Livrables
describe the development and the functionalities of projects repository
Final evaluation KPIsEvaluation methodology and KPIs
Edge AI Learning Pipeline Orchestrationdesign of the pipeline for model generation based on the data analysis, the (semi-)automatic knowlEdge extraction and the automated model generation
Report on the contribution of standardizationa report that documents contributions to standardization
Evolutionary Requirement Engineering and Innovations [Initial/Updated/Final]this report describes the requirements analysis all stakeholder requirements (functional and non-functional)
Initial Business models and requirementsthe deliverable presents a first version of the results of the stakeholders business requirements and models analyses for AI implementation
Vision, Specifications and System Architecture [Initial/Updated/Final]a public report documenting the requirements and architecture specifications
Technical review (Interim management report)Interim management report for the reviewers without the financial part otherwise required for management reports issued at the end of reporting periods
Final Business models and requirementsthe deliverable presents the final version of the results of the stakeholders’ business requirements and models analyses for AI implementation
Market Radar and Technology Adaptations [Initial/Updated/Final]this report describes the evolving landscape of AI technologies related with the industry and manufacturing sector
Final Description of KnowlEdge Repositorythis deliverable updates D5.2
Data management planspecifies how research data are handled during and after a research project
Bootstrapping on AI Modelsthis deliverable describes all the necessary procedures for execution of AI models in HPC or cloud environment
Pilot Evaluation methodology and implementation planImplementation and management plan
Project Manual (quality assurance and risk assessment)helps the Consortium with quality assurance and identification of relevant quality standards and best practices and means to achieve them and supports the implementation of internal quality checks
User need specification and scenario definition [Initial/Updated/Final]this report describes the knowlEdge scenarios based on the analysis of user needs. As part of the iterative methodology, the report is updated twice
Dissemination Roadmap & Activitiesthis deliverable provide the projects dissemination activities
Update on Exploitation strategy & IPR managementUpdates the report on the project's Exploitation strategy & IPR management
Last report on dissemination activitiesExploitation Strategy & IPR Management
The deliverable describes how to facilitate the acceptance and utilisation by the market of the developed solutions
AI model Descriptiondescribe all the technologies and APIs to make AI models functional in the process environment
Training, Workshops, and Seminarsa report that documents all the workshops and seminars that were planned during the project, their contents and their goals
Generalized Automated Learning for Industrial Environmentsthis deliverable describes a multi-scale dynamic environment for autonomous learning and re-training of the AI models
Final Data Management and Data Quality modulesthis deliverable will contain the final work done in task T3.2. In particular will describe the adopted approach, architecture and structure of the data quality and management modules with respect to the platform.
(Semi-)Automatic Knowledge Discovery for AI Model Generationinitial data analysis results including the description and evaluation of used methods
First report on dissemination activitiesInitial Data Management and Data Quality modules
this deliverable will contain the initial work done in task T32 In particular will describe the initial approach architecture and structure of the data quality and management modules
this deliverable provide a brief description of the initial projects website and is functionalities
Initial Site-wide Data Storage and Governance suitprovides the overview of the results produced in T3.3. This deliverable lists the data models, protocols and planned architecture of the components to be developed.
Human-AI Collaboration and Domain Knowledge FusionDescribe the collaboration of humans with an automated AI pipeline.
Final Decision Support Frameworkthis deliverable updates D7.2.
Initial explainable mechanisms DFSa prototype deliverable that documents the design and implementation of the integrated 2-Axis DSF, that encapsulates decision strategies and rules, KPIs, as well as a number of parameters that affect product and process quality.
Initial user-centric dashboards as enable explainable AIa prototype deliverable that documents all the enhanced visualizations that support the decision making mechanisms of the 2-Axis DSF
Initial Provisioning and Deployment Management Toolplanned Concept, architecture and communication protocols that shall be developed as part of T6.3
Final site-wide data collection and integration toolkitprovides the overview of the results produced in T3.1. This deliverable updates on D3.1 with the details of the deployments and results.
Final knowlEdge Marketplace Platformthis deliverable updates D5.4
Full knowledge websitethis deliverable provide a brief description of the full project website and is functionalities
Final Provisioning and Deployment Management Toolthis deliverable updates D6.4
Initial site-wide data collection and integration toolkitprovides the overview of the results produced in T31 This deliverable lists the data models protocols and planned architecture of the components to be developed
Final Site-wide Data Storage and Governance suitprovides the overview of the results produced in T3.3. This deliverable updates on D3.5 with the details of the deployments and results.
Final user-centric dashboards as enable explainable AIthis deliverable updates D7.4
Initial KnowlEdge Marketplace Platforma public prototype deliverable that documents the architecture, the features and the capabilities of the knowlEdge marketplace
"Pilot#3: Bonfiglioli, Gear machines for Industrial applications – Final"
"Final assessment of the pilot#3: Bonfiglioli, Gear machines for Industrial applications"
"Pilot3#: Bonfiiglioli, Gear machines for Industrial applications""Pilot#1: Parmalat, Milk Industry"
"Pilot#1: Parmalat, Milk Industry – Final"
"Final assessmentof the pilot#1: Parmalat, Milk Industry"
"Pilot#2: Kautex Textron, Plastic parts for Car Industry – Final""Final assessmen tof the pilot#2: Kautex Textron, Plastic parts for Car Industry"
AI maturity tool results and KPI performance indicators (assessment framework). Describes the overall impact of the application of AI models on the pilot sites. These results are provided as open research data for others to access and re-use and may for example be used to validate results in scientific publications.
Publications
Auteurs:
Jona Scholz
Maike Holtkemper
Alexander Graß
Christian Beecks
Publié dans:
Artificial Intelligence in Manufacturing, Numéro 2024, 2024, Page(s) 351-360, ISBN 978-3-031-46452-2
Éditeur:
Springer Nature Switzerland
DOI:
10.1007/978-3-031-46452-2_20
Auteurs:
Marta Barroso
Daniel Hinjos
Pablo A. Martin
Marta Gonzalez-Mallo
Victor Gimenez-Abalos
Sergio Alvarez-Napagao
Publié dans:
Artificial Intelligence in Manufacturing, Numéro 2024, 2024, Page(s) 333-350, ISBN 978-3-031-46452-2
Éditeur:
Springer Nature Switzerland
DOI:
10.1007/978-3-031-46452-2_19
Auteurs:
Sisay Adugna Chala
Alexander Graß
Publié dans:
Artificial Intelligence in Manufacturing, Numéro 2024, 2024, Page(s) 43-54, ISBN 978-3-031-46452-2
Éditeur:
Springer Nature Switzerland
DOI:
10.1007/978-3-031-46452-2_3
Auteurs:
Stefan Walter
Publié dans:
Service Design for Emerging Technologies Product Development, Numéro 21 July 2023, 2023, Page(s) 207-232, ISBN 978-3-031-29306-1
Éditeur:
Springer, Cham
DOI:
10.1007/978-3-031-29306-1_12
Auteurs:
Alexandros Nizamis
Georg Schlake
Georgios Siachamis
Vasileios Dimitriadis
Christos Patsonakis
Christian Beecks
Dimosthenis Ioannidis
Konstantinos Votis
Dimitrios Tzovaras
Publié dans:
Artificial Intelligence in Manufacturing, Numéro 2024, 2024, Page(s) 27-41, ISBN 978-3-031-46452-2
Éditeur:
Springer Nature Switzerland
DOI:
10.1007/978-3-031-46452-2_2
Auteurs:
Victor Anaya
Enrico Alberti
Gabriele Scivoletto
Publié dans:
Artificial Intelligence in Manufacturing, Numéro 2024, 2024, Page(s) 181-193, ISBN 978-3-031-46452-2
Éditeur:
Springer Nature Switzerland
DOI:
10.1007/978-3-031-46452-2_10
Auteurs:
Stefan Walter
Markku Mikkola
Publié dans:
Artificial Intelligence in Manufacturing, Numéro 2024, 2024, Page(s) 281-300, ISBN 978-3-031-46452-2
Éditeur:
Springer Nature Switzerland
DOI:
10.1007/978-3-031-46452-2_16
Auteurs:
Fabian Berns, Jan David Hüwel, Christian Beecks
Publié dans:
IEEE International Conference on Data Mining, Numéro 2021, 2021, Page(s) 1000-1005, ISBN 978-1-6654-2398-4
Éditeur:
IEEE
DOI:
10.1109/icdm51629.2021.00113
Auteurs:
Jan David Hüwel; Fabian Berns; Christian Beecks
Publié dans:
IEEE International Conference on Big Data, Numéro 2021, 2021, Page(s) 3584-3588, ISBN 978-1-6654-3902-2
Éditeur:
IEEE
DOI:
10.1109/bigdata52589.2021.9671767
Auteurs:
Alvarez-Napagao, Sergio; Ashmore, Boki; Barroso, Marta; Barrué, Cristian; Beecks, Christian; Berns, Fabian; Bosi, Ilaria; Chala, Sisay Adugna; Ciulli, Nicola; Garcia-Gasulla, Marta; Grass, Alexander; Ioannidis, Dimosthenis; Jakubiak, Natalia; Köpke, Karl; Lämsä, Ville; Megias, Pedro; Nizamis, Alexandros; Pastrone, Claudio; Rossini, Rosaria; Sànchez-Marrè, Miquel; Ziliotti, Luca
Publié dans:
IEEE 19th International Conference on Industrial Informatics, Numéro 2021, 2021, Page(s) 7, ISBN 978-1-7281-4395-8
Éditeur:
IEEE
DOI:
10.1109/indin45523.2021.9557410
Auteurs:
Georg Stefan Schlake; Jan David Hüwel; Fabian Berns; Christian Beecks
Publié dans:
IEEE International Conference on Data Engineering Workshops, Numéro 2022, 2022, Page(s) 70-73, ISBN 978-1-6654-8104-5
Éditeur:
IEEE
DOI:
10.1109/icdew55742.2022.00015
Auteurs:
Fabian Berns, Joschka Hannes Strueber, Christian Beecks
Publié dans:
33rd International Conference on Scientific and Statistical Database Management, Numéro 2021, 2021, Page(s) 209-213, ISBN 978-1-4503-8413-1
Éditeur:
ACM
DOI:
10.1145/3468791.3468839
Auteurs:
Konstantinos Georgiadis, Alexandros Nizamis, Thanasis Vafeiadis, Dimosthenis Ioannidis, Dimitrios Tzovaras
Publié dans:
Procedia Computer Science, Numéro 204, 2022, Page(s) 424-431, ISSN 1877-0509
Éditeur:
Elsevier
DOI:
10.1016/j.procs.2022.08.052
Auteurs:
Fabian Berns, Jan Hüwel, Christian Beecks
Publié dans:
SN Computer Science, Numéro 3 (4), 2022, Page(s) 300, ISSN 2661-8907
Éditeur:
Springer Nature
DOI:
10.1007/s42979-022-01186-x
Auteurs:
Alberti, Enrico; Alvarez-Napagao, Sergio; Anaya, Victor; Barroso, Marta; Barrué, Cristian; Beecks, Christian; Bergamasco, Letizia; Chala, Sisay Adugna; Gimenez-Abalos, Victor; Graß, Alexander; Hinjos, Daniel; Holtkemper, Maike; Jakubiak, Natalia; Nizamis, Alexandros; Pristeri, Edoardo; Sànchez-Marrè, Miquel; Schlake, Georg; Scholz, Jona; Scivoletto, Gabriele; Walter, Stefan
Publié dans:
Systems, Numéro 12, 48, 2024, Page(s) 32, ISSN 2079-8954
Éditeur:
MDPI
DOI:
10.3390/systems12020048
Auteurs:
Domenech i Vila, M.; Gnatyshak, D.; Tormos, A.; Gimenez-Abalos, V.; Alvarez-Napagao, S.
Publié dans:
Electronics, Numéro 13(3), 573, 2024, Page(s) 19, ISSN 2079-9292
Éditeur:
MDPI
DOI:
10.3390/electronics13030573
Auteurs:
Stefan Walter
Publié dans:
Transportation Research Procedia, Numéro 72, 2023, Page(s) 3443-3449, ISSN 2352-1465
Éditeur:
Elsevier
DOI:
10.1016/j.trpro.2023.11.773
Auteurs:
Stefan Walter
Publié dans:
Discover Artificial Intelligence, Numéro 3:28, 2023, ISSN 2731-0809
Éditeur:
Springer Nature
DOI:
10.1007/s44163-023-00061-9
Auteurs:
Domènech Vila, Marc; Gnatyshak, Dmitry; Tormos Llorente, Adrián; Álvarez Napagao, Sergio
Publié dans:
Frontiers in Artificial Intelligence and Applications, Numéro Volume 356: Artificial Intelligence Research and Development, 2022, Page(s) 355-364, ISSN 0922-6389
Éditeur:
IOS Press
DOI:
10.3233/faia220358
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