The verification phase (Final Test) of inertial MEMS gyroscopes design properties takes a basic role in the sensors production. It's during this phase that devices are tested to reveal their effectiveness in the survey of the angular rate. The verification phase is based on a set of several tests able to evaluate the gyroscope characteristic properties, such as the resonance frequency, the quality factor, the quadrature error, and the gyroscope sensitivity. The main goal of the Final Test is to identify characteristic parameters as far as possible in order to save money: actually each gyroscope has to be tested before putting it on the market. The test bench used during the Final Test phase will be proposed as well as a new experimental procedure able to fast identify characteristic parameters. This new testing procedure has been applied both for single-axis and double-axis gyroscopes. The results achieved has been compared with a more accurate (but slower) experimental procedure.

Experimental testing components, innovative solutions and control strategies is essential in order to increase performances, optimize efficiency and ensure a proper safety level of a railway vehicle. These tests are usually performed directly on line, thus being very expansive. In order to reduce costs and save time during the testing/analysis phase, the use of dedicated test rigs is increasing. A roller rig for testing full scale locomotives is considered in this paper. In order to investigate the influence of the roller rig dynamics on the test bench behavior in a design stage, a numerical model of the full system (including the locomotive, the roller rig and the corresponding control systems) has been developed and a parametric analysis has been carried out.

The mechanical modelling of MEMS requires the determination of the inertia, of the damping and of the stiffness of the various elements that constitute the device. Although some parameters seem easy to be determined (e.g., the inertial parameters), at working frequencies typical of MEMS inertial sensors some elements, such as supporting beams, not only contribute to the elasticity of the system but also to its inertia. For what concerns damping, two main pressure levels have to be considered: atmospheric pressure level (from now on called "high pressure," i.e., 10(5) Pa) and vacuum (from now on called "low pressure," i.e., 26 Pa). At high pressure the mean free path of an air molecule is much smaller than typical MEMS dimensions. Thus, air can be considered as a viscous fluid and two phenomena occur: flow damping and squeeze film damping. These two terms can be evaluated through a simplified Navier-Stokes equation. In vacuum the air cannot be considered as a viscous fluid any more since the mean free path of an air molecule is of the same order of magnitude of typical MEMS dimensions. Thus, the molecular fluid theory must be used to estimate the damping. The present paper shows an approach to pass from a complex FEA model to a lumped parameter model of the considered MEMS inertial sensor at both ambient and low pressure levels that can easily be used during the design or optimisation phases. Although developed and validated for a specific MEMS inertial sensor, the proposed approach is fully general and could be used for any other MEMS device.

Except for MEMS working in a ultra high vacuum, the main cause of damping is the air surrounding the system. When the working pressure is equal to the atmospheric one (from now on called “high pressure,” i.e., 105 Pa), the mean free path of an air molecule is much smaller than typical MEMS dimensions. Thus, air can be considered as a viscous fluid and two phenomena occur: flow damping and squeeze film damping. These two phenomena can be evaluated through a simplified Navier–Stokes equation. In a medium vacuum (from now on called “low pressure”), i.e., the “packaging” pressure, the air cannot be considered as a viscous fluid any more since the mean free path of an air molecule is of the same order of magnitude of typical MEMS dimensions. Thus, the molecular fluid theory must be used to estimate the damping. To predict the damping of a MEMS device both at high and low pressure levels, a multiphysics code was used. The proposed approach was validated through comparison with experimental data.

MOEMS (Micro-Opto-Electro-Mechanical System) are MEMS in which the optical part plays a dominant role. MOEMS are usually used to deflect and/or focus light from/on a given spot thus acting as optical switches.Now, imagine to have a 2D array of optical switches (e.g. a CCD) and to rotate the MOEMS so that the incoming light is focused subsequently on each of these switches. What you have designed is a 2D scanner. If you reverse the process (i.e. one light source and the MOEMS projecting on a screen) you have designed a projector. The main advantage of a MOEMS projector and/or scanner are the reduced dimensions/weight with respect to traditional projectors and scanners and the fact that the necessity of optical lenses is greatly reduced thus reducing the cost of the system.In the present paper, a 2D MOEMS projector/scanner is designed. The peculiarity of the system is that the MOEMS mirror is able to rotate around two perpendicular axes having only one excitation point instead of two thus allowing to design a system that can be produced from a planar surface. This is achieved by applying the excitation to the micro-mirror indirectly through a supporting frame that is free to rotate around an eccentric vertical axis. The micro-mirror, instead, is free to rotate around an eccentric horizontal axis with respect to such supporting frame (figure 3). To be able to excite the motion of the micro-mirror along two perpendicular axes, the actuation force has to be applied in a point that is not nodal for any of the two rigid vibration modes of the system, has to be bi-harmonic and, to increase displacements, has to have frequencies that correspond to the two rigid eigenfrequencies of the micro-mirror.

The electronics on board of a vibrating MEMS sensor is able to compensate only for small changes in the mechanical characteristics of the device. Thus, it is of great importance to find an easy and fast way to evaluate the mechanical parameters, such as the resonance frequency and the damping coefficient, of a MEMS sensor before putting the device on the market [A. Cigada, E. Leo, M. Vanali, Optical and electrical methods to measure the dynamic behaviour of a MEMS gyroscope sensor, Proceedings of IMECE2005 ASME International Mechanical Engineering Congress and Exposition 2005, Orlando, Florida, USA [ 14]]. Moreover, for a vibrating MEMS gyroscope also the quadrature error, i.e. [Y.Y Bao, C.L. Yung, Modelling and compensation of quadrature error for silicon MEMS microgyroscope], has to be kept below a given threshold in order to be able to acurately measure low angular speeds.Verification tests are usually carried out at the end of the production process, i.e. when the package is complete. Thus, only electrical measurements are possible. In the present paper, a reliable approach to determine mechanical parameters as well as the quadrature error of the MEMS device through electrical measurements is proposed and results are compared to more traditional optical measurements. (c) 2006 Elsevier B.V. All rights reserved.

The aim of the research is to identify the movements of a race motorcycle rider while running on a race track. It is well known how the inertial characteristics of human body are comparable to the motorcycle ones. It is necessary to understand the body-vehicle interactions to describe the dynamical behaviour of the complete system. A numerical model of pilot-vehicle system, in a multi body logic, is being developed, using the results of this research. The tested motorcycle is equipped by a fixed original measurements system, characterised by five cable-extension position transducer (voltage divider type). The pilot's back is equipped by a rigid ergal staff, constrained by a multi cross belts system, on which two target points are placed, with pre defined distance. These targets are pointed by the five sensors. Through the use of a non linear numerical model it is possible to identify the translational and the cardanic rotational 3D movements of the rider body. To proceed with the procedure validation and calibration, an anthropomorphic arm robot was used. The targets staff was constrained on the end effector, the sensors system was ground fixed and incremental displacements and rotations were programmed. Some results finally obtained in a first experimental session are reported.

Except for MEMS working in ultra high vacuum, the main cause of damping is the air surrounding the system. When the working pressure is equal to the atmospheric one (from now on called “high pressure”, i.e. 105Pa), the mean free path of an air molecule is much smaller than typical MEMS dimensions. Thus, air can be considered as a viscous fluid and two phenomena occur: flow damping and squeeze film damping. These two terms can be evaluated through a simplified Navies-Stocks equation. In vacuum (from now on called “low pressure”, i.e. 26Pa), the air cannot be considered as a viscous fluid any more since the free path of an air molecule is of the same order of magnitude of typical MEMS dimensions. Thus, the molecular fluid theory must be used to estimate the damping. To predict the damping of a MEMS device both at high and low pressure levels, a multi-physics code was used and the achieved numerical results were compared to experimental data measured on the same device.

Let us consider a MEMS translational gyroscope. When significantly displacing the proof mass, the nonlinear hardening characteristic of the supporting beams becomes visible. Thus, the resonance peak of the structure bends towards the higher frequencies. This property is useful to easily synchronise sense and drive resonances thus increasing the sensibility of the MEMS gyroscope. Through a test structure designed to access the high deformation range of the supporting beams, its nonlinear vibrations were investigated both experimentally and numerically. It is shown that a simple nonlinear lumped parameter model is sufficient to schematise the gyroscope and that a semi-analytical integration method allows to quickly determine both stable and unstable branches of the system's dynamic response.

MEMS sensors usually work at frequencies well above 1kHz. At these frequencies, supporting beams do not only contribute to the elasticity of the system but also to its inertia. However, the supporting beams’ inertia is not equal to any modal mass since excitation frequency is not equal to any eigenfrequency of the supporting beams. Based on a FEA model of the supporting beams it is possible to determine this inertial contribution at any working frequency thus allowing to set up a simple lumped parameter model that correctly reproduces the dynamic behaviour of the device.

Except for MEMS working in ultra high vacuum, the main cause of damping is the air surrounding the system. When the working pressure is equal to the atmospheric one (from now on called "high pressure", i.e. 105Pa), the mean free path of an air molecule is much smaller than typical MEMS dimensions. Thus, air can be considered as a viscous fluid and two phenomena occur: flow damping and squeeze film damping. These two terms can be evaluated through a simplified Navier-Stokes equation. In vacuum (from now on called "low pressure", i.e. 26Pa), the air cannot be considered as a viscous fluid any more since the free path of an air molecule is of the same order of magnitude of typical MEMS dimensions. Thus, the molecular fluid theory must be used to estimate the damping. To predict the damping of a MEMS device both at high and low pressure levels, a multi-physics code was used and the achieved numerical results were compared to experimental data measured on the same device

A semi-physical model of an hydraulic power steering system is presented in this paper. The proposed model allows to evaluate the wheels dynamic response to steering inputs and to calculate the corresponding reaction torque on the steering-wheel (steering torque). The analyzed steering system increases its stiffness (so that the steering assist level is decreases) with the rise of the vehicle speed. Thus, vehicle maneuverability is improved during parking maneuvers, while at high vehicle speeds, stability and driver perceived steering feel are ensured. A two d.o.f. (steering-wheel and rack-pinion rotations) model has been implemented during this study. The model parameters have been identified through the standard laboratory tests carried out to characterize a steering system, minimizing the difference between the experimental data and the model numerical results. During laboratory tests the hydraulic power system has been characterized first, measuring its stiffness variation as a function of the relative rotation between the steering-wheel and the rack-pinion, and the steering torque as a function of the difference between the delivery and the reversal pressure of the double-acting ram. The complete steering system has been then characterized, suspending the vehicle and placing the wheels on appropriate low-friction plates which permit them to turn; sine and frequency sweep steering input have been applied by a robot and the corresponding reaction torque on the steering-wheel has been measured. Simulations results are in good agreement with the experimental ones for all the performed tests. The steering system model has been integrated into a 14 d.o.f. vehicle model developed by the Mechanical Department of the Politecnico di Milano in order to access its reliability during handling maneuvers. Several simulations have been performed both in open (step-steer, steering pad, etc.) and in closed loop (lane change, double lane change, slalom, etc). Simulation results have shown a reduction of the toe angle due to the deformability of the steering system and a time delay of the wheel angle respect to the cinematic condition introduced by the steering system dynamics. The reaction torque on the steering-wheel has also been calculated during the simulations to access the driver perceived steering feel during the maneuvers.

The study of motorcycle’s stability is an important task for the passenger’s safety. The range of frequencies involved for the handling stability is lower than 10 Hz. A numerical model was developed to access the stability of a motorcycle vehicle in this frequency range. The stability is analysed using a linearized model around the straight steady state condition. In this condition, the vehicle’s vertical and longitudinal motion are decoupled, hence the model has only four degrees of freedom (steering angle, yaw angle, roll angle and lateral translation), while longitudinal motion is imposed. The stability was studied increasing the longitudinal speed. The input of the model can be either a driver input manoeuvre (roll angle) or a transversal component of road input able to excite the vibration modes. The driver is introduced in the model as a steering torque that allows the vehicle to follow a reference trajectory. To validate the model, experimental tests were done. To excite the vehicle modes, the driver input was not taken into account considering both the danger for the driver and the repeatability of the manoeuvre. Two different vehicle configurations were tested: vehicle 1 is a motorcycle [7] and vehicle 2 is a scooter. Through the use of the validated model, a sensitivity analysis was done changing structural (for example normal trail, steering angle, mass) and non structural parameters (for example longitudinal speed).

When significantly displacing a proof mass, the nonlinear hardening characteristic of the supporting beams becomes visible. Thus, the resonance peak of the structure is no longer vertical but bends towards the higher frequencies. This property is useful to easily synchronise sense and drive resonances thus increasing the sensibility of the MEMS gyroscope. Through a test structure designed to investigate the high deformation range of the supporting beams, its nonlinear vibrations were investigated both experimentally and numerically. It is shown that a simple nonlinear lumped parameter model could be sufficient to schematise the system and that a semi-analytical integration method allows to quickly determine both stable and unstable branches of the system’s dynamic response and to design the supporting structure.

A full characterization of the mechanical parameters for vibrating MEMS sensors is required before integrating the mechanical and the electronic part. This is to verify that the main design specifications are fulfilled before sensors are available on the market. The main goal is to accurately establish the well-working devices in the shortest time. In this paper the electrical method based on the measurement of the GND current is used to satisfy this purpose. To check the validity of the achieved results through this method a comparison is done with those obtained through the widely used optical method based on vibration measurements through by means of a Laser Doppler Vibrometer (LDV).

When significantly displacing a proof mass, the nonlinear hardening characteristic of the supporting beams becomes visible. Thus, the resonance peak of the structure is no longer vertical but bends towards the higher frequencies. This property is useful to easily synchronise sense and drive resonances thus increasing the sensibility of the MEMS device. Through a test structure designed to investigate the high deformation range of the supporting beams, its nonlinear vibrations were investigated both experimentally and numerically.

The present paper deals with the development and identification of a new dynamic model of an hydraulic bushing. The peculiarity and the difficulty of such a bushing is its highly nonlinear behavior. In fact, common linear theological models are not able to realistically reproduce the relation between applied displacements and reaction forces. A nonlinear viscoelastic model presents significant difficulties in the identification of its parameters since no frequency based approach is feasible. Thus, special multi-step identification procedures have to be implemented.The adoption of such a nonlinear viscoelastic model inside a multi-body vehicle model significantly increases the computational cost of the entire vehicle model. It is therefore necessary to optimize the model not only from a reliability point of view but also from a computational cost point of view. The best compromise has therefore to be chosen.