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

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 critical requirements.

Nevertheless, the physical phenomena governing LFW remain particularly complex. Heat is generated through friction and severe plastic deformation, while the resulting temperature increase strongly affects the material behaviour, contact conditions, interfacial friction, material flow, and bonding mechanisms. These phenomena are highly coupled and evolve rapidly during the different stages of the process. Moreover, the final weld quality depends not only on macroscopic variables, such as temperature, stress, strain, flash formation, and applied force, but also on microstructural transformations occurring at and around the interface. An inadequate combination of operating parameters may therefore lead to insufficient bonding, voids, cracks, or undesirable microstructural changes.

Experimental investigations provide essential information for understanding these mechanisms. However, the high cost of testing, the short duration of the process, and the difficulty of accessing the welding interface during operation limit the amount of information that can be obtained experimentally. Furthermore, extensive experimental campaigns are generally required for each new material and welding configuration. High-fidelity numerical models can complement these investigations by predicting the thermomechanical history, material flow, contact behaviour, and microstructure evolution. However, their computational cost remains incompatible with real-time applications.

Real-time simulation is an essential requirement for the future monitoring, optimisation, and control of LFW. It would make it possible to estimate physical quantities that cannot be measured directly, detect deviations from nominal process conditions, and predict potential defects before the end of the welding operation. Achieving this objective requires reduced-order or surrogate models that preserve the relevant physical behaviour while considerably reducing computation time.

Artificial neural networks provide rapid predictions but generally require large, representative datasets and may produce physically inconsistent results outside their training domain. Physics-Informed Neural Networks (PINNs) offer a promising alternative by incorporating governing equations, boundary conditions, and physical constraints into the learning process. They can also combine heterogeneous information obtained from numerical simulations, experimental measurements, and expert knowledge. Nevertheless, their application to LFW raises several scientific challenges, including the representation of strongly coupled and highly nonlinear thermomechanical phenomena, moving and evolving interfaces, complex contact conditions, multiscale behaviour, and rapid transient events. Their convergence, accuracy, robustness, and computational efficiency must also be demonstrated under process-relevant conditions.

In this context, the present PhD project will focus on developing real-time surrogate models for LFW based on physics-informed machine-learning approaches and multiscale experimental data. The models will aim to reproduce the main thermomechanical and microstructural responses of the process, from the welding interface to the machine scale, and to establish relationships between operating parameters, physical phenomena, and defect formation. Particular attention will be paid to contact and bonding conditions, material flow, temperature and stress fields, and microstructure evolution.

The project does not aim to develop a complete digital twin or its closed-loop decision-making and control architecture. Instead, it will address one of its most critical scientific components: a fast, physically consistent, and experimentally validated computational model capable of providing real-time predictions. This framework will ultimately constitute a computational building block for future digital-twin applications dedicated to the monitoring, optimisation, and control of Linear Friction Welding.

Créé en 1989, le Laboratoire Génie de Production (LGP) est, depuis novembre 2023, un laboratoire de l'Université de Technologie Tarbes Occitanie Pyrénées (UTTOP). À ce titre, il est intégré à l’Université de Toulouse et mutualise un certain nombre d'actions avec d'autres établissements et laboratoires, nationaux et internationaux.
Le LGP est un laboratoire pluridisciplinaire qui développe des activités de recherche autour des matériaux, de la mécanique, de l'automatique, de l'informatique, du génie électrique, de la robotique et des sciences et techniques de production dans le champ des Sciences et de l'Ingénierie des Systèmes. Sa mission est de conduire et de développer des recherches dans les domaines relatifs à la formation des ingénieurs généralistes et de favoriser le lien entre formation, recherche et transfert technologique.
Les recherches sont menées le plus souvent en lien étroit avec des problématiques réelles du monde socio‐économique dans le cadre des trois enjeux sociétaux de l'Université de Technologie de Tarbes.
Le LGP s’appuie sur des équipements remarquables, cohérents avec le besoin des entreprises et le profil des ingénieurs formés sur le site tarbais.
Il est organisé autour  de deux départements scientifiques « Mécanique-Matériaux-Procédés » et « Systèmes » regroupant l’ensemble des enseignants-chercheurs et chercheurs, et de deux axes transverses « Procédés Additifs Intelligents, de la Matière au Système » et « Jumeau Numérique », fédérant les activités autour de la fabrication additive, de l’intelligence artificielle, de la simulation et des jumeaux numériques.

• Département Scientifique "Mécanique-Matériaux-Procédés"
• Département Scientifique "Systèmes"
• Axes transverses PAMS (Procédés Additifs Intelligents, de la Matière au Système) et JN (Jumeau Numérique),

Le candidat ou la candidate devra être titulaire d’un master ou d’un diplôme d’ingénieur en mécanique, calcul scientifique ou science des matériaux. Des compétences en modélisation par éléments finis, thermomécanique et programmation Python sont attendues. Une expérience avec Abaqus et en intelligence artificielle ou PINNs serait appréciée. Rigueur, autonomie, aptitude au travail en équipe et maîtrise de l’anglais sont requises.

Niveau d'anglais requis: Intermédiaire supérieur: Vous pouvez utiliser la langue de manière efficace et vous exprimer précisément.