G. Coffignal

Characterization of residual stress in metallic films on silicon with micromechanical devices

Al and Cr cantilever microbeams, microbridges and suspended microrings were fabricated by isotropic etching of silicon with a SF6/O2 plasma after film patterning. They were used for the characterization of both compressive stress, extensive stress and stress gradients in the metallic films. It is shown that valid residual stress measurements with such micromechanical devices must take into account stress changes due to process fabrication, underetching at the clamped ends, edge effects and stress gradients. This is demonstrated by bucking threshold measurements of microbridges and microrings of various sizes and 3D finite element linear static and linear buckling analyses including underetching or stress gradients. Cantilever microbeam profile measurements shows that temperature rise during etching must be carefully minimizes to avoid thermally induced stress gradients. Stress gradients in the deposited films have no effect on microbridges critical buckling stress but lead to a distortion of the microrings before the occurrence of buckling.

Contribution of computational mechanics in numerical simulation of machining and blanking: State-of-the-Art

SummaryBlanking and machining are commonly used in processes to obtain the shape of many mechanical pieces. Although considerable number of experimental results exist, certain essential aspects of cutting are still not well understood. This comes from the complexity of the thermomechanical phenomena induced by the material separation as well as from the complexity of the dynamical behaviour of the whole workpiece/tool/machine system. Numerical simulations make it possible to go further in the comprehension and the prediction of machining and cutting processes.In this work the state-of-the art is analysed and we present the most recent developments in the contribution of computational mechanics to numerical simulation of machining and blanking. This contribution is, on one hand, developed at a very global scale calledmacroscopic scale. At this scale a representation of the deformations of the piece is necessary, for example when thin walls are present, and when both predictions of the geometrical state of final surface and/or stability of the process are expected. On the other hand, the contribution is also located at a more local scale: themesoscopic scale. At this scale, the aim is the determination of thermomechanical sollicitations applied to the tool, the simulation of chip formation, or the description of residual states (mechanical, chemical) inside the workpiece after machining.

Computational methods for accounting of structural uncertainties, applications to dynamic behavior prediction of piping systems

Computational probabilistic methods enable us to incorporate and propagate uncertainties in mechanical models. However, in some cases, classical methods, such as FORM/SORM methods and Monte-Carlo methods, can be computationally expensive or inaccurate. An efficient importance sampling method is then suggested to yield sufficiently accurate results with acceptable computational cost in an industrial context. The method is an importance sampling method based on a second order asymptotic approximation combined with the HyperCube Latin method. A clustering method is used to solve the global optimization problem which arises to find the points of maximum likelihood. The efficiency of the method compared to classical methods is illustrated with several examples. Considerable reduction of the statistical error of the estimated failure probability can be achieved. The interest of the method is assured provided the points of local maximum likelihood are not too numerous and uniformly distributed. The paper presents two vibratory test cases, the second one is an industrial piping system.

A general Bayesian framework for ellipse-based and hyperbola-based damage localization in anisotropic composite plates

This article focuses on Bayesian Lamb wave-based damage localization in structural health monitoring of anisotropic composite materials. A Bayesian framework is applied to take account of uncertainties from experimental time-of-flight measurements and angle-dependent group velocity within the composite material. An original parametric analytical expression of the direction dependence of group velocity is proposed and validated numerically and experimentally for anisotropic composite and sandwich plates. This expression is incorporated into time-of-arrival (ellipse-based) and time-difference-of-arrival (hyperbola-based) Bayesian damage localization algorithms. This way, the damage location and the group velocity profile are estimated jointly and a priori information is taken into consideration. The proposed algorithm is general as it allows us to take into account uncertainties within a Bayesian framework, and to model effects of anisotropy on group velocity. Numerical and experimental results obtained with different damage sizes or locations and for different degrees of anisotropy validate the ability of the proposed algorithm to estimate both the damage location and the group velocity profile as well as the associated confidence intervals. Results highlight the need to consider for anisotropy in order to increase localization accuracy, and to use Bayesian analysis to quantify uncertainties in damage localization.

A data-driven temperature compensation approach for Structural Health Monitoring using Lamb waves

This paper presents a temperature compensation method for Lamb wave structural health monitoring. The proposed approach considers a representation of the piezo-sensor signal through its Hilbert transform that allows one to extract the amplitude factor and the phase shift in signals caused by temperature changes. An ordinary least square (OLS) algorithm is used to estimate these unknown parameters. After estimating these parameters at each temperature in the operating range, linear functional relationships between the temperature and the estimated parameters are derived using the least squares method. A temperature compensation model is developed based on this linear relationship that allows one to reconstruct sensor signals at any arbitrary temperature. The proposed approach is validated numerically and experimentally for an anisotropic composite plate at different temperatures ranging from 16 ° C to 85 ° C . A close match is found between the measured signals and the reconstructed ones. This approach is interesting as it needs only a limited set of piezo-sensor signals at different temperatures for model training and temperature compensation at any arbitrary temperature. Damage localization results after temperature compensation demonstrate its robustness and effectiveness.

ERROR ESTIMATION THROUGH THE CONSTITUTIVE RELATION FOR REISSNER-MINDLIN PLATE BENDING FINITE ELEMENTS

Abstract This paper deals with the so-called ‘error estimation through the constitutive relation’ for Reissner–Mindlin plate bending finite element analysis, which is based on the calculation of statically admissible stress resultant fields. Further an energy norm through the constitutive relation is introduced to quantify the difference between the calculated statically admissible stress resultant fields and the stress resultants given by the finite element analysis. The statically admissible stress resultant fields are obtained by local calculations at the node or at the element level. The procedures involve an optimization for estimating the statically admissible quantities as close as possible to the finite element result, and thus lead to a more accurate error estimation, considering the Pragger–Synge theorem. The set of examples shows the good agreement between the proposed indicator and the error obtained from the analytical solution. It is concluded that plate bending analysis is a field of application which is well fitted for error estimation through the constitutive relation.

New element for sandwich beams with piezoelectric layers

A new element is developed for the analysis of laminated/sandwich beams incorporating piezoelectric layers. Beam theory employed here satisfies the interface stress and displacement continuity and has zero shear stress on the top and bottom surfaces of the beam. Piezoelectric layers can be placed arbitrarily and can act as sensors and actuators. The transverse shear deformation in the form of trigonometric sine function is incorporated to define the transverse shear strain. The displacement and stress variations along the length and thickness obtained are presented.

Mechanical Simulation of Machining

We have developed a new mechanical simulation for machining with precision cutting tools in order to predict vibratory behaviour, but more importantly, resulting final surface. For this simulation a new machining model has been developed. The various mechanical aspects of the problem, including vibratory motion of flexible machine parts, cutting law and the interaction between tool and workpiece, have been taken into account within the same experimental software.

Simulation of a finishing operation : milling of a turbine blade and influence of damping

Milling is used to create very complex geometries and thin parts, such as turbine blades. Irreversible geometric defects may appear during finishing operations when a high surface quality is expected. Relative vibrations between the tool and the workpiece must be as small as possible, while tool/workpiece interactions can be highly non-linear. A general virtual machining approach is presented and illustrated. It takes into account the relative motion and vibrations of the tool and the workpiece. Both deformations of the tool and the workpiece are taken into account. This allows predictive simulations in the time domain. As an example the effect of damping on the behavior during machining of one of the 56 blades of a turbine disk is analysed in order to illustrate the approach potential.© 2012 ASME

Structural Damage Diagnosis Using Subspace Based Residual and Artificial Neural Networks

Abstract In this paper, an Artificial Neural Network (ANN) based approach using a new non-parametric residual is presented for damage diagnosis. The residual is associated with Observability null-space of the system and is generated by using a parity matrix obtained from covariance driven output-only Subspace Identification (SubID) algorithm. Training of ANN is established using residuals generated from Finite Element (FE) model of the structure under consideration for different defect cases. This trained ANN is in turn used to identify the predefined defect types, in semi-real time, on the actual structure. The proposed methodology is applied to an active composite beam structure and its effectiveness to identify damage is studied by testing a trained ANN with data obtained from both simulation and experimentation. Promising results were obtained showing that, it was possible to distinguish between healthy and damaged states with good accuracy and repeatability.