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Purpose: The aim of this paper is to investigate experimentally the effect of large vibration of a cantilever and a fully free rectangular plate made by a Fused Filament Fabrication process. Furthermore, this investigation attempts to compare our measurements and those obtained in the literature experimentally. Design/methodology/approach: For this purpose, a test rig was designed and manufactured for all experimental trials. The plate was excited randomly and harmonically at large displacement respectively, to obtain the linear and non-linear frequencies parameter. Findings: The non-linear dynamic behaviour of our structure at forced vibration is figured by exciting the plate at large displacement. The dependence of frequency and amplitude vibration are examined for the first, second, and third mode shapes. The non-linear dynamic behaviour of cantilever plates is compared with literature to illustrate the convergence of our results by using our specific mechanical properties, printing parameters, and process. Furthermore, the non-dimensional comparison is shown by 33.38%, 5.83%, and 20.58% for the first, second, and third mode shapes, respectively. Research limitations/implications: Experimental tests will be performed on a 3D-printed metal plate to improve the present work. Practical implications: This work is intended to determine the dynamic proprieties of our parts in order to manufacture a safe and comfort machine. Originality/value: Actually, the dynamic behaviour of our 3D printing plates is compared with the obtained in the case of the isotropic plate for the aim to predict the convergence of both structures. [ABSTRACT FROM AUTHOR]

This study focuses on the variations in the brain tissue dynamic behaviour pointing out new insight into the material nonlinear viscoelasticity. Shear dynamic response curves are obtained in different working conditions in terms of strain sweep and superimposed static compression offsets (SCO) applied in orthogonal direction to the shear. The strain sweep mode is used to study the storage and loss moduli dependence on the amplitude of the applied strain. It is found that the material exhibits linear viscoelastic behaviour up to about 0.1% strain amplitude. Above this critical threshold, the storage modulus G′ decreases rapidly with increasing dynamic strain amplitude and this effect is gradually intensified as the SCO are increased. In addition, it is observed that the loss factor (G"/G′) increases by increasing the SCO applied to the specimens. The dynamic strain amplitude results of the storage modulus reveal that the elastic component of the brain tissue's stiffness (G′) evaluated at low strain strongly increases with increasing static superimposed compression strain while the loss factor in the same strain range appears to be SCO independent. Finally, dynamic stiffness recovery after a large strain deformation is considered. The reduction in low amplitude dynamic modulus and subsequent recovery kinetics due to a perturbation is found to be independent of the level of the SCO. The same assessments were carried out on 5 consecutive strain sweep cycle loading. It has been noticed that at the last cycle, the dissipation peak is reduced, and the non-linearity of the curve begins earlier. This could be explained by the effects of cerebral edema on cells and their surrounding environment. [Display omitted] • Brain tissue shows Payne effect similarly to filled elastomers. • Payne effect could be related to proteins and cerebrospinal fluid rearrangement. • Storage modulus at low strain increases with increasing static offset. • Recovery kinetics could be considered pure-elastic and static offset independent. • On 5 strain sweep cycles the accumulation of intra -extracellular fluids is supposed. [ABSTRACT FROM AUTHOR]

Abstract: This paper introduces a new way to obtain the dynamical response of Euler–Bernoulli beams under large deflections. They are simulated with parsimonious models based on a two integrated neural networks. Our article proves that neural network based-reduced modelling allows mechanical simulations to benefit shorter time processing without a great loss of accuracy. Our model reduction technique ensures a good error-speed compromise. It rapidly leads to algebraic generic model matching interactive simulation or high-realistic multi-sensorial virtual protoyping requirements. The originality of our solution consists in the linking of two neural networks. They are integrated in a conditional loop. First, the article presents a systematic numerical technique that enables users to efficiently build an optimal neural network. This approach is based on the handling of discrete optimization evolutionary technique. Our optimization process is applied to the construction of the two neural networks. From an exhaustive learning base created from non-linear finite element analysis series, we secondly clearly describe our two neural network-based reduced models. The first neural network that enables nodal displacements to be determined from boundary conditions is linked to the second one that aims to set boundary conditions from a deformation state. The new relationship is finally analysed for justifying the successes of our proposal. The article completely details a new numerical process supervising the making of an integrated neural network loop being a new from of model for beam non-linear behaviour representation. [Copyright &y& Elsevier]