A computational stochastic multiscale methodology for MEMS structures involving adhesive contact

Hoang, Thong; Wu, Ling; Paquay, Stéphane; Golinval, Jean‐Claude; Arnst, Maarten; Noels, Ludovic

doi:10.1016/j.triboint.2016.10.007

Cited by 12 publications

(22 citation statements)

References 65 publications

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“…To quantify the uncertainty of MEMS structures subjected to stiction, the stochastic model-based multi-scale method developed by the authors in [30] is applied. The generated samples of the contacting surfaces obtained using the generator developed in Sec.…”

Section: A Multi-scale Stochastic Framework For Adhesive Contactmentioning

confidence: 99%

“…Although the uncertainty characteristics [30], [31], [27], [26] and the non-Gaussianity of the surface height distribution [5], [32], [33] play important roles in the stiction phenomenon, they were either both neglected or accounted for but individually. On the one hand, in [5], the importance of non-Gaussian properties including the skewness and the kurtosis of the contacting surface heights was illustrated by a comparison between numerical predictions and experimental results; however, only deterministic predictions were given.…”

Section: Introductionmentioning

confidence: 99%

“…On the one hand, in [5], the importance of non-Gaussian properties including the skewness and the kurtosis of the contacting surface heights was illustrated by a comparison between numerical predictions and experimental results; however, only deterministic predictions were given. On the other hand, in [30], the authors have developed a stochastic multiscale methodology to predict the uncertainty of the stiction phenomenon but only for Gaussian contacting surfaces.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we enhance the stochastic multiscale method developed in [30] by accounting for skewness and kurtosis when simulating the surface roughness. Numerical predictions are compared with experimental results from [5] for the purpose of model validation.…”

Section: Introductionmentioning

confidence: 99%

“…The stochastic model-based multi-scale framework, which was developed by the authors in [30], is then applied. The method consists in constructing a FE model of the considered MEMS structure, in which the random adhesive contact forces are integrated as random contact laws.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Stochastic Multiscale Model of MEMS Stiction Accounting for High-Order Statistical Moments of Non-Gaussian Contacting Surfaces

Hoang

Golinval

et al. 2018

J. Microelectromech. Syst.

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Abstract-Stiction is a failure mode of microelectromechanical systems (MEMS) involving permanent adhesion of moving surfaces. Models of stiction typically describe the adhesion as a multiple asperity adhesive contact between random rough surfaces, and they thus require a sufficiently accurate statistical representation of the surface, which may be non-Gaussian. If the stiction is caused primarily by multiple asperity adhesive contact in only a small portion of the apparent area of the contacting surfaces, the number of adhesive contacts between asperities may not be sufficiently statistically significant for a homogenized model to be representative. In [Hoang et al., A computational stochastic multiscale methodology for MEMS structures involving adhesive contact, Tribology International, 110:401-425, 2017], the authors have proposed a probabilistic multiscale model of multiple asperity adhesive contact that can capture the uncertainty in stiction behavior. Whereas the previous paper considered Gaussian random rough surfaces, the aim of the present paper is to extend this probabilistic multiscale model to non-Gaussian random rough surfaces whose probabilistic representation accounts for the high order statistical moments of the surface height. The probabilistic multiscale model thus obtained is validated by means of a comparison with experimental data of stiction tests of cantilever beams reported in the literature.

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Section: A Multi-scale Stochastic Framework For Adhesive Contactmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Stochastic Multiscale Model of MEMS Stiction Accounting for High-Order Statistical Moments of Non-Gaussian Contacting Surfaces

Hoang

Golinval

et al. 2018

J. Microelectromech. Syst.

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SoftFEM: The Soft Finite Element Method

Peña

LaTorre

Jérusalem

2019

Numerical Meth Engineering

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Summary While the finite element method (FEM) has now reached full maturity both in academy and industry, its use in optimization pipelines remains either computationally intensive or cumbersome. In particular, currently used optimization schemes leveraging FEM still require the choice of dedicated optimization algorithms for a specific design problem, and a “black box” approach to FEM‐based optimization remains elusive. To this end, we propose here an integrated finite element‐soft computing method, ie, the soft FEM (SoftFEM), which integrates a finite element solver within a metaheuristic search wrapper. To illustrate this general method, we focus here on solid mechanics problems. For these problems, SoftFEM is able to optimize geometry changes and mechanistic measures based on geometry constraints and material properties inputs. From the optimization perspective, the use of a fitness function based on finite element calculation imposes a series of challenges. To bypass the limitations in search capabilities of the usual optimization techniques (local search and gradient‐based methods), we propose, instead a hybrid self adaptive search technique, the multiple offspring sampling (MOS), combining two metaheuristics methods: one population‐based differential evolution method and a local search optimizer. The formulation coupling FEM to the optimization wrapper is presented in detail and its flexibility is illustrated with three representative solid mechanics problems. More particularly, we propose here the MOS as the most versatile search algorithm for SoftFEM. A new method for the identification of nonfully determined parameters is also proposed.

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A model-adaptivity method for the solution of Lennard-Jones based adhesive contact problems

Dhia¹,

Du²

2018

Comput Mech

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A computational stochastic multiscale methodology for MEMS structures involving adhesive contact

Cited by 12 publications

References 65 publications

Stochastic Multiscale Model of MEMS Stiction Accounting for High-Order Statistical Moments of Non-Gaussian Contacting Surfaces

Stochastic Multiscale Model of MEMS Stiction Accounting for High-Order Statistical Moments of Non-Gaussian Contacting Surfaces

SoftFEM: The Soft Finite Element Method

A model-adaptivity method for the solution of Lennard-Jones based adhesive contact problems

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