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CORDIS - Resultados de investigaciones de la UE
CORDIS

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

CORDIS proporciona enlaces a los documentos públicos y las publicaciones de los proyectos de los programas marco HORIZONTE.

Los enlaces a los documentos y las publicaciones de los proyectos del Séptimo Programa Marco, así como los enlaces a algunos tipos de resultados específicos, como conjuntos de datos y «software», se obtienen dinámicamente de OpenAIRE .

Resultado final

Project Handbook (se abrirá en una nueva ventana)

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 (se abrirá en una nueva ventana)

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 (se abrirá en una nueva ventana)

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 (se abrirá en una nueva ventana)

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 (se abrirá en una nueva ventana)

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 (se abrirá en una nueva ventana)

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

Publicaciones

Artificial Intelligence in Steel Production: Questions of Augmentation, Optimisation and Accountability

Autores: Dean Stroud, Rachel Hale; Martin Weinel; Luca Antonazzo; Vinicio Di Iorio
Publicado en: 2026
Editor: ELGAR - Handbook of Disruptive Digital Technology

Machine Learning models to forecast defects occurrence on foundry products (se abrirá en una nueva ventana)

Autores: S. Dettori, A. Zaccara, L. Laid, I. Matino, M. Vannucci, V. Colla, G. Bontempi, L. Forlani
Publicado en: IFAC-PapersOnLine, Edición 58, 2024, ISSN 2405-8963
Editor: Elsevier BV
DOI: 10.1016/J.IFACOL.2024.09.300

ALCHIMIA: what it really takes to build industrial AI in European steelmaking

Autores: David Blazquez, Irene Garcia and Barbara Fernandez
Publicado en: 2026
Editor: Steel Times International

Embedding Human-Centred AI in Steelmaking: Lessons from the Alchimia Project

Autores: Dean Stroud and Martin Weinel
Publicado en: 2026
Editor: Steel Times International

Optimizing Steelmaking with Models, AI, and Federated and Continual Learning (se abrirá en una nueva ventana)

Publicado en: Automaatioväylä, Edición 41(1), 2025, ISSN 0784-6428
Editor: Finnish Society of Automation
DOI: 10.5281/ZENODO.14741719

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