Projection-based in-situ 4D mechanical testing

Abstract : The quantitative analysis of 3D volumes obtained from tomography allows models to be identified and validated. It consists of a sequence of three successive inverse problems: (i) volume reconstruction (ii) kinematic measurement from Digital Volume Correlation (DVC) and (iii) identification. The required very long acquisition times prevent fast phenomena from being captured.A measurement method, called Projection-based DVC (P-DVC), shortens the previous sequence and identifies the kinematics directly from the projections. The number of radiographs needed for tracking the time evolution of the test is thereby reduced from 500 to 1000 down to 2.This thesis extends this projection-based approach to further reduce the required data, letting faster phenomena be captured and pushing the limits of time resolution. Two main axes were developed:- On the one hand, the use of different spatial and temporal regularizations of the 4D fields (space/time) generalizes the P-DVC approach (with a known reference volume) to the exploitation of a single radiograph per loading step. Thus, the test can be carried out with no interruptions, in a few minutes instead of several days.- On the other hand, the measured motion can be used to correct the reconstructed volume itself. This observation leads to the proposition of a novel procedure for the joint determination of the volume and its kinematics (without prior knowledge) opening up new perspectives for material and medical imaging where sometimes motion cannot be interrupted.end{itemize}The development of these two axes opens up new ways of performing tests, faster and driven to the identification of key quantities of interest. These methods are compatible with the recent ``hardware" developments of fast tomography, both at synchrotron beamlines or laboratory and save several orders of magnitude in acquisition time and radiation dose.
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Clément Jailin. Projection-based in-situ 4D mechanical testing. Mechanics of the solides [physics.class-ph]. Université Paris-Saclay, 2018. English. ⟨NNT : 2018SACLN034⟩. ⟨tel-01927948⟩

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