Henry Proudhon

Nanox: a miniature mechanical stress rig designed for near-field X-ray diffraction imaging techniques

A compact design for a miniature tensile stress rig, compatible with the space and weight constraints imposed by near-field diffraction imaging techniques, is presented. The device can carry tensile loads up to 500 N and is driven by a piezoelectric actuator which can work in a static and dynamic regime up to frequencies of 100 Hz.

Three-dimensional characterization of fatigue-relevant intermetallic particles in high-strength aluminium alloys using synchrotron X-ray nanotomography

Second-phase particles and small porosities are known to favour fatigue crack initiation in high-strength aluminium alloys 2050-T8 and 7050-T7451. Using high-resolution X-ray tomography (320 nm voxel size), with Paganin reconstruction algorithms, the probability that large clusters of particles contain porosities could be measured for the first time in 3D, as well as precise 3D size distributions. Additional holotomography imaging provided improved spatial resolution (50 nm voxel size), allowing to estimate the probability of finding cracked particles in the as-received material state. The extremely precise 3D shape (including cracks) as well as local chemistry of the particles has been determined. This experiment enabled unprecedented 3D identification of detrimental stress risers relevant for fatigue in as-received aluminium alloys.

Three-level multi-scale modeling of electrical contacts sensitivity study and experimental validation

An experimental and numerical study of electrical contact for low currents in sphere-plane set-up is presented. A three-level multi-scale model is proposed. We use the finite element analysis for macroscopic mechanical and electric simulations. It takes into account the setup geometry, elasto-plastic mechanical behavior of contacting components in the finite-strain-plasticity framework and electrostatic properties. A sensitivity analysis with respect to the brass plastic behavior and to the thickness of coating layers is also performed. The finite element results are used for an asperity-based model, which includes elasto-plastic deformation of asperities and their mutual elastic interactions. This model enables us to simulate the real morphology of contact spots at the roughness scale using the experimentally measured surface topography. Finally, the Greenwood multi-spot model is used to estimate the electrical contact resistance. This three-level model yields results which are in good agreement with experimental measurements carried out in this study.

Using machine learning and a data-driven approach to identify the small fatigue crack driving force in polycrystalline materials

The propagation of small cracks contributes to the majority of the fatigue lifetime for structural components. Despite significant interest, criteria for the growth of small cracks, in terms of the direction and speed of crack advancement, have not yet been determined. In this work, a new approach to identify the microstructurally small fatigue crack driving force is presented. Bayesian network and machine learning techniques are utilized to identify relevant micromechanical and microstructural variables that influence the direction and rate of the fatigue crack propagation. A multimodal dataset, combining results from a high-resolution 4D experiment of a small crack propagating in situ within a polycrystalline aggregate and crystal plasticity simulations, is used to provide training data. The relevant variables form the basis for analytical expressions thus representing the small crack driving force in terms of a direction and a rate equation. The ability of the proposed expressions to capture the observed experimental behavior is quantified and compared to the results directly from the Bayesian network and from fatigue metrics that are common in the literature. Results indicate that the direction of small crack propagation can be reliably predicted using the proposed analytical model and compares more favorably than other fatigue metrics.Crack propagation: machine learning identifies micromechanical variablesA machine learning technique can identify the complex variables behind the propagation direction of small cracks in a titanium alloy. A team led by Michael Sangid at Purdue University in the U.S.A built two separate Bayesian networks using machine learning to analyse diffraction and tomography data acquired during in situ fatigue cycling of a titanium alloy. The orientation of the first principal stress axis in a specific direction and the maximum resolved shear stress were the most strongly correlated with crack propagation, and were incorporated into an analytical relationship to describe the probability of the crack propagation direction. This analytical expression reproduced experimental results and was more reliable than previous literature predictions. This sort of semi-supervised machine learning methodology may help us identify driving forces in other complex engineering problems.

In vivo evaluation of the bone integration of coated poly(vinyl-alcohol) hydrogel fiber implants

Recently, it has been shown that constructs of poly(vinyl alcohol) (PVA) hydrogel fibers reproduce closely the tensile behavior of ligaments. However, the biological response to these systems has not been explored yet. Here, we report the first in vivo evaluation of these implants and focus on the integration in bone, using a rabbit model of bone tunnel healing. Implants consisted in bundles of PVA hydrogel fibers embedded in a PVA hydrogel matrix. Half of the samples were coated with a composite coating of hydroxyapatite (HA) particles embedded in PVA hydrogel. The biological integration was evaluated at 6 weeks using histology and micro-CT imaging. For all implants, a good biological tolerance and growth of new bone tissue are reported. All the implants were surrounded by a fibrous layer comparable to what was previously observed for poly(ethylene terephthalate) (PET) fibers currently used in humans for ligament reconstruction. An image analysis method is proposed to quantify the thickness of this fibrous capsule. Implants coated with HA were not significantly osteoconductive, which can be attributed to the slow dissolution of the selected hydroxyapatite. Overall, these results confirm the relevance of PVA hydrogel fibers for ligament reconstruction and adjustments are proposed to enhance its osseointegration.Graphical abstract