ProjectP5

Laser welding and machining processes are now widely used in strategic industrial sectors such as aerospace, automotive and e-mobility. Yet their development remains lengthy, costly and heavily dependent on successive test campaigns.

This empirical approach leads to:

• high resource consumption (materials, energy, tools)
• rejects caused by unanticipated defects
• long development times before industrialisation

In a context of increased competition and the transition towards more sustainable industry, it is becoming essential to move from a trial-and-error approach to a predictive, data-driven approach.

The P5 project therefore aims to fundamentally transform the way laser processes are designed, set up and secured, using artificial intelligence, simulation and advanced instrumentation.

The P5 project aims to make laser processes quicker to develop, more reliable and fully data-driven.

Its main objectives are to:

• Integrate advanced sensors on machines for real-time process monitoring
• Develop AI-based predictive models to anticipate defects
• Build an intelligent database linking manufacturing parameters, sensor data and characterisation results
• Reduce process development time by 50% through simulation and optimisation
• Develop an AI engine capable of automatically adjusting welding and machining parameters
• Move from TRL 4 to TRL 6 by the end of the project

These objectives converge on a single goal: making the industrialisation of laser processes more reliable and faster while reducing costs and rejects.

As project coordinator, IREPA LASER leads an integrated approach combining experimentation, simulation and artificial intelligence.

Process instrumentation and monitoring
The project begins by deploying sensors directly on the equipment to track the critical parameters of laser welding and machining processes in real time.
These data greatly improve understanding of the physical phenomena involved.

Optimising and understanding laser processes
IREPA LASER develops and improves laser welding processes by integrating innovative beam shaping methods, in order to better control the deposited energy and material interactions.

Advanced thermomechanical simulation
Numerical models are used to simulate the laser welding process in order to:

• predict results before testing
• optimise operating parameters
• reduce the number of experiments required

Building an intelligent database
A centralised database is created to link:

• machine data
• sensor data
• analysis and characterisation results

This structure is designed to be directly usable by artificial intelligence engines.

Developing an artificial intelligence engine
The project includes the development of machine learning algorithms capable of:

• predicting defects before they occur
• optimising process parameters
• automatically adapting settings in real time

This approach enables a move towards autonomous, self-optimising processes.

The P5 project is carried out in collaboration between industrial and technological partners:

• IREPA LASER – Project coordinator, process development, instrumentation and AI
• CIRTES – Technological and scientific partner, expertise in simulation and data structuring

The project is funded under Région Grand Est and FEDER schemes:

• Total project cost: €999,034.50
• FEDER (ERDF) funding: €256,682.00
• Région Grand Est funding: €171,122.00
• Overall funding rate: approx. 70% (Région Grand Est + FEDER)

The P5 project delivers major benefits for manufacturing industry and advanced laser processes.

Lower development costs and shorter lead times
With a 50% reduction in process development time, manufacturers can significantly accelerate the time to market of their processes.

Fewer defects and rejects
Predicting defects upstream reduces non-conformities and improves the quality of the parts produced.

Optimised resource consumption
The project helps reduce waste of materials, energy and tooling, in line with a more sustainable approach to industry.

Automated, intelligent processes
Integrating AI engines enables automatic adjustment of manufacturing parameters, reducing human intervention and increasing process robustness.

Wider adoption of laser technologies
By reducing the complexity of process development, the project makes laser welding and machining technologies accessible to a larger number of manufacturers.