PhD Thesis Proposal Toward a Smart Digital Twin for Aeronautical Production Lines: From Data To Model

This PhD is part of a supervision by IMT Mines Albi CGI team (France)

·       Location: Albi, CGI  https://cgi.imt-mines-albi.fr/

·       Expected start date: September 2026

·       Funding: CORAC PME Infinity

·       Keywords: Agility / Model-Driven Engineering / Interoperability / Systemic Approach / Complex Systems / Semantic Web / Digital Twin engineering.

In the context of Industry 4.0 and beyond, industrial stakeholders are deploying digitization strategies where operational systems are connected to increasingly intelligent cyber systems. In this context, the organizational digital twin refers to the system that enables bidirectional interaction between a physical organization (people and systems) and its virtual replica, with the goal of globally managing flows.

Recent reviews and typologies (e.g., Kritzinger et al., 2018; Onaji et al., 2022; Soori et al., 2023; Kober et al., 2024; Traoré, 2024; Namaki Araghi et al., 2025) on digital twins in manufacturing show that the digital twin manages workshop data and interoperates with various systems (MES, ERP, traceability, quality). It uses physics-based or data-driven simulation models focused on flow management, and orchestrates services oriented toward data processing or simulation models for decision support. The digital twin has various objectives (assessing the impact of changes and performance, optimization) and interacts with users as needed to support decisions at different time horizons (tactical and operational) and potentially of different natures (planning, maintenance, quality). Implementing such a system represents a major organizational and technical challenge that must be adapted to the specificities and evolving needs of industry.

The INFINITY project aims to design and test a digital twin system to support industrial planning in the context of the surface treatment industry. It is in partnership with a leading industrial player in this field for the aeronautics industry, Mecaprotec, and the project is funded by CORAC. Since large-scale industrial digital twin projects are rare, one of the project’s challenges is to study the scalability of the proposed system and the impact of the digital twin on the organization. As part of the project also focuses on designing new treatment processes, another aspect involves evaluating the impact of these new processes on the production system’s digital twin.

Several PhD thesis are being conducted simultaneously within this project, each with complementary focuses on:

(i)     data collection and construction of informational needs for the digital twin (this PhD thesis proposal),

(ii)   digital twin for short- and medium-term planning,

(iii)  digital twin for strategic planning.

This PhD thesis focuses on the management of production line data, which is a key challenge: it determines these industrial agents’ ability to effectively manage their processes, ensure the traceability of their operations, and meet the sector’s regulatory requirements. The nature of the operations performed (painting, surface treatment) imposes significant constraints on the availability of this data: its automatic, real-time collection is not always technically feasible, which poses a major challenge for feeding the digital twin.

The thesis topic proposed as part of this project aims to address the following scientific challenges:

The main steps of the PhD include:

·      Analysis of requirements and informational needs,

·      Definition meta-models,

·      Definition of algorithms to feed the models

·      Definition of algorithms to manage missing data,

·      Application to a few examples from the industrial case study,

·      Evaluation of the PoC.

[1] J. Bézivin, ‘On the unification power of models’, Software & Systems Modeling, vol. 4, no. 2, pp. 171–188, 2005.

[2] W. Charles, N. Aussenac-Gilles, and N. J. Hernandez, Temporalité et graphes de connaissances : analyse théorique et enjeux pratiques », in 34es Journées francophones d’Ingénierie des Connaissances (IC 2023)@ PFIA 2023, AFIA, 2023, pp. 1–10.

[3] Y. Chasseray, A.-M. Barthe-Delanoë, S. Négny, and J.-M. Le Lann, ‘A generic metamodel for data extraction and generic ontology population’, Journal of Information Science, vol. 48, no. 6, pp. 838–856, 2022.

[4] N. Shah, S. Shah, P. Jain, and N. Doshi, ‘Overview of Present-Day IoT Data Processing Technologies’, Procedia Computer Science, vol. 210, pp. 277–282, Jan. 2022, doi: 10.1016/j.procs.2022.10.150.

[5] S. Arbesman, The half-life of facts: Why everything we know has an expiration date. Penguin, 2013.

[6] E. S. Knudsen and L. B. Lien, ‘The half-life of knowledge and strategic human capital’, Human Resource Management Review, vol. 33, no. 4, p. 100989, 2023.

IMT Mines Albi, a school under the authority of the French Ministry of Industry, is part of the Institut Mines-Télécom, France’s leading group of engineering and management schools. At the forefront of industrial and academic challenges on the international stage, it acts as a scientific and economic driver for its region by combining its four missions—training engineers with a focus on sustainable development, conducting scientific research, contributing to economic development, and promoting the culture of science, technology, and innovation—into a virtuous and innovation-driven cycle.

Its position in education and research establishes IMT Mines Albi as a reference school in three of the IMT’s four thematic areas: future sustainable industries, energy - circular economy and society and, health and well-being engineering.

Through its Centre Génie Industriel (CGI), IMT Mines Albi conducts research at the intersection of artificial intelligence and industrial engineering, in collaboration with national and international public and industrial partners.

The Centre Génie Industriel (CGI) (cgi.imt-mines-albi.fr) comprises approximately 70 people, including 25 PhD students. The center focuses on supporting the transition of ecosystems by enabling responsible and sustainable decision-making in unstable or disrupted environments. This is achieved through the representation, modeling, and analysis of organizational data to formalize knowledge that leads to decision-making in heterogeneous, collaborative, uncertain, and/or disrupted contexts.

The CGI is structured around applied research axes and scientific programs. The applied research axes are:

·       FLOWS: Flexible Logistics and Operations for Sustainable Worlds (this PhD is affiliated with this axis).

·       DiSCS: Digital Systems for Crisis Management and Security;

·       TRACE: Territorial Resilience, Agility, and Circular Economy;

·       WHOPS: Well-being and Health through Organizational Processes and Services.

The two core scientific programs underpinning these research axes are:

·       HOPOPOP: Hybridization for Operations & Planning, Organizations & Performance, Optimization & Problem-solving.

·       AIME-DM: Automated Information Modeling and Extraction for Decision-Makers (this PhD is affiliated with the AIME-DM program).

·       General engineering degree in industrial engineering or computer science, or a master’s degree in industrial engineering or computer science, or equivalent.

·       Conceptual modelling of complex systems (Model-Driven Engineering),

• Systems analysis,

• Software architecture (including n-tiers, Service-Oriented Architecture),

• Proficiency in UML is essential.

·       Minimum B2 level in English required (see European benchmark, e.g., TOEIC score of 785/990 minimum), attested by a formal certification (TOEFL, TOEIC, IELTS). Fluency in French (B2 minimum) is an asset to facilitate communication with experts and field stakeholders (SDIS) during the design, implementation, and testing phases of the case study

• Autonomy and ability to work collaboratively within a research team.

• Motivation to contribute to industrial application of research.

·       Knowledge of algorithms, the Semantic Web, and multi-agent modelling and/or simulation is a plus.

Anne-Marie Barthe-Delanoë, CGI IMT Mines Albi, anne-marie.barthe@mines-albi.fr

Myriam Lamolle, CGI IMT Mines Albi, myriam.lamolle@mines-albi.fr