Digital Twins for Flow Simulation, Monitoring, and Control : Application to Battery Manufacturing Processes H/F

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Research work

Scientific Context:

The development of digital twins (DT) represents a significant advancement in industrial modeling and simulation (Abisset-Chavanne et al., 2024; Leng et al., 2021). DTs are virtual replicas of real objects, systems, or processes, integrating real-time data and enabling analysis, prediction, and optimization of their behavior. In the specific context of battery processes, this technology offers considerable potential for improving productivity, quality, and energy efficiency of manufacturing facilities (Ayerbe et al., 2022; Kies et al., 2023). Indeed, batteries require highly capable and efficient production processes to meet growing demand and quality and sustainability

requirements. Discrete event simulation is one of the key technologies for intelligent production systems (Leng et al., 2021). Using a discrete event simulation tool allows not only detailed modeling of complex processes involved in battery manufacturing but also testing alternative scenarios, identifying potential bottlenecks, and testing different algorithms and approaches for production flow management (Morabito et al., 2021). This includes inventory management, resource planning, integration of automation strategies and optimization of production-related processes such as maintenance, quality control, logistics, etc.

By integrating real production data, the simulator can provide an accurate and dynamic representation of the production environment, enabling line operators and managers to make informed decisions to improve the efficiency and sustainability of industrial operations (Abdoune et al., 2024).

Internship objectives :

The internship aims to explore the use of digital twins to improve production system monitoring and control, particularly in battery manufacturing

or similar processes. The first objective will be to conduct a thorough state-of-the-art study on the integration of digital twins in these specific industrial contexts. This literature review will emphasize best practices and emerging technologies used to model and simulate production flows, optimize resources, and anticipate operational challenges. Based on this analysis, the project will then focus on developing a discrete event-based model and simulation aimed at creating an accurate virtual replica of battery production processes. This simulator will not only faithfully reproduce real operations but also test optimization scenarios, evaluate the impact of new technologies, and develop advanced control strategies to improve process efficiency and sustainability (Destouet et al., 2024).

Work Program :

1. Literature survey on DTs in production system monitoring and control (3-4 weeks)

2. Development of a simulation model of a battery production line (8-10 weeks)

3. Simulator validation and calibration (2-4 weeks)

4. Development of a monitoring and control tool integrated into the simulator (3-6 weeks)

5. Internship report writing (3-4 weeks)

Internship context :

Host laboratory: CESI LINEACT (UR 7527), Laboratory for Digital Innovation for Businesses and Learning to Support the Competitiveness of Territories, anticipates and accompanies the technological mutations of sectors and services related to industry and construction. The historical proximity of CESI with companies is a determining element for our research activities, and has led us to concentrate our efforts on applied research close to the company and in partnership with them. A human-centered approach coupled with the use of technologies, as well as territorial networking and links with training,have enabled the construction of cross-cutting research; it puts humans, their needs and their uses, at the center of its issues and addresses the technological angle through these contributions. Its research is organized according to two interdisciplinary scientific teams and several application areas.

1. Team 1 « Learning and Innovating » mainly concerns Cognitive Sciences, Social Sciences and Management Sciences, Training Techniques and those of Innovation. The main scientific objectives are the understanding of the effects of the environment, and more particularly of situations instrumented by technical objects (platforms, prototyping workshops, immersive systems…) on learning, creativity and innovation processes.
2. Team 2 « Engineering and Digital Tools » mainly concerns Digital Sciences and Engineering. The main scientific objectives focus on modeling, simulation, optimization and data analysis of cyber physical systems. Research work also focuses on decision support tools and on the study of human-system interactions in particular through digital twins coupled with virtual or augmented environments.

These two teams develop and cross their research in application areas such as

1. Industry 5.0,
2. Construction 4.0 and City of the Future,
3. Digital Services.

Areas supported by research platforms, mainly that in Rouen to Factory 5.0 and those in Nanterre dedicated to Factory 5.0 and Construction 4.0.