Publications

Multi-Point Incremental Forming (MPIF) process is a new hybrid process that combines two common manufacturing methods. These are Multipoint Forming (MPF) and Incremental Sheet Forming (ISF) processes. In this study, an experimental set-up, based on a MP reconfigurable die, was designed and manufactured to explore the flexibility of this innovative process and its potentialities to produce complex parts using the same tools. The obtained results have indicated that this novel technique, that doesn’t require costly equipments, is an effective approach to manufacture multitude of parts with different shapes. Moreover, it has been shown that the parts geometrical accuracy as well as thickness distribution are enhanced compared to the conventional ISF process and that the geometrical defects, called ‘dimples’ and caused by the pins’ ends, are significantly reduced and almost eliminated after the insertion of a rubber piece between the reconfigurable die and the blank sheet. On the other hand, the effect of the size and geometry of the rectangular pins on the geometrical accuracy and the dimpling defect has been studied using a finite element analysis.

Because of its excellent properties, such as good corrosion resistance, high specific strength and important ductility, the AA5383 aluminum alloy is largely employed for naval applications. In this work, the mechanical behavior of the AA5383 alloy at elevated temperatures, which is an important aspect for the control of forming operations, is investigated. For this purpose, an experimental campaign, including uniaxial tension, biaxial tension, and shear tests, is performed to cover an important range of temperatures (623∼723 K) and strain rates (10⁻⁴∼10⁻ⁱ s⁻ⁱ). A constitutive model for the description of the high temperature behavior of the AA5383 alloy is then proposed. For the deformation behavior, this model combines a viscoplastic flow rule with the BBC2003 anisotropic yield criterion. Also, the prediction of ductile fracture, which is an important aspect for formability, relies on an extended version of the modified Mohr-Coulomb criterion. The extension allows including the impact of temperature and strain rate on ductile fracture as well as a cut-off value for stress triaxiality. Finally, numerical simulations of the experimental tests are performed to identify the flow rule, yield criterion and fracture criterion parameters by combining different optimization methods. The numerical and experimental results of the different tests are in good agreement, which indicates that the proposed constitutive model is well suited for investigating the impact of process conditions on the formability of the AA5383 alloy at high temperatures.

This paper focuses on the impact of cryogenic assistance on the drilling of Ti6Al4V titanium alloy. It develops a relation between the phenomenon of hole shrinkage and measurements performed either during or after the machining operation. Indeed, because this phenomenon is apparently strongly associated with heat generation, which is the main issue in titanium alloy drilling, this work proposes to verify the effect of liquid nitrogen cooling on hole shrinkage, quantify it, and then relate it to these measurements. Specifically, the cutting forces and final hole geometry are analyzed and their variations are explained using the collected data on hole shrinkage.

Drilling operation with cryogenic assistance is beneficial toward solving critical issues in machining difficult-to-cut materials and structures, especially in terms of improving surface integrity, elongating tool life, sustainability, and so on for providing high-performance components in aerospace industries. This article presents an overview of the state of the art on this technique in recent years. It aims at analyzing its requirements and orient future directions. It starts with a summary concerning its application for different categories of work materials, including metals, composites, and hybrid stacks. Then, the main methodologies of numerical modeling and experimental characterization toward understanding the fundamentals are reviewed. The goal is to present a general view of current approaches, discuss their advantages, and disadvantages to understand the requirements toward future work. In addition, impacts of cryogenic drilling on cutting performance are reviewed in terms of thermomechanical loadings, surface integrity, tool wear, and sustainability. Finally, a brief summary is presented from different perspectives, and an outlook is recommended for future orientations.

A deep geological repository for radioactive waste, such as Andra’s Cigéo project, requires long-term (persistent) monitoring. To achieve this goal, data from a network of sensors are acquired. This network is subject to deterioration over time due to environmental effects (radioactivity, mechanical deterioration of the cell, etc.), and it is paramount to assess each sensor’s integrity and ensure data consistency to enable the precise monitoring of the facilities. Graph neural networks (GNNs) are suitable for detecting faulty sensors in complex networks because they accurately depict physical phenomena that occur in a system and take the sensor network’s local structure into consideration in the predictions. In this work, we leveraged the availability of the experimental data acquired in Andra’s Underground Research Laboratory (URL) to train a graph neural network for the assessment of data integrity. The experiment considered in this work emulated the thermal loading of a high-level waste (HLW) demonstrator cell (i.e., the heating of the containment cell by nuclear waste). Using real experiment data acquired in Andra’s URL in a deep geological layer was one of the novelties of this work. The used model was a GNN that inputted the temperature field from the sensors (at the current and past steps) and returned the state of each individual sensor, i.e., faulty or not. The other novelty of this work lay in the application of the GraphSAGE model which was modified with elements of the Graph Net framework to detect faulty sensors, with up to half of the sensors in the network being faulty at once. This proportion of faulty sensors was explained by the use of distributed sensors (optic fiber) and the environmental effects on the cell. The GNNs trained on the experimental data were ultimately compared against other standard classification methods (thresholding, artificial neural networks, etc.), which demonstrated their effectiveness in the assessment of data integrity.

Resolving Partial Differential Equations (PDEs) through numerical discretization methods like the Finite Element Method presents persistent challenges associated with computational complexity, despite achieving a satisfactory solution approximation. To surmount these computational hurdles, interpolation techniques are employed to precompute models offline, facilitating rapid online solutions within a metamodel. Probability distribution frameworks play a crucial role in data modeling across various fields such as physics, statistics, and machine learning. Optimal Transport (OT) has emerged as a robust approach for probability distribution interpolation due to its ability to account for spatial dependencies and continuity. However, interpolating in high-dimensional spaces encounters challenges stemming from the curse of dimensionality. The article offers insights into the application of OT, addressing associated challenges and proposing a novel methodology. This approach utilizes the distinctive arrangement of an ANOVA-based sampling to interpolate between more than two distributions using a step-by-step matching algorithm. Subsequently, the ANOVA-PGD method is employed to construct the metamodel, providing a comprehensive solution to address the complexities inherent in distribution interpolation.

The human brain is arguably the most complex “machine” to ever exist. Its detailed functioning is yet to be fully understood, let alone modelled. Neurological processes have logical signal-processing and biophysical aspects, and both affect the brain’s structure, functioning and adaptation. Mathematical approaches based on both information and graph theory have been extensively used in an attempt to approximate its biological functioning, along with Artificial Intelligence frameworks inspired by its logical functioning. In this article, an approach to model some aspects of the brain learning and signal processing is presented, mimicking the metastability and backpropagation found in the real brain while also accounting for neuroplasticity. Several simulations are carried out with this model to demonstrate how dynamic neuroplasticity, neural inhibition and neuron migration can reshape the brain’s logical connectivity to synchronise signal processing and obtain certain target latencies. This work showcases the importance of dynamic logical and biophysical remodelling in brain plasticity. Combining mathematical (agents, graph theory, topology and backpropagation) and biomedical ingredients (metastability, neuroplasticity and migration), these preliminary results prove complex brain phenomena can be reproduced—under pertinent simplifications—via affordable computations, which can be construed as a starting point for more ambitiously accurate simulations.

Machine learning approaches are currently used to understand or model complex physical systems. In general, a substantial number of samples must be collected to create a model with reliable results. However, collecting numerous data is often relatively time-consuming or expensive. Moreover, the problems of industrial interest tend to be more and more complex, and depend on a high number of parameters. High-dimensional problems intrinsically involve the need for large amounts of data through the curse of dimensionality. That is why new approaches based on smart sampling techniques have been investigated to minimize the number of samples to be given to train the model, such as active learning methods. Here, we propose a technique based on a combination of the Fisher information matrix and sparse proper generalized decomposition that enables the definition of a new active learning informativeness criterion in high dimensions. We provide examples proving the performances of this technique on a theoretical 5D polynomial function and on an industrial crash simulation application. The results prove that the proposed strategy outperforms the usual ones.

The aim of this article is to extend Moulinec and Suquet (1998)’s FFT-based method for heterogeneous elasticity to non-periodic Dirichlet/Neumann boundary conditions. The method is based on a decomposition of the displacement into a known term verifying the boundary conditions and a fluctuation term, with no contribution on the boundary, and described by appropriate sine–cosine series. A modified auxiliary problem involving a polarization tensor is solved within a Galerkin-based method, using an approximation space spanned by sine–cosine series. The elementary integrals emerging from the weak formulation of the equilibrium are approximated by discrete sine–cosine transforms, which makes the method relying on the numerical complexity of Fourier transforms. The method is finally assessed in several problems including kinematic uniform, static uniform and arbitrary Dirichlet/Neumann boundary conditions.

Trajectory optimization is a complex process that includes an infinite number of possibilities and combinations. This work focuses on a particular aspect of the trajectory optimization, related to the optimization of a velocity along a predefined path, with the aim of minimizing power consumption. To tackle the problem, a functional formulation and minimization strategy is developed, by means of the Euler-Lagrange equation. The minimization is later performed using a neural network approach. The strategy is deemed Lagrange-Net, as it is based on the minimization of the energy functional, by the means of Lagrange's equation and neural network approximations.