Scientific direction Development of key enabling technologies
Transfer of knowledge to industry

PhD : selection by topics

Technological challenges >> Factory of the future incl. robotics and non destructive testing
12 proposition(s).

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Situation awareness and task planning for a mobile manipulator in uncertain logistic environment

Département Intelligence Ambiante et Systèmes Interactifs (LIST)

Laboratoire de Contrôle et Supervision Robotique

01-03-2021

SL-DRT-21-0459

eric.lucet@cea.fr

Factory of the future incl. robotics and non destructive testing (.pdf)

The proposed research project concerns the autonomous evolution of a mobile robot in a logistic context, for example an automated forklift truck. Based on an analysis of the situation, the system will have to be able to autonomously find the sequence of actions allowing it to move towards an object of interest, grasp it and place it in a predefined region. This must be done while avoiding collisions and possibly clearing the path to make possible the movement of the mobile base and gripper. In case of failure, the replanning inherent in the method must be able to find a new sequence of actions. It will thus be necessary to implement a statistical model of the current and future navigation situation in the proximity of a mobile robot equipped with a gripper system, as well as a task planning algorithm based on this model. The Situation Awareness model will be based on contextual data from the perception modules, the process and task models, the agents (robots and humans) present in the environment and their states, the intrinsic data of the robot and the geometric model of the environment. The detection of particular situations (anomalies, etc.) can be handled by data analysis and automatic learning algorithms, possibly with a training phase based on feedback and a priori knowledge. In particular, the Hierarchical Planning in the Now (HPN) approach integrates task and motion planning and deals with uncertainty. It avoids trying to find optimal solutions for the POMDP (which is insoluble), by constructing a deterministic approximation of the dynamics (model of the situation), establishing a sequential plan, and executing this plan while observing possible changes in relation to the expected results, and re-planning it when deviations occur. In addition, to deal with uncertainty about the current state, planning must be done within the belief space, which is the probability of distributions over the states of the world. Thus, based on preliminary work on this subject, an improvement of the hierarchical plan, as well as a better understanding and formulation of models of the modification of belief states resulting from these actions, will be investigated.

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Shell finite elements for real time simulations in virtual reality

Département Intelligence Ambiante et Systèmes Interactifs (LIST)

Laboratoire de Simulation Interactive

01-09-2021

SL-DRT-21-0594

anders.thorin@cea.fr

Factory of the future incl. robotics and non destructive testing (.pdf)

With growing interest in interactive simulation comes the need to simulate shells (i.e. volumes with a thickness smaller than the two other dimensions) in real time. The objective of this thesis is to simulate the dynamics of thin shells in small strains, in the context of large displacements (geometric non-linearity) in real time. The work will be carried out in three parts, i) provide criteria to determine what can be simulated in real time or not (with a given number of degrees of freedom and a given precision), ii) identify or develop finite elements that are both robust to handle different use cases and efficient in terms of computational cost, iii) provide an implementation and examples of simulations that cannot be simulated in real time to date, with equal hardware.

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Optimization of acquisition parameters in measurement and X-ray imaging through simulation

Département Imagerie Simulation pour le Contrôle (LIST)

Laboratoire Simulation et Modélisation en Electro-magnétisme

01-10-2021

SL-DRT-21-0723

anthony.touron@cea.fr

Factory of the future incl. robotics and non destructive testing (.pdf)

The objective of the thesis is to set up a numerical method for optimizing acquisition parameters in X-ray control (XR). The DISC is developing a simulation software for Non Destructive Testing which integrates a module for X-ray imaging. This module, which corresponds to a digital twin of the testing, allows the operator to reproduce an experimental configuration (source, detector, geometry and material of the object, defects, ...) in order to estimate the result he would observe on a real acquisition and thus evaluate the sensitivity of the method, the limit of detectability of defects or the impact of different parameters (tension and filtration of the tube, acquisition geometry). To define these configurations, the operator has to rely on his own experience, which results in the realization of potentially non-optimal acquisitions. This limitation related to user dependency is particularly critical in cases where the signal sought is very weak, for example in the identification of a small quantity of element in X-ray fluorescence or for X-ray tomography which involves a large number of acquisition parameters. The objective is to make the experimental measurements more robust, by adding intelligence to the existing simulation tool, which would then integrate a functionality to optimize the acquisition parameters. The PhD student will have to propose a numerical method allowing to define, from a set of simulations, the parameters maximizing a given quality criterion. This criterion will be defined as a function of the targeted testing (typically the amplitude of the fluorescence peak or the transmission rate of the X-ray signal) and validated experimentally.

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Simulation-assisted imaging for SHM by elastic guided waves: tomography and shape derivative

Département Imagerie Simulation pour le Contrôle (LIST)

Laboratoire Méthodes CND

01-10-2021

SL-DRT-21-0738

tom.druet@cea.fr

Factory of the future incl. robotics and non destructive testing (.pdf)

Structural health monitoring (SHM) consists of monitoring the health of a structure using integrated sensors. In this context, the LIST is working on innovative elastic guided wave tomography methods to image corroded areas on simple geometries: plates and pipes. The subject of the thesis is to adapt to our dynamic problems the so-called "shape derivative" method, mostly used on static data, to develop iterative imaging based on a digital twin. This method allows a high quality reconstruction for complex geometries but implies the resolution of an expensive numerical problem. The quality of the image is also highly dependent of the initialization, often based on the perfect structure, potentially far from the current state. We propose to address these two points as follows: the initialization of the shape derivative will be obtained from existing tomography methods, thus improving convergence by limiting the number of iterations and the effect of local minima. The numerical problem will be solved using transient high-order spectral finite elements, allowing fast and inexpensive computations. The definition of metrics suitable for the comparison of simulated and experimental signals will also be part of the study. The performances will be validated on experimental data representative of complex cases of interest for the industry (corrosion under support, etc.).

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Deep learning based approach for sparse view Computed Tomography

Département Imagerie Simulation pour le Contrôle (LIST)

Laboratoire Simulation et Modélisation en Electro-magnétisme

01-10-2021

SL-DRT-21-0739

caroline.vienne@cea.fr

Factory of the future incl. robotics and non destructive testing (.pdf)

X-ray Computed Tomography (CT) is a well-established contactless and non-destructive inspection technique for various industrial parts. With its roots in the medical field, it evolved to industry as a key tool for three dimensional (3D) characterization and inspection. Industrial progress brings the need for verification of various objects on the production line and in some cases only the X-ray CT can fit the requirements. However, CT scans take a long time, often up to several hours, to achieve reasonable data quality. This long scanning time is an obstacle preventing integration of CT into production lines. In-line tomography implies strong constraints on the acquisition geometry, such as limited angle and sparse views configuration. Due to missing data, reconstructed images suffer from important artifacts that make the inspection task difficult. Many approaches have been investigated to reduce such artifacts. Among them, those based on deep learning seem promising. Convolutive neural network such as U-Net and generative adversarial networks will be studied for recovering missing projection data. The considered framework consists in creating a first version of the sinogram by projecting the initial reconstruction obtained by a FDK algorithm applied to the original incomplete projections and using the complete sinogram as target. Following the previous training of the neural network on simulated data, a second step will consist in using a transfer learning approach and experimental data sets to train a model able to deal with real data.

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Multi-architecture programming for high performance passive tomography reconstruction

Département Imagerie Simulation pour le Contrôle (LIST)

Laboratoire Développement Informatique

01-03-2021

SL-DRT-21-0745

hamza.chouh@cea.fr

Factory of the future incl. robotics and non destructive testing (.pdf)

Structural Health Monitoring is a set of non-destructive testing methods that aim to be directly integrated within the structures to control. This allows for continuous structural testing without disabling any equipment or involving additional human resources or testing hardware. Passive ultrasonic tomography uses structural noise of specimens to monitor their thickness. This method is dedicated to specimens that can be described as wave-guides. It is particularly useful to detect corrosion and erosion defects. The testing process implies dealing with large amount of numerical signals and applying various algorithms to them. In order to embed SHM controls into lightweight and low energy equipment, this thesis aims the development of a full passive ultrasonic tomography toolchain. This will require the evaluation of several hardware architectures (GPU, Low power GPU, FPGA) to find the best-suited ones. To this end, the student will produce high performance implementations of the tomography algorithms on the selected architectures and compare them with the performance obtained via a generic approach like Sycl. The development process should be flexible enough to provide maintainable and scalable software in order to ease the integration of future evolutions of the passive ultrasonic tomography method.

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Development of an X-ray phase imaging technique suitable for in-situ tomography of composite materials

Département Imagerie Simulation pour le Contrôle (LIST)

Laboratoire Instrumentation et Capteurs

01-10-2021

SL-DRT-21-0847

adrien.stolidi@cea.fr

Factory of the future incl. robotics and non destructive testing (.pdf)

Due to their high strength-to-weight ratios, composite materials are widely used in the aeronautic industry. Because of their structural heterogeneity, composite materials can be affected by complex damage that may appear either shortly during/after their manufacturing or later during their lifetime. However, conventional non-destructive techniques can provide sufficient accuracy to identify microscale flaws, such as matrix cracking and local fibre/matrix debonding, of geometrical dimensions of a few millimetres at the most. X-ray computed tomography (CT) is a powerful tool enabling to resolve microscale flaws and identify them in 3D dimension. However, low atomic number materials produce poor contrast, calling for innovating X-ray approaches such as phase contrast imaging, in demanding conditions for instance the in-line and time resolved monitoring of composite materials during stress fatigue. The goal of this thesis is to develop an innovating in-situ tomography technique based on phase shifting of the X-ray produice by the sample, in addition to the X-ray absorption. A first experience, showing promising results, was developed in the framework of Onera and CEA ? LIST collaboration, based on multilateral shearing interferometry (MSI), a phase measurement technique developed by Onera. The first part of this thesis work will be to reliable the experimental bench and developed adapted in-situ phase tomographic methods in order to compare them to classical attenuation tomographic approaches. The use of non-standard equipment such as robotic tomography platform (CEA Saclay & Nantes) will be part of this thesis work. Representative characterisations will be performed in agreement with the mains goals fixed by Onera specialists in aeronautics material. Time resolved X-ray phase contrast imaging will also be performed with mechanical constraints on sample(volume flaws of size of tens of microns or less). This part will be perform with Onera and CEA in close collaboration with LP3 laboratory (mixt research unit of Aix-Marseille university and CNRS), who as developed an laser-plasma X-ray source with a repetition rate of 100 Hz, based on intense pulsed laser beam. Preliminary experience will be perform in this installation. The second part of this work will be focus on the use of scattering information, encoded in the MSI measurement, in order to retrieved the fibres orientation of the composite sample. Previous work based on tensor tomography reconstruction can be found in the literature where directional information of the micro-structures of the sample can be link to the privileged scattering orientations, in function of the inspection energy used. The LP3 source (Molybdenum K-alpha source) will be used in order to work on the scattering responses at 17.48 keV. The correlation between the scattering signal and the fibres orientation distribution will be based on spherical deconvolution approach. This work will be mainly perform at Onera and CEA laboratories. A theoretical part, focus on interferometry technics and X-ray interaction, will be achieve. Then an important experimental part, with some numerical developments, will be carry on X-ray installations (CEA & LP3). These experimentals developments will lead to an important technological developments with a strong innovating part.

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Cyclophane-Loaded Plastic Scintillators for Neutron/Gamma Discrimination

Département Métrologie Instrumentation et Information (LIST)

Laboratoire Capteurs et Architectures Electroniques

01-10-2021

SL-DRT-21-0868

guillaume.bertrand@cea.fr

Factory of the future incl. robotics and non destructive testing (.pdf)

In the field of nuclear detection, neutron and gamma radiations are very hard to differentiate. Only a few formulations of plastic scintillators are able to perform neutron/gamma discrimination, using the phenomenon known as triplet-triplet annihilation (TTA). The CYCLOPS project aims at synthesizing poly-aromatic cyclophanes molecules, which can act as exciton antenna and promote TTA, and incorporating them in plastic scintillators. Our objective is to enhance the discrimination power of plastic scintillators with these new molecules. This proposal is built around encouraging preliminary results that lead to a patent. In order to capitalize on this, we organized the project in three parts: organic synthesis; scintillator manufacturing and testing; photophysical exploration. If successful, application may range from homeland security and non-proliferation to plant monitoring.

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High-speed distributed FBG sensing for SHM applications based on dispersive spectrometry

Département Métrologie Instrumentation et Information (LIST)

Laboratoire Capteurs Fibres Optiques

01-10-2021

SL-DRT-21-0887

sylvain.magne@cea.fr

Factory of the future incl. robotics and non destructive testing (.pdf)

Fiber Bragg Grating (FBG) sensing for structural health monitoring (SHM) is currently strongly limited in capacity and scan rate. Dispersive Bragg Spectrometry (DBS) is identified as an innovative approach to overcome these limitations (improving both capacity and scan rate) while maintaining cost-effectiveness. Until now, DBS is mostly applied to shock physics using high-bandwidth oscilloscopes. The principle is to use highly-dispersive optical media providing Bragg-to-time delay conversion of pulse light signals. As capacity is trading-off with speed, the same readout device may be tailored to every end-user demand. As an application of the DBS for aeronautics (collaboration with Safran), distributed strain measurements along bonded or soldered joints or within complex inaccessible structure parts may be performed with the help of Chirped Fiber Bragg Gratings (CFBG). Sensing lengths are currently 10-cm long, likely to increase in the future. The principle is to record the time-domain interferogram resulting from the interference between light reflected back by a sensing CFBG and by a reference one and to apply Fourier-Transform-based inverse calculation to retrieve the strain distribution along the CFBG. The PhD student will design the CFBG (on-going collaboration between CEA and University of Lille) and will participate in their characterization. Then, he will setup the experiment involving a pulse laser, a pair of CFBGs and high-bandwidth detector and oscilloscope. Preliminary experiments will be done in laboratory under controlled strain distribution and the inverse calculation will be performed and assessed. Meantime, a dedicated time detection electronic circuit will be provided for the project, as cheaper and simpler solution than oscilloscopes. This circuit will be evaluated with PZT actuators and possibly with laser ultrasonics techniques (collaboration with CNRS-PIMM). Finally, the DBS method will be tested in a representative situation, at Safran premises, on real composite parts.

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Study and application of a FBG-based neutron/gamma dosimetry in severe radiative environment, extension to monitoring within the RJH experimental reactor

Département Métrologie Instrumentation et Information (LIST)

Laboratoire Capteurs Fibres Optiques

01-10-2021

SL-DRT-21-0904

sylvain.magne@cea.fr

Factory of the future incl. robotics and non destructive testing (.pdf)

In-core and ex-core temperature and fluence cartographies are essential data for online monitoring of operational/incidental conditions of power and experimental reactors. High fluence level and gradients combined with lack of available space and access strongly limit the deployment of in-core sensing devices. The phenomena of Radiation-Induced Attenuation (RIA) in phosphosilicate fibers is already applied to distributed dosimetry (using FBG or OFDR monitoring techniques) at temperature lower than 70°C and dose lower than some kGy. For higher temperatures (e.g. 300°C, PWR), the RIA strongly depends on both time and temperature through the recovery process. Therefore, in-core RIA-based dosimetry is still challenging and no proof-of-concept is yet established. Industry already makes large use of Fibre Bragg Gratings (FBG, wavelength-multiplexed sensors) for temperature sensing. FBGs have also great potential for dose/fluence monitoring considering the Bragg Wavelength Shift (BWS) related to the radiation-induced change of the mean refractive index of silica. The work plan for the doctoral period will consist in designing a distributed gamma/neutron dosimeter based on a dedicated multicore fibre and testing it in several radiative environments. Reliable temperature compensation will be achieved by photowriting several FBGs in each core along the same fibre section, even in presence of high dose gradients. Furthermore, each core would exhibit complementary radiosensitivity in order to discriminate several radiation contributions. This photo-inscription scheme will be reproduced at several locations along the fibre to achieve a distributed dosimetry. This design will provide a distributed temperature monitoring as well, corrected for radiative influence. The candidate will work in collaboration with laboratory engineers in charge of femtosecond laser FBG photowriting. He will be supported by two scientific collaborations between CEA and two French universities, first the PhLAM lab. (University of Lille) dealing with special preform manufacturing, fiber drawing and characterization, and second the LabHC (University of Saint-Etienne), expert in radiative phenomena in fibres. Finally, this study is supported by the INSNU project of the CEA (DES/DPE/GEN23) that provides the technical framework and access to radiative facilities, partly through agreements with foreign institutes. The LDCI lab of the CEA (DES/IRESNE/DER/SPESI) will also participate into irradiation setups. Radiation tests on X-ray generator (LabHC) and within reactors (CEA/CABRI, JSI/TRIGA, SCK/BR2) are also planned.

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Path planning by deep reinforcement learning for optimal coverage of an industrial equipment

Département Intelligence Ambiante et Systèmes Interactifs (LIST)

Laboratoire de Simulation Interactive

01-10-2021

SL-DRT-21-1057

gilles.rougeron@cea.fr

Factory of the future incl. robotics and non destructive testing (.pdf)

In order to acquire 3D surveys of industrial sites or devices, mobile lidar scanners placed on autonomous vehicles or drones are commonly used. The goal of this thesis is to determine a priori, knowing a geometric model, the best path to follow to optimize the quality of the scan (by minimizing occlusions and noise, and obtaining a sufficient and homogeneous density of points), while avoiding static obstacles and minimizing the total distance traveled. To solve this problem of scan coverage path planning, we will use approaches based on deep reinforcement learning fed with simulations of potentially very complex environments. We will carry out quality comparisons of results compared with exhaustive paths, or paths resulting from meta heuristic optimization methods. Finally, we will search a way to quickly adapt the optimal path obtained a priori to the real, non-modelized, conditions of the site or industrial device to be scanned.

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Adaptive control of a load-bearing exoskeleton

Département Intelligence Ambiante et Systèmes Interactifs (LIST)

Laboratoire d'Architecture des Systèmes Robotiques

01-09-2021

SL-DRT-21-1066

franck.geffard@cea.fr

Factory of the future incl. robotics and non destructive testing (.pdf)

In recent years, there has been a lot of progress in the research on how to make robots collaborate with humans. However, one of the major difficulties that still blocks the massive use of these new technologies in industry for the execution of complex tasks is the lack of a satisfactory solution that allows the correct anticipation of the operator's intention. This need is all the more critical in the case of physical interaction on the whole of an operator's limb, as is often the case for exoskeletons, and in particular those providing effort assistance. The objective of this thesis is to develop and evaluate new learning and control laws to improve the usability and comfort of exoskeletons. To do this, different research ways will be considered. The first will consist of working on the progressive and intuitive learning of trajectories for the exoskeleton, by pursuing the work already carried out in the host laboratory, and by taking into account the characteristics of human movement as a criterion for optimising the comfort of the man/exoskeleton interaction. The second theme will concern the early detection, or even anticipation, of the user's motor intention, via the use of on-board sensors. In particular, we will study how to make the exoskeleton more transparent by exploiting predictive models of human arm trajectories. Finally, the problem of co-adaptation will also be addressed to design adaptive control laws. One of the underlying ideas here is, for example, to characterise the human/exoskeleton interaction in real time in order to try to assist it progressively and thus offer it a smoother and more comfortable assistance.

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