Shannon Entropy in Stochastic Analysis of Some Mems

This work is focused on the numerical determination of Shannon probabilistic entropy for MEMS devices exhibiting some uncertainty in their structural response. This entropy is a universal measure of statistical or stochastic disorder in static deformation or dynamic vibrations of engineering systems and is available for both continuous and discrete distributions functions of structural parameters. An interval algorithm using Monte Carlo simulation and polynomial structural response recovery has been implemented to demonstrate an uncertainty propagation of the forced vibrations in some small MEMS devices. A computational example includes stochastic nonlinear vibrations described by the Duffing equation calibrated for some micro-resonators, whose damping is adopted as a Gaussian, uniformly and triangularly distributed input uncertainty source

A highly efficient simulation technique for piezoelectric energy harvesters

This paper presents a new numerical technique which is aimed at obtaining fast and accurate simulations of piezoelectric beams, used in inertial energy harvesting MEMS. The execution of numerical analyses is greatly important in order to predict the actual behaviour of MEMS device and to carry out the optimization process on the basis of Design of Experiments (DOE) techniques. In this paper, a refined, yet simple, model is proposed with reference to the multi-physics problem of piezoelectric energy harvesting by means of laminate cantilevers. The proposed model is calibrated and validated with reference to 3D finite element analyses

Design and modeling of a periodic single-phase sandwich panel for acoustic insulation applications

Sandwich and composite panels are widely adopted in acoustic applications due to their sound insulation properties that overcome mass-law-based partitions in medium–high frequency regions. A key aspect in the design procedure of acoustic panels is the control of the resonance-dominated region of the sound transmission loss (STL) curve. Within that frequency range, such systems usually show acoustic weakness and poor insulation performances with respect to standard single-layer solutions. In the present contribution, we want to highlight an innovative approach to the sandwich partition concept. A novel single-phase sandwich panel is realized by adopting a periodic repetition of a properly designed unit cell. The resulting internal truss structure is self-sustained, and its mechanical stiffness can be tuned to maximize the STL in the resonance-dominated region. A set of parametric analyses is reported to show how the topology of the unit cell affects the noise reduction properties of the panel. Experimental validation is performed on a nylon 3D-printed prototype. The proposed panel is then integrated with some locally resonant elements that can be adopted to further improve the low-frequency STL of the solution. Industrial and production considerations are also taken into account during the design process to make the solution industrially valid with a circular economy focus

Numerical and experimental evaluation of the magnetic interaction for frequency up-conversion in piezoelectric vibration energy harvesters

The purpose of this work is to improve the modelling process for the application of permanent magnets in a frequency up-conversion (FuC) mechanism for piezoelectric energy harvesters. More specifically, the aim is to avoid the burdensome finite element analyses (FEA) in the framework of electromechanical devices design. The analytical calculations are compared with experimental tests conducted by an ad-hoc set up and with FEA. After investigations on the interaction, an application of FuC mechanism is proposed on a meso-scale case study in which a low frequency seismic mass (LFM) interacts non-linearly, due to magnetic field, with an high frequency piezoelectric vibration energy harvester (PVEH). Numerical simulations have been carried out in the time domain (step-by-step analysis) under a harmonic low-frequency input acceleration signal. The peculiar behavior, due to non-linear dynamics, is investigated in both the repulsive and the attractive configurations of the magnets. The results confirm the effectiveness of magnetic FuC and show that the repulsive case allows the device to recover a larger amount of energy than the attractive configuration

On the application of piezolaminated composites to diaphragm micropumps

This paper deals with the numerical simulation of piezolaminated microplates adopted as actuators in micropumps. The performances of piezoelectric actuation is critically assessed by means of comparisons with devices based on the electrostatic force

A top-down, three-scale numerical analysis of wafer-to-wafer metallic bonding

To study the sensitivity to micro-scale imperfections of the strength of a metallic, wafer-to-wafer MEMS bonding, we propose a three-scale numerical (finite element) approach. At the wafer level (macro-scale), accounting for the whole metallic sealing through nonlinear springs connecting the two silicon wafers modelled as thin plates, we link the force transferred by each single MEMS die to the external pressure applied to the wafers. This force is next used as an index for the input pressure at the die level (meso-scale), where the geometry of the metallic rings is accurately described: the local stress field at the interface between the upper and lower metallic rings is so obtained. Finally, a local (micro-scale) model is used to link the aforementioned local stress field in each die to the bonding strength: representative volumes of the rings getting into contact, accounting in a statistically way for the relevant surface roughness (which is on the order or tens of nanometers at most), are adopted to obtain the relationship between the external pressure and the percentage of sealed area. This information is exploited to assess the properties of the rings, in terms of expected bonding strength

An Experimental and Numerical Study on Glass Frit Wafer-to-Wafer Bonding

A thermo-mechanical wafer-to-wafer bonding process is studied through experiments on the glass frit material and thermo-mechanical numerical simulations to evaluate the effect of the residual stresses on the wafer warpage. To experimentally characterize the material, confocal laser profilometry and scanning electron microscopy for surface observation, energy dispersive X-ray spectroscopy for microstructural investigation, and nanoindentation and die shear tests for the evaluation of mechanical properties are used. An average effective Young’s modulus of 86.5 ± 9.5 GPa, a Poisson’s ratio of 0.19 ± 0.02, and a hardness of 5.26 ± 0.8 GPa were measured through nanoindentation for the glass frit material. The lowest nominal shear strength ranged 1.13 ÷ 1.58 MPa in the strain rate interval to 0.33 ÷ 4.99 × 10 (Formula presented.) s (Formula presented.). To validate the thermo-mechanical model, numerical results are compared with experimental measurements of the out-of-plane displacements at the wafer surface (i.e., warpage), showing acceptable agreement

Attention Mechanism-Driven Sensor Placement Strategy for Structural Health Monitoring

Automated vibration-based structural health monitoring (SHM) strategies have been recently proven to be promising in the presence of aging and material deterioration threatening the safety of civil structures. Within such a framework, ensuring high-quality and informative data is a critical aspect that is highly dependent on the deployment of the sensors in the network and on their capability to provide damage-sensitive features to be exploited. This paper presents a novel data-driven approach to the optimal sensor placement devised to identify sensor locations that maximize the information effectiveness for SHM purposes. The optimization of the sensor network is addressed by means of a deep neural network (DNN) equipped with an attention mechanism, a state-of-the-art technique in natural language processing (NLP) that is useful in focusing on a limited number of important components in the information stream. The trained attention mechanism eventually allows for quantifying the relevance of each sensor in terms of the so-called attention scores, thereby enabling to identify the most useful input channels to solve the relevant downstream SHM task. With reference to the damage localization task, framed here as a classification problem handling a set of predefined damage scenarios, the DNN is trained to locate damage on labeled data that had been simulated to emulate the effects of damage under different operational conditions. The capabilities of the proposed method are demonstrated by referring to an eight-story shear building, characterized by damage states possibly located at any story and of unknown severity