WAS – Wire Additive manufacturing process Simulation

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Project description

To optimize the control strategies for the Manufacturing process by robotic metal-wire deposition.

If manufacturing processes by metal-wire deposition such as overlay welding have existed for decades, they are now experiencing a renewed interest from industrialists because, when they are exploited in the context of additive manufacturing techniques, they offer very high material removal rates and minimize problems in the areas of HSE (Health, Safety, Environment) related to the exploitation of metallic powders. Their exploitation on robotic cells requires the development of manufacturing strategies adapted to the parts to be manufactured using simulation tools, so as to guarantee the efficiency of the process while minimizing the deformations.

Launched in 2019, SystemX’s WAS project aims to optimize manufacturing strategies based on multiphysical modeling and will lead to the creation of a demonstrator. It is part of a larger project on the control of the WAAM (Wire and Arc Additive Manufacturing) process, supported by the Additive Factory Hub platform coordinated by CETIM.

Expected results

The development of a digital twin of the complete system, which will allow a robust and realistic simulation of the torch orientation position with respect to the part and its kinematic behaviour in all circumstances, in order to guarantee the effectiveness of the process;
The definition of innovative and optimal manufacturing strategies to meet the quality criteria required by the targeted applications;
The modelling, simulation and optimization of all or part of the process to limit deformation of parts and reduction of mechanical properties.

Implemented skills

	Data science and AI
	Scientific computation

Targeted markets

Oil and gas;
Aeronautics;
Energy production.

Supervised thesis in the framework of the project

Thesis #1: Optimization of additive manufacturing strategies by metal wire and arc deposition” done by François Rouchon (2019-2022) and supervised by Sylvain Lavernhe of the LURPA laboratory of ENS Paris-Saclay.