Explainable Manufacturing Artificial Intelligence

Informations projet

XMANAI

N° de convention de subvention: 957362

DOI

10.3030/957362

Projet clôturé

Date de signature de la CE 28 Juillet 2020

Date de début 1 Novembre 2020

Date de fin 30 Avril 2024

Financé au titre de

• INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT)

Coût total

€ 5 998 902,50

Contribution de l’UE

€ 5 998 902,50

Coordonné par

TXT E-TECH SRL
Italy

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Livrables

Other (8)

XMANAI Platform - Beta Version

The beta release of the platform, with supporting documentation.

XMANAI Platform - Alpha Version

An alpha version of the platform delivered for early assessment accompanied with the updated functional and nonfunctional requirements system architecture the bundles design and the APIs The initial interface includes the basic UIUX for the users of the platform

XMANAI AI Bundles - First Release

This deliverable will provide the first release of the tools to be developed in WP2 following the prioritisation of the user stories based on the MVP of WP1

XMANAI Asset Management Bundles - First Release

This deliverable will provide the first release of the tools to be developed in WP2 following the prioritisation of the user stories based on the MVP of WP1

System Architecture, Bundles Placement Plan and APIs Design

The first version of the system architecture the bundles design the APIs

XMANAI Asset Management Bundles - Second Release

This deliverable will provide the second and final release of the tools to be developed in WP2

XMANAI AI Bundles - Second Release

This deliverable will provide the second and final release of the tools to be developed in WP2

XMANAI Platform - Version 1.0

The final release of the platform and tools, with documentation.

Documents, reports (13)

Final Evaluation and Impact Assessment Report

Evaluation of the demonstrators using the final version of the platform (v1.00) and impact assessment consolidating the input of Tasks 6.2-6.8.

XMANAI Knowledge Transfer and Replication Roadmap

Recommendations and guidelines for porting scaling up and replication the XMANAI concept at larger scales, and for transferring knowledge of XMANAI and the developed assets and infrastructure, to other manufacturing sectors and domains.

2nd Dissemination, Communication, Standardisation Report and Plan for next period

D84 will report on the WP8 activities performed during the 2nd period and will provide an updated plan for all dissemination standardisation and communication activities that will be scheduled for the 3rd reporting period M31M42

1st Dissemination and Communication and Plan for next period

D83 will report on all the WP8 activities performed during the 1st reporting period and will provide an updated plan for all dissemination and communication activities that will be scheduled for the 2nd reporting period M19M30

Draft Catalogue of XMANAI AI and Graph Machine Learning Models

This deliverable will provide the list of baseline models that are generally appropriate for the manufacturing domain and for the different pilot scenarios to be supported by XMANAI identifying their main parameters and structural elements as well as their inputs outputs and usage scenarios

AI Bundles Methods and System Designs

This deliverable will provide the methods and the designs to be used for the development of the bundles of WP3 alongside with low fidelity functional mockups that will be used for a first round of evaluation

Final Dissemination, Communication and Standardisation Report

This deliverable aims to provide the final report on all dissemination, communication, standardisation and stakeholder engagement activities performed during the last reporting period of the project.

State of the Art Review in XMANAI Research Domains

D11 will include a thorough analysis of the state of the art of the research domains to be touched by XMANAI delivering a list of concepts methods and tools that will be further considered by the project for the implementation of both the XMANAI methodology the platform and the algorithms and models to be constructed

Updated Requirements and AI/Graph Analytics focused MVP

This deliverable will update the requirements and will provide an MVP version that will focus on the AI and Graph Analytics parts of the platform following the initial work on those that will be performed in WP3 and WP4

Final XMANAI MVP

Final version of the methodology and a fullyfledged MVP describing the overall operations of the XMANAI platform which will drive the implementation work of the next WPs

Asset Management Bundles Methods and System Designs

This deliverable will provide the methods and the designs to be used for the development of the bundles of WP2 alongside with low fidelity functional mockups that will be used for a first round of evaluation

XMANAI Concept Detailing, Initial Requirements, Usage Scenarios and Draft MVP

This deliverable will coin the detailed XMANAI concept and will provide the initial requirements of the platform the early identification of data sources to be used and an initial set of usage scenarios driving a high level MVP that will describe core backbone data management and analysis operations to be designed in WP2 and WP3

Project Evaluation Plan and First Round of Demonstrators Implementation Plan

Documentation of the evaluation framework and validation methodology defining the various practices for recording feedback from the demonstration activities and including a set of testcases to be executed by the demonstrator partners

Open Research Data Pilot (1)

Ethics and Data Management Plan

Direct outcome of T92 documenting the data management handling plan and the ethics report and safeguarding measures It will constitute a living document

Websites, patent fillings, videos etc. (1)

Project Website and Communication Channels Instantiation

This deliverable is related to the provision of the Website of the project and will set up all needed Web 20 and social channels that will be used during the project for communication

Publications

Peer reviewed articles (3)

Explainability as the key ingredient for AI adoption in Industry 5.0 settings

Auteurs: Agostinho Carlos; Dikopoulou Zoumpolia; Lavasa Eleni; Perakis Konstantinos; Pitsios Stamatis; Branco Rui; Reji Sangeetha; Hetterich Jonas; Biliri Evmorfia; Lampathaki Fenareti; Rodríguez Del Rey Silvia and Gkolemis Vasilis
Publié dans: Frontiers in Artificial Intelligence, Numéro 6, 2023, ISSN 2624-8212
Éditeur: Frontiers
DOI: 10.3389/frai.2023.1264372

A novel human-centric framework for maintenance digitization using Augmented Reality

Auteurs: Valentini, Lorenzo; Grandi, Fabio; Peruzzini, Margherita, Pellicciari, Marcello
Publié dans: International Journal of Agile Systems and Management, 2024, ISSN 1741-9182
Éditeur: Inderscience
DOI: 10.5281/zenodo.12075524

A methodology to guide companies in using Explainable AI-driven interfaces in manufacturing contexts interfaces in manufacturing contexts

Auteurs: Grandi, Fabio; Zanatto, Debora; Capaccioli, Andrea; Napoletano, Linda; Cavallaro, Sara; and Peruzzini, Margherita
Publié dans: Procedia Computer Science, Numéro 232, 2024, Page(s) 3112-3120, ISSN 1877-0509
Éditeur: Elsevier
DOI: 10.1016/j.procs.2024.02.127

Conference proceedings (12)

Industrial Asset Management and Secure Sharing for an XAI Manufacturing Platform

Auteurs: Reji, Sangeetha; Hetterich, Jonas; Pitsios, Stamatis; Gkolemi, Vasilis; Perez-Castanos, Sergi; Pertselakis, Minas
Publié dans: 29th ICE IEEE/ITMC Conference, 2023
Éditeur: IEEE
DOI: 10.5281/zenodo.7991121

An Explainable & Secure Artificial Intelligence platform for the manufacturing industry – Applications & lessons learnt

Auteurs: Konstantinos Perakis; Silvia Rodriguez; Fenareti Lampathaki
Publié dans: Proceedings of Interoperability for Enterprise Systems and Applications Workshops, co-located with 12th International Conference on Interoperability for Enterprise Systems and Applications (I-ESA 2024), 2024
Éditeur: CEUR Workshop Proceedings
DOI: 10.5281/zenodo.12698135

Explainable Artificial Intelligence Bundles for Algorithm Lifecycle Management in the Manufacturing Domain

Auteurs: Biliri Evmorfia; Lampathaki Fenareti; Mandilaras George; Prieto-Roig Ausias; Calabresi Mattia; Branco Rui; Gkolemis Vasileios
Publié dans: 29th ICE IEEE/ITMC Conference, 2023
Éditeur: IEEE
DOI: 10.5281/zenodo.8010284

UX-Driven Methodology to Design Usable Augmented Reality Applications for Maintenance

Auteurs: Valentini, Lorenzo; Grandi, Fabio; Peruzzini, Margherita; Pellicciari, Marcello
Publié dans: Proceedings of the 30th ISTE International Conference on Transdisciplinary Engineering, 2023, Page(s) 42 - 51
Éditeur: IOS Press
DOI: 10.3233/atde230596

Explainable AI in Manufacturing: an Analysis of Transparency and Interpretability Methods for the XMANAI Platform

Auteurs: Branco, Rui; Agostinho, Carlos; Gusmeroli, Sergio; Lavasa, Eleni; Dikopoulou, Zoumpolia; Monzo, David; Lampathaki, Fenareti
Publié dans: 29th ICE IEEE/ITMC Conference, 2023
Éditeur: IEEE
DOI: 10.5281/zenodo.7987108

Regionally Additive Models: Explainable-by-design models minimizing feature interactions

Auteurs: Gkolemis, Vasilis; Tzerefos, Anargiros; Dalamagas, Theodore; Ntoutsi, Erini; Diou, Christos
Publié dans: Uncertainty meets Explainability Workshop at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD 2023), 2023
Éditeur: ECML PKDD
DOI: 10.5281/zenodo.10933636

DALE: Differential Accumulated Local Effects for efficient and accurate global explanations

Auteurs: Gkolemis, Vasilis; Dalamagas, Theodore; Diou, Christos
Publié dans: Asian Conference in Machine Learning (ACML), 2023
Éditeur: ACML
DOI: 10.5281/zenodo.7220114

"Moving from ""black box"" to ""glass box"" Artificial Intelligence in Manufacturing with XMANAI"

Auteurs: Fenareti Lampathaki, Carlos Agostinho, Yury Glikman, Michele Sesana
Publié dans: 27th ICE/IEEE International Technology Management Conference, Cardiff UK, June 21-23 2021, 2021
Éditeur: 27th ICE/IEEE International Technology Management Conference
DOI: 10.1109/ice/itmc52061.2021.9570236

Towards Explainable AI Validation in Industry 4.0: A Fuzzy Cognitive Map-based Evaluation Framework for Assessing Business Value

Auteurs: Dikopoulou, Zoumpolia; Lavasa, Eleni; Perez-Castanos, Sergi; Monzo, David; Moustakidis, Serafeim
Publié dans: 29th ICE IEEE/ITMC Conference, 2023
Éditeur: IEEE
DOI: 10.5281/zenodo.7997624

A novel Explainable Artificial Intelligence and secure Artificial Intelligence asset sharing platform for the manufacturing industry

Auteurs: Dimitris Miltiadou; Konstantinos Perakis; Michele Sesana; Mattia Calabresi; Fenareti Lampathaki; Evmorfia Biliri
Publié dans: 29th ICE IEEE/ITMC Conference, 2023
Éditeur: IEEE
DOI: 10.5281/zenodo.8010282

Integrating Explainability-by-Design for Transparent and Efficient AI in Manufacturing

Auteurs: Capaccioli, Andrea; Agostinho, Carlos; Antonello, Veronica; Lampathaki, Fenareti; Sesana, Michele
Publié dans: Proceedings of Interoperability for Enterprise Systems and Applications Workshops, co-located with 12th International Conference on Interoperability for Enterprise Systems and Applications (I-ESA 2024), 2024
Éditeur: CEUR Workshop Proceedings
DOI: 10.5281/zenodo.12665533

RHALE: Robust and Heterogeneity-Aware Accumulated Local Effects

Auteurs: Gkolemis, Vasilis; Dalamagas, Theodore; Ntoutsi, Eirini; Diou, Christos
Publié dans: Proceedings of 26th European Conference on Artificial Intelligence (ECAI 2023), 2023, Page(s) 859 - 866
Éditeur: IOS Press
DOI: 10.3233/faia230354

Book chapters (4)

Holistic Production Overview: Using XAI for Production Optimization

Auteurs: Perez-Castanos, Sergi; Prieto-Roig, Ausias; Monzo, David; and Colomer-Barbera, Javier
Publié dans: Artificial Intelligence in Manufacturing, 2023, Page(s) 423–436, ISBN 978-3-031-46452-2
Éditeur: Springer, Cham
DOI: 10.1007/978-3-031-46452-2_24

XAI for Product Demand Planning: Models, Experiences, and Lessons Learnt

Auteurs: Fenareti Lampathaki, Enrica Bosani, Evmorfia Biliri, Erifili Ichtiaroglou, Andreas Louca, Dimitris Syrrafos, Mattia Calabresi, Michele Sesana, Veronica Antonello & Andrea Capaccioli
Publié dans: Artificial Intelligence in Manufacturing, 2023, Page(s) 437–458, ISBN 978-3-031-46452-2
Éditeur: Springer, Cham
DOI: 10.1007/978-3-031-46452-2_25

Toward Explainable Metrology 4.0: Utilizing Explainable AI to Predict the Pointwise Accuracy of Laser Scanning Devices in Industrial Manufacturing

Auteurs: Eleni Lavasa, Christos Chadoulos, Athanasios Siouras, Ainhoa Etxabarri Llana, Silvia Rodríguez Del Rey, Theodore Dalamagas & Serafeim Moustakidis
Publié dans: Artificial Intelligence in Manufacturing, 2023, Page(s) 479–501, ISBN 978-3-031-46452-2
Éditeur: Springer, Cham
DOI: 10.1007/978-3-031-46452-2_27

Process and Product Quality Optimization with Explainable Artificial Intelligence

Auteurs: Michele Sesana, Sara Cavallaro, Mattia Calabresi, Andrea Capaccioli, Linda Napoletano, Veronica Antonello & Fabio Grandi
Publié dans: Artificial Intelligence in Manufacturing, 2023, ISBN 978-3-031-46452-2
Éditeur: Springer, Cham
DOI: 10.1007/978-3-031-46452-2_26