications of HORIZON projects.

Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .

Deliverables

Project Handbook (opens in new window)

T1.1-T1.3. It shall describe all operational aspects of the project, project execution and management in order to provide the consortium partners with background information about specific procedures and norms to be followed during the project lifetime. Quality procedures, risk identification, management and mitigation procedures, as well as guidelines on knowledge management for information sharing, IPR protection and innovation will be here described.

Plan for impact creation, standardisation and exploitation (opens in new window)

T6.1-T6.3 Report on the communication, dissemination, and standardisation. This report will include detailed plans for dissemination, communication, standardization and exploitation activities and IPR to be followed by all partners to publicise the ALCHIMA results. This deliverable will also include a preliminary Business Plan for the sustainability of the project

Use-cases preparation (opens in new window)

T5.1. Report that describes the readiness of each pilot for the development and deployment activities based on T5.1.

Requirements and human-centric recommendations (opens in new window)

T2.1, T2.3 This deliverable will include the stakeholders’ expectations. Also, the findings of the research with survey results and interviews provide insights and foresight recommendations for human-centric technology development and insertion.

Data Management Plan (opens in new window)

T1.5. It defines the guidelines for data management in order to ensure a high level of data quality and accessibility for final users and stakeholders and to allow the application of data analytics techniques. Note: Financial and progress reporting will be provided at the end of each reporting period through the EC tool.

Federated Learning framework_version 1 (opens in new window)

T3.1-T3.3 Report and prototype implementation of ALCHIMIA Federated Learning framework, Transfer Learning and domain adaptation techniques.

Publications

Machine Learning models to forecast defects occurrence on foundry products (opens in new window)

Author(s): S. Dettori, A. Zaccara, L. Laid, I. Matino, M. Vannucci, V. Colla, G. Bontempi, L. Forlani
Published in: IFAC-PapersOnLine, Issue 58, 2024, ISSN 2405-8963
Publisher: Elsevier BV
DOI: 10.1016/J.IFACOL.2024.09.300

Optimizing Steelmaking with Models, AI, and Federated and Continual Learning (opens in new window)

Published in: Automaatioväylä, Issue 41(1), 2025, ISSN 0784-6428
Publisher: Finnish Society of Automation
DOI: 10.5281/ZENODO.14741719

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