Data and METAdata for advanced digitalization of manuFACTURING industrial lines

Informazioni relative al progetto

MetaFacturing

ID dell’accordo di sovvenzione: 101091635

DOI

10.3030/101091635

Progetto chiuso

Data della firma CE 29 Novembre 2022

Data di avvio 1 Gennaio 2023

Data di completamento 31 Dicembre 2025

Finanziato da

• Digital, Industry and Space

Costo totale

€ 5 768 142,75

Contributo UE

€ 4 997 759,13

Coordinato da

KATHOLIEKE UNIVERSITEIT LEUVEN
Belgium

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

Documents, reports (7)

AI and hybrid models for industrial ML

A document outlining the methodology for setting up AI and hybrid (AI+first principle) models in the context of industrial problems, focussing on reliability and scalability.

Continuum thermal material identification approach

A document outlining the DT based material identification approach. This document will both contain the theory, as well as guidelines and overview of the software setup to perform this identification.

Measures to guarantee valuable validation results

A report outlining the main KPI which will be considered for the MetaFacturing developments, as well as their target values to yield economic viability.

CHADA testing documentation

This document outlines how the various testing procedures, identification methods and obtained parameters for the materials can be converted into description files consistent with the CHADA standard.

Definition of integrated data consolidation

A document outlining the formatting requirements for the various data (streams) to guarantee inter-operability of the developed (sub)systems.

Operational, functional and usability requirements

A report to outline the various requirements for the MetaFacturing developments. This document will be updated as the development and validation continues and a final version is scheduled for M26.

Schedule and integration concept

A detailed deployment plan for the developed (and under development) MetaFacturing toolchain on the two industrial scale use cases.

Demonstrators, pilots, prototypes (1)

Final Requirements Update

A report on the requirements of effective MetaFacturing system, starting from the lesson learned on the serial production use cases.

Data Management Plan (1)

Project manag. handbook, quality plan and DMP

A document outlining the project management approach, as well as a data-management plan to ensure an efficient and safe exchange of data and models between the project and external partners.

Pubblicazioni

Peer reviewed articles (2)

Innovative Data Models: Transforming Material Process Design and Optimization

Autori: Amir M. Horr; Matthias Hartmann; Fabio Haunreiter
Pubblicato in: Metals, 2025, ISSN 2075-4701
Editore: MDPI
DOI: 10.3390/MET15080873

Insights on Weld Quality using Unsupervised Learning: Clustering of MIG/MAG Process Signals

Autori: Evans Doe Ocansey, Hannes Hinterbichler, Roxana-Maria Holom, Michael Araz
Pubblicato in: IOP Conference Series: Materials Science and Engineering, Numero 1315, 2024, ISSN 1757-8981
Editore: IOP Publishing
DOI: 10.1088/1757-899X/1315/1/012003

Book chapters (4)

Industrial Artificial Intelligence

Autori: Roxana Holom, Evans Doe Ocansey, Paul Heinzlreiter, Sandra Wartner
Pubblicato in: ISBN 978-3-200-09843-5
Editore: ÖWGP Österreichische Wissenschaftliche Gesellschaft für Produktionstechnik

Declarative Programming Approaches for Robust Anomaly Detection in HPDC Process Data

Autori: Tomasz Piotr Michno, Roxana-Maria Holom, Sebastian Schmalzer, Pauline Meyer-Heye, Giulia Scampone, Elias Riegler, Matthias Hartmann, Urban Repanšek, Nejc Košir, Peter Šifrer
Pubblicato in: Lecture Notes in Networks and Systems, Distributed Computing and Artificial Intelligence, Special Sessions I, 22nd International Conference, 2026, ISSN 2367-3389
Editore: Springer Nature Switzerland
DOI: 10.1007/978-3-032-05745-7_4

Towards a Zero-Defect in Welding: An Exploration of Factors to Improve the Training Data for Image Classification

Autori: Negin Javanbakhtghahfarokhi, Angel J. Lopez, Jorge Rodríguez-Echeverría, Sidharta Gautama
Pubblicato in: Lecture Notes in Networks and Systems, Intelligent Systems and Applications, 2024
Editore: Springer Nature Switzerland
DOI: 10.1007/978-3-031-66329-1_30

Data Quality Strategies in Gas Metal Arc Welding Production for Machine Learning Applications

Autori: Jorge Rodríguez-Echeverría, Evans Doe Ocansey, Roxana-Maria Holom, Tomasz Piotr Michno, Hannes Hinterbichler, Pauline Meyer-Heye, José Neves, Martin Schnall, Matthias Hartmann, Sidharta Gautama
Pubblicato in: Lecture Notes in Networks and Systems, Distributed Computing and Artificial Intelligence, Special Sessions I, 21st International Conference, 2025
Editore: Springer Nature Switzerland
DOI: 10.20944/PREPRINTS202503.1190.V1

Conference proceedings (3)

Data Models for Casting Processes – Performances, Validations and Challenges

Autori: A M Horr, R Gómez Vázquez, D Blacher
Pubblicato in: IOP Conference Series: Materials Science and Engineering, Numero 1315, 2024, ISSN 1757-8981
Editore: IOP Publishing
DOI: 10.1088/1757-899X/1315/1/012001

A physics-based welding digital twin for predicting temperature profiles under varying input parameters

Autori: Talha Saleh, Frank Naets
Pubblicato in: 19th CIRP Conference on Intelligent Computation in Manufacturing Engineering - CIRP ICME '25, Procedia CIRP, 2026, ISSN 2212-8271
Editore: Elsevier

Porosity Classification in High Pressure Die Casting using Thermal Images and Sensor Data Fusion via Fuzzy Cognitive Maps

Autori: Tomasz Michno, Roxana Holom, Sebastian Schmalzer, Giulia Scampone, Elias Riegler, Matthias Hartmann, Urban Repanšek, Nejc Košir, Peter Šifrer, Katarzyna Poczęta
Pubblicato in: 2026
Editore: SCITEPRESS

Other (1)

Automated Execution of Data Pipelines based on Configuration Files.

Autori: Károly Bósa; Paul Heinzlreiter
Pubblicato in: Open Research Europe, 2025
Editore: European Commission
DOI: 10.12688/OPENRESEUROPE.21019.1