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PhD Thesis (M/W): "Search for resilient microtrusses inspired by analogues in cristalline plasticity"

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Date Limite Candidature : vendredi 8 décembre 2023

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Informations générales

Intitulé de l'offre : PhD Thesis (M/W): "Search for resilient microtrusses inspired by analogues in cristalline plasticity" (H/F)
Référence : UMR7239-STEBER-003
Nombre de Postes : 1
Lieu de travail : METZ
Date de publication : vendredi 17 novembre 2023
Type de contrat : CDD Doctorant/Contrat doctoral
Durée du contrat : 36 mois
Date de début de la thèse : 1 mars 2024
Quotité de travail : Temps complet
Rémunération : 2 135,00 € gross monthly
Section(s) CN : Material and structural engineering, solid mechanics, biomechanics, acoustics

Description du sujet de thèse

Thesis background: Architected materials derived from innovative additive manufacturing processes, consisting of three-dimensional arrangements of periodic arrays of microtrusses connected to each other by nodes, form so-called "lattice" structures that are very good candidates for lightweight structures. The elastic moduli and Poisson's ratios of these structures can be optimized by adjusting the arrangement of the periodic lattice [1]. However, beyond their elastic behavior, their mechanical behavior generally becomes unstable, with a sudden drop in stress due to the early appearance of localization bands, leading to structural collapse at relatively low strain levels. Recently, initial work published internationally [2,3] has proposed delaying this generalized collapse by means of "meta-structures" made up of several lattice arrangements. An interesting analogy is made in [3] between crystallographic slip/grain boundary interactions and collapse band interactions in lattice structures/interfaces between different lattice orientations. In particular, the presence of these interfaces delays the overall collapse of the structure. However, the analogy remains highly phenomenological.

In the ANR-funded MIRACLES project (2024-2028), we propose to go beyond simple phenomenological analogy, building on the initial idea of [2,3]. We will draw on the knowledge gained from crystal plasticity calculations in LEM3 [4,5], to study the effects of grain boundaries on the mechanical behavior of bi-crystals and poly-crystals. We will not go into the search for analogies in terms of physical mechanisms, which are quite different. We propose to use the concepts of bi-crystallography commonly used in the "grain boundary" community (coincidence networks, intergranular misorientations, etc.) to guide, by geometric analogy, the fabrication of new "bi-crystalline" or "oligo-crystalline" architectural structures with "meta-grains" corresponding to an optimized distribution of lattice orientations and interfaces between these "meta-grains". This methodology will be developed jointly by LEM3 and PIMM to determine meta-grain boundary configurations, with optimized transferable mechanical properties to limit the propagation of collapse bands. In order to manage the topological optimization of connectivities at meta-grain interfaces, artificial intelligence methods (AI) developed at PIMM will be used [6,7]. The project therefore proposes to develop new "meta-grain" architected materials inspired by analogues in bicrystalline and polycrystalline plasticity, more tolerant to collapse-band damage. These new architected structures and will be fabricated and experimentally studied at SIMAP [8,9].

Thesis program: In order to propose transfers of crystalline plasticity to "Bicristallo-inspired" micro-lattices, a combination of optimization methods using a genetic algorithm (GA) and prediction methods using AI with neural network algorithms [6] (graphical method based on GNN-type neural networks), will be proposed in conjunction with PIMM to optimize the flow stress and predict the mechanical properties of bi-crystals (with CSL or random joints) and oligo-crystals (up to 8 orientations). The physical model that will generate the database is a mesoscopic crystal plasticity approach mastered at LEM3 [4,5]. Important output parameters will be studied, such as the flow stress and the slip transmission parameter. The study of these quantities will enable an analogy and transfer of geometrical concepts from crystalline plasticity and bi-crystallography (cf. Figure) to "lattice" structures with meta-grains, in order to optimize the disorientations and structures of the interfaces between meta-grains. Optimized bi- and oligocrystalline configurations will be transferred for the design and finite element simulations of bicrystalline-inspired microtrusses, to identify potential candidates for such structures with optimized behavior, to be produced by additive manufacturing with a view to their experimental characterization. The approach will also involve proposing transfers of optimized bicrystalline and oligocrystalline plasticity configurations and investigating the feasibility of additive manufacturing such structures. We will use a design of experiment emanating from an AI approach based on Topology Data Analysis ("TDA": Topology Data Analysis [7]) to study connectivities at the interfaces of meta-grains, and we will rely on a GNN-based graphical method [6] as model reduction. Local generic dimensioning criteria based on geometric parameters (connectivity, slenderness, relative angle, etc.) will be studied, and these results will be compared with the criteria for blocking slip at grain boundaries.

References :
[1] Albertini, F., Dirrenberger, J., Molotnikov, A., & Sollogoub, C. (2019). J. Applied Mechanics, 86(11), 111003
[2] Pham, M.S., Liu, C., Todd, I, Lertthanasarn, J. (2019). Nature 565, 305-311.
[3] Liu, C., Lertthanasarn, J., Pham, M.S. (2021) Nature Communications 12, 4600.
[4] Richeton, T., Tiba, I., Berbenni, S., & Bouaziz, O. (2015). Philosophical Magazine 95, 12-31
[5] Berbenni, S., Taupin, V. & Lebensohn, R.A. (2020). Journal of the Mechanics and Physics of Solids 135, 103808.
[6] Hernández, Q., Badías, A., Chinesta, F., Cueto, E. (2022) Thermodynamics-informed graph neural networks.
[7] Frahi, T., Falco, A., Vinh Mau, B., Duval, J.L., & Chinesta, F. (2021) Empowering Advanced Parametric Modes Clustering from Topological Data Analysis, Applied Sciences, MDPI 11, 6554
[8] Plancher, E., Suard, M., Dendievel, et al.. (2020) Materials Letters, 282, 128669.
[9] Suard, M., Plancher, M., Martin, G., Dendievel, R., Lhuissier P. (2020). Ad. Eng. Mater., 2000315.

Contexte de travail

Scientific Environment: The thesis will be carried out in two laboratories: LEM3 (CNRS / Université Lorraine / Arts et Métiers) (S. Berbenni (Resp.), T. Richeton, O. Bouaziz) will be in charge of WP1: Development of geometrical analogues between bi-crystallography and bicrystalline and oligocrystalline microtrusses with meta-grains. Analytical and numerical simulations of crystal plasticity, property optimizations by genetic algorithm (GA). PIMM (CNRS / Arts et Métiers / CNAM) (J. Dirrenberger (Resp.), F. Chinesta, C. Gnathios, N. Hascoet) will be in charge of WP2 and AI methods related to the project: Design methodologies of architected materials obtained by additive manufacturing, full-field numerical simulation, modeling. AI expertise to develop TDA, GNN methods.

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