Real-time simulation of friction welding processes for the development of their digital twins: application to linear friction welding (LFW)

id="offer-description" class="ecl-u-type-heading-2 ecl-u-mt-none ecl-u-mb-l">Offer Description The main objective of this PhD project is to develop a real-time computational framework for the simulation of Linear Friction Welding (LFW). The proposed approach will rely on surrogate models, particularly Physics-Informed Neural Networks (PINNs), combining physical knowledge with multiscale experimental data ranging from the microstructural scale to the machine scale. The models will aim to predict the process behaviour and the formation of defects and anomalies, such as voids and cracks, according to the operating parameters. Particular attention will be paid to material flow, contact and bonding conditions, and their effects on microstructure evolution. Ultimately, this real-time simulation framework will provide a key computational building block for the future development of a digital twin dedicated to the monitoring and optimisation of the LFW process. Linear Friction Welding (LFW) is a solid-state joining process with significant potential for assembling high-performance alloys that are difficult to weld using conventional fusion-based techniques. Since the materials do not reach their melting point, LFW limits solidification-related defects and helps preserve the intrinsic properties of advanced alloys. These advantages make the process particularly attractive to the aerospace industry, where structural lightweighting, mechanical performance, and joint reliability are crit