The École Nationale Supérieure de Mécanique et des Microtechniques (ENSMM) is a French school of engineering. It is part of Polyméca, a network of schools focusing on mechanical engineering.
The fracture mechanics properties and dynamic crack propagation in polyoxymethylene plates are examined in this study. Experiments are carried out in terms of three-point-bending of beams with an initial central crack and also tensile testing of compact-tension test specimens. The test specimens were taken from plates that had been manufactured by injection-moulding. Test specimens were taken in different orientations in the test plates in order to investigate the possible influence of anisotropy. The influence of crack sharpness and loading rate was also investigated. The mechanical fields around the initial, stationary crack were analysed using a rate-dependent plasticity model and the standard finite element method. Dynamic crack propagation was modelled using an AT1-type phase-field model. In the phase-field model, the bulk material was modelled as a linearly elastic material, and the surface energy was taken to be the equivalent surface energy that also includes possible dissipative processes in the surrounding bulk material. In both of the test specimens, dynamic crack propagation would initiate at a critical external load. In the three-point-bending tests, the crack would just propagate straight-ahead as a single crack, whereas in the compact-tension tests, the crack would branch into two main cracks after a short distance of single crack propagation. The crack branches made an angle of about 40 degrees to the initial crack plane, and the branched cracks reached speeds of up to 30% of the Rayleigh wave speed. The simulations suggested that the surface energy is much higher at quasi-static crack growth (about 3 kJ/m2) than during dynamic crack propagation (5-6 times lower). Also, the phase-field simulations were able to reproduce the crack branching observed in the CT specimens.
Topological insulators enable robust, unidirectional edge modes resistant to defects. Inspired by this, we present a topologically protected mechanical metamaterial exhibiting spin-like bifurcation-driven nonreciprocity for one-way elastic siliton-wave propagation. By leveraging angular momentum bias in a resonator lattice—analogous to magnetic bias in photonics—we break time-reversal symmetry, creating a mechanical analogue of magnetically biased graphene. Additionally, we explore topology-nonlinearity interplay using bistable networks with asymmetric energy landscapes, inducing nonlinear transition waves that act as mechanical diodes. Numerical simulations and experiments confirm the robustness and tunability of these states, enabling precise soliton-wave control. Our work advances phononic metamaterials for vibration isolation, energy harvesting, and nonreciprocal waveguiding. In addition, we will discuss how active solicitation can drastically change the landscape of mechanical metamaterials.
Due to the complicated nature of Parkinson disease (PD), a number of subjective considerations (eg, staging schemes, clinical assessment tools, or questionnaires) on how best to assess clinical deficits and monitor clinical progression have been published; however, none of these considerations include a comprehensive, objective assessment of all functional areas of neurocognition affected by PD (eg, motor, memory, speech, language, executive function, autonomic function, sensory function, behavior, and sleep). This paper highlights the increasing use of digital health technology (eg, smartphones, tablets, and wearable devices) for the classification, staging, and monitoring of PD. Furthermore, this Viewpoint proposes a foundation for a new staging schema that builds from multiple clinically implemented scales (eg, Hoehn and Yahr Scale and Berg Balance Scale) for ease and homogeneity, while also implementing digital health technology to expand current staging protocols. This proposed staging system foundation aims to provide an objective, symptom-specific assessment of all functional areas of neurocognition via inherent device capabilities (eg, device sensors and human-device interactions). As individuals with PD may manifest different symptoms at different times across the spectrum of neurocognition, the modernization of assessments that include objective, symptom-specific monitoring is imperative for providing personalized medicine and maintaining individual quality of life.
This paper presents a new framework for stochastic updating of a finite element model for a composite plate, considering the influence of temperature on Lamb wave propagation. The framework involves deterministic updating to optimize mechanical properties and stochastic updating to derive probability density functions for key parameters. It utilizes sensitivity analysis and Bayesian inference with Markov-Chain Monte Carlo simulations and the Metropolis–Hastings sampling algorithm. This paper proposes a machine learning surrogate model based on artificial neural networks to improve computational efficiency. This surrogate modeling approach allows parallelized Monte Carlo simulations, reducing updating time significantly without compromising the accuracy of the resulting probability density functions for model parameters. These advancements show a promising way to enhance composite plate modeling and Lamb wave propagation studies, providing a more efficient and accurate approach to verify and validate finite element models with potential applications in engineering simulations.
