FONCER – SDG Innovation Network

Program Overview

Master’s, PhD, and undergraduate internship

The originality of the proposed training is based on the exceptional environment offered to students. The universities will have operational factory-laboratories on their premises. Funded by the Canada Foundation for Innovation (CFI), these factories enable the actual manufacturing of goods using cutting-edge technologies compared to the industry. They will be operated by student teams. This training program is led by Université Laval’s Lab-Usine, which plays a central role in the implementation and coordination of activities.

In these factory-laboratories, research teams will develop and experiment with systems for design, configuration, manufacturing, and production control. Each student research project will be associated with a partner company and will aim to address a complex digital transformation issue, previously identified with the company. The solution will first be developed and tested within the training factory, then deployed with the student at the partner company. The skills and leadership developed by these students will make them the change agents needed by the manufacturing industry to drive its digital transition. Additionally, management science students will intervene transversally within these factories and with industrial partners to establish best practices and success factors for digital transformation.

Scientific and technical objectives

The training program aims to address the technological challenges of the manufacturing sector, particularly the transition to the fourth-generation manufacturing systems. These systems enable the design and production of customized products at costs comparable to mass production. This includes automated engineering based on artificial intelligence and mathematical models, as well as self-reconfigurable cyber-physical production systems with automatic production launch (real-time automated operations planning, monitoring, assisted control, augmented reality, etc.). Each student research project is associated with an industry partner and aims to address a complex digital transformation challenge previously identified with the company. The solution is first developed and tested within the training factory, then deployed by the student at the partner’s site.

Collaboration and professional development objectives

The training emphasizes the development of interdisciplinary skills and the strengthening of ties between academia and industry. Each student is paired with an industry partner for the entire duration of their program, promoting full immersion in real industrial challenges. This approach not only enables the development of concrete solutions but also trains professionals capable of leading digital transformation projects. Industrial internships, combined with experience gained in university factories, promote the acquisition of leadership, project management, and collaborative work skills, thereby preparing a new generation of engineers, scientists, and managers capable of meeting the needs of the manufacturing sector.

List of Ongoing and Completed Projects

Students	Supervisor	Description
Abdelrahman Fawaz	Soumaya Yacout	Improving electric bus maintenance through Maintenance 4.0, 5G, and time synchronization.
Ahmed Daoud	Soumaya Yacout	Condition monitoring and global anomaly detection for Bombardier flights.
Charles Beaulieu	Jonathan Gaudreault	Automation of the electrostatic painting process in a dynamic industrial context with high diversity and low volume; a robust method combining 6DOF object tracking and 3D alignment for 3D model coverage and reconstruction.
Chloé Samuel	Jean-François Audy	Improving operational management of shared tools in a workshop with a digital twin.
Dorsaf Sfar	Tasseda Boukkeroub	Development of a method for evaluating industrial layouts through discrete-event simulation in the context of digital transformation.
Éloïse Prévost	Jonathan Gaudreault	Integrated software platform enabling the sharing of the same digital model at every stage of manufacturing.
Haitam Chaouki	Lucas Hof	Artificial intelligence-based modeling of electrostatic powder coating.
Hedir Oukassi	Soumaya Yacout	Digital twin for predictive maintenance of electric vehicles.
Jean-Thomas Sexton	Jonathan Gaudreault	Assisting welders/assemblers through real-time optimization and non-invasive augmented reality.
Lamyaa El Mahi	Soumaya Yacout	Condition monitoring and local anomaly detection for Bombardier flights.
Madina Ouali	Soumaya Yacout	Monitoring, analysis, and mitigation of anomalies and failures in electric vehicles (EV): test bench experiments, data collection, and analysis of the electric drivetrain.
Mehdi Lamouchi Chebbi	Tasseda Boukkeroub	Development of a digital twin of a critical equipment for learning and predicting its future state to enable proactive maintenance (Case of LIFE).
Omar Alkassas	Soumaya Yacout	Self-healing battery management system with online diagnostics and machine learning for electric vehicles.
Rafael Brunet	Lucas Hof	Development of an artificial intelligence model for powder coating application on a production line with high diversity and low volume.
Soheila Kabir	Rolf Wuthrich	Improvement of glass micromachining by spark-assisted chemical etching, using software sensors based on artificial intelligence technologies.
Zhaohan Zheng	Rolf Wuthrich	Extension of glass micromachining by spark-assisted chemical etching towards 6DOF.