Publications

The mechanical properties of structural timber largely depend on the occurrence of knots and on fibre deviation in their vicinities. In recent strength grading machines, lasers and cameras are used to detect surface characteristics such as the size and position of knots and local fibre orientation. Since laser dot scanning only gives reliable information about the fibre orientation in the plane of board surfaces, simple assumptions are usually made to define the inner fibre orientation to model timber boards. Those models would be improved by better insight into real fibre deviation around knots. In the present work, a laboratory method is developed to evaluate growth layers geometries and fibre orientation, solely based on the fact that the fibers are parallel to the tree rings and without any further assumptions. The method simply relies on color scans and laser dot scans of Douglas fir (Pseudotsuga menziesii) timber specimen sections revealed by successive planing. The proposed method provides data on fibre orientation in 3D with an accuracy that is relevant for the calibration of detailed models.

This paper describes an innovative method developed by the authors to support basic decisions concerning the structural restoration of a large historical panel painting which had been damaged by inappropriate attachment to a wall and ongoing exposure to severe changes in environmental humidity. The Lapidazione di Santo Stefano is a large panel (2.78 × 3.92 m2) painted by Giorgio Vasari in 1571 and has been housed since then in the Church of Santo Stefano dei Cavalieri in Pisa (Italy). Its wooden support is made of large horizontal planks glued together along their edges and stiffened by vertical, dovetailed crossbeams. The panel was tightly fastened to a church wall with several rigid bolts; due to the moisture cycling produced by rainwater leakage and a subsequent “compression set”, it had developed severe tension stresses perpendicular to the grain, resulting in cracks affecting both the wood and the paint layers. To decide how to carry out the structural restoration of the panel, it was necessary to know whether slippage could occur between the panel and crossbeams during seasonal variations in environmental humidity. Without slippage, tensile stresses would be generated in the wood and could produce further cracks and damage the paint layers. An in situ monitoring method for assessing the possibility of slippage was developed and implemented. An analysis of data collected over a period of 6 months before the structural restoration confirmed that adequate slippage was possible; hence, the decision to fully repair the cracks was taken. Monitoring continued for a year after restoration and confirmed the previous findings. This paper describes the monitoring method, the equipment used, the results of its implementation and its value as a preventive conservation tool.

Automatic control of a workpiece being manufactured is a requirement to ensure in-line correction and thus move towards a more intelligent manufacturing system. There is therefore a need to develop control strategies which are capable of taking precise account of real working conditions and enabling first-time-right control. As part of such a smart-control strategy, this paper introduces a machine learning-based approach capable of accurately predicting a priori the 3D coverage of a part according to a scan configuration given as input, i.e. predicting before scanning it which areas of the part will be acquired for real. This corresponds to a paradigm shift, where coverage estimation no longer relies on theoretical visibility criteria, but on rules learned from a large amount of data acquired in real-life conditions. The proposed 3D Scan Coverage Prediction Network (3DSCP-Net) is based on a 3D feature encoding and decoding module, which is capable of taking into account the specifics of the scan configuration whose impact on the 3D coverage is to be predicted. To take account of real working conditions, features are extracted at various levels, including geometric ones, but also features characterising the way structured-light projection behaves. The method is thus able to incorporate inter-reflection and overexposure issues into the prediction process. The database used for the training was built using an ad-hoc platform specially designed to enable the automatic acquisition and labelling of numerous point clouds from a wide variety of scan configurations. Experiments on several parts show that the method can efficiently predict the scan coverage, and that it outperforms conventional approaches based on purely theoretical visibility criteria.

Complex mechanical parts with high geometry and dimensional accuracy require multi-axis machining with low volumetric errors. To achieve such precision in the micrometer range, the geometric errors of the machine tool must be correctly identified and compensated. This article proposes a new methodology to measure directly the geometric errors of the rotary axes on machine tool. The methodology consists of a system of several non-contact sensors that are strategically placed around a datum cylinder. This system is held on the machine spindle and remains static over the entire measurement. The relative motion between the sensors and the cylinder is obtained by rotating the table of the multi-axis machine tool. The cylinder could be fast centered and leveled by using micro linear stages. A rotation stage enables to decouple the rotary axis motion from the datum cylinder rotation, consequently carry out methodologies of errors separation associated with multiprobe and multistep methods.

The dimensional accuracy of machined parts can be influenced by numerous factors, among which inaccuracies in the machine’s structural loop and thermal expansion of components have the biggest impact. Hence, highly accurate machining requires effective error compensation. This motivates the development of a real-time compensation system implemented on a five-axis machine tool. In this study, a physical monitoring device is installed in the feedback loops of the machine’s axial position control circuit, to intercept and modify linear encoder signals. It communicates with a custom software application that processes the data and generates corrected signals according to geometric model based on the rigid body assumption. The numerical controller (NC) is then induced to perform volumetric error correction based on its default programming. The key advantage of this software-based compensation strategy over the use of look-up tables or NC program modification is the total independence from the NC. The same real-time program is also used for the characterization of linear axis controls. This article outlines the behaviour of an NC machine when an axis displacement is generated via the modification of measuring systems feedback. The rate of change of the virtually added movement appears to be more of a limiting factor to the controller than the magnitude. Moreover,

Liquid nanofilms are ubiquitous in nature and technology, and their equilibrium and out-of-equilibrium dynamics are key to a multitude of phenomena and processes. We numerically study the evolution and rupture of viscous nanometric films, incorporating the effects of surface tension, van der Waals forces, thermal fluctuations, and viscous shear. We show that thermal fluctuations create perturbations that can trigger film rupture, but they do not significantly affect the growth rate of the perturbations. The film rupture time can be predicted from a linear stability analysis of the governing thin film equation, by considering the most unstable wavelength and the thermal roughness. Furthermore, applying a sufficiently large unidirectional shear can stabilize large perturbations, creating a finite-amplitude traveling wave instead of film rupture. In three dimensions, unidirectional shear does not inhibit rupture, as perturbations are not suppressed in the direction perpendicular to the applied shear. However, if the direction of shear varies in time, then the growth of large perturbations is prevented in all directions, and rupture can be impeded. © 2024 American Physical Society.

This work deals with a numerical investigation of the onset of inelastic instability in cruciform columns using the limit-point method. In this aim, a nonlinear buckling analysis was developed to determine the limit-point stress and the structure response during the post-buckling stage. Both total deformation and flow theories are used to describe the mechanical behavior. The numerical simulations were carried out considering cruciform columns with different material and geometric parameters. The obtained results were compared with experimental data from existing literature focused on the influence of the plasticity theory. The influences of the slenderness ratio and material parameters are discussed.

Recent advances have prominently highlighted physics informed neural networks (PINNs) as an efficient methodology for solving partial differential equations (PDEs). The present paper proposes a proof of concept exploring the use of PINNs as an alternative to finite element (FE) solvers in both classical and gradient-enhanced solid mechanics. To this end, spatio-temporal PINNs are designed to represent continuous solutions of boundary value problems within spatio-temporal space. These PINNs directly incorporate the equilibrium and constitutive equations in their differential and rate forms, bypassing the requirement for incremental implementation. This simplifies application of PINNs to solve complex mechanical problems, particularly those involved in the context of gradient-enhanced continua. Moreover, traditional meshing is no longer required as it is replaced by a point cloud, making it possible to overcome meshing drawbacks. The results of this investigation prove the effectiveness of the proposed methodology, especially with regards to non-monotonic loading conditions and irreversible plastic deformation. Compared to classical FE approaches, the proposed spatio-temporal PINNs are more readily applied to complex problems, which are tackled in their raw form. This is especially true for gradient-enhanced continuum problems, where there is no need to introduce additional degrees of freedom as in classical FE approaches. However, PINNs training generally requires more computation time, a challenge that can be mitigated by employing the concept of transfer learning as shown in this paper. This concept, which is very useful when performing parametric studies, involves applying knowledge grained from solving one problem to another different but related one. The use of PINNs as mechanical solvers is shown to be highly promising in the forthcoming era, where advancements in GPU technology can further enhance their performance in terms of computation time.

Pressure Ulcer (PU) prevention remains a main public health issue. The physiopathology of this injury is not fully understood, and satisfactory ther-apy is currently not available. A better assessment of the internal behavior could allow to enhance the modeling of the transmission of loads into the different structures composing the heel. Computational modeling has shown promise in developing a better understanding of the biomechanical response of soft tissue in areas of high risk for PU such as the heel. An MRI-derived patient-specific FE model was developed to investigate heel soft tissue response and the impact of material properties on the tissue displacement and strain. Biplanar radiographs, ultrasound acquisitions, and pressure measurements were collected to inform the development of the model and verify its results. The FE simulations demon-strated variability in compressive strain values and localization in the tissue which informs the conclusions drawn from such models. Further work must be undertaken in characterizing these tissues in the use of such models such that their performance can be beneficial in a clinical setting.

Clinical management of cancer has continuously evolved for several decades. Biochemical, molecular, and genomics approaches have brought and still bring numerous insights into cancerous diseases. It is now accepted that some phenomena, allowed by favorable biological conditions, emerge via mechanical signaling at the cellular scale and via mechanical forces at the macroscale. Mechanical phenomena in cancer have been studied in-depth over the last decades, and their clinical applications are starting to be understood. If numerous models and experimental setups have been proposed, only a few have led to clinical applications. The objective of this contribution is to review a large scope of mechanical findings which have consequences on the clinical management of cancer. This review is mainly addressed to doctoral candidates in mechanics and applied mathematics who are faced with the challenge of the mechanics-based modeling of cancer with the aim of clinical applications. We show that the collaboration of the biological and mechanical approaches has led to promising advances in terms of modeling, experimental design, and therapeutic targets. Additionally, a specific focus is placed on imaging-informed mechanics-based models, which we believe can further the development of new therapeutic targets and the advent of personalized medicine. We study in detail several successful workflows on patient-specific targeted therapies based on mechanistic modeling.