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Stefano Mariani   Dr.  Research or Laboratory Scientist 
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Stefano Mariani published an article in January 2019.
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Top co-authors See all
Francesco Ciucci

110 shared publications

Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Hong Kong 999077, SAR, China

Eleni N. Chatzi

105 shared publications

Department of Civil Environmental and Geomatic Engineering, Swiss Federal Institute of Technology, Zurich, Switzerland

A. Pandolfi

100 shared publications

Dipartimento di Ingegneria Civile ed Ambientale, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy

Alberto Corigliano

78 shared publications

Politecnico di Milano

Francesco Braghin

70 shared publications

Department of Mechanical Engineering, Politecnico di Milano, Via La Masa, 1, Milan, Italy

Publication Record
Distribution of Articles published per year 
(2001 - 2019)
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Publications See all
Article 0 Reads 0 Citations Identification of strength and toughness of quasi-brittle materials from spall tests: a Sigma-point Kalman filter approa... Stefano Mariani, Giorgio Gobat Published: 03 January 2019
Inverse Problems in Science and Engineering, doi: 10.1080/17415977.2018.1561674
DOI See at publisher website
Article 0 Reads 0 Citations Modelling the cushioning properties of athletic tracks Luca Andena, Serena Aleo, Francesco Caimmi, Francesco Briati... Published: 17 November 2018
Sports Engineering, doi: 10.1007/s12283-018-0292-z
DOI See at publisher website
CONFERENCE-ARTICLE 12 Reads 0 Citations <strong>Polysilicon MEMS sensors: sensitivity to sub-micron imperfections</strong> Aldo Ghisi, Marco Geninazzi, Stefano Mariani Published: 16 November 2018
Proceedings, doi: 10.3390/ecsa-5-05858
DOI See at publisher website ABS Show/hide abstract

The drive towards miniaturization in polysilicon MEMS industry leads unavoidably to question the hypothesis of homogeneity commonly accepted for continuum mechanics. Silicon grain morphology and orientation eventually influences the mechanical response of MEMS devices, when critical structural components (such as e.g. suspension springs) shrink. Moreover, the deep reactive-ion etching process, leading to the so-called over-etch, whose relevance is more and more increasing when referred to dimensions comparable with the grain size, affects the accuracy of the geometrical layout. Under these conditions, a spread in the working operational behavior of the devices is expected, which is obviously a matter of concern both for MEMS design and reliability. While this consequence is well known and expected, the quantification of the aforementioned spread is far to be under control, both in design practice and theory.

In this work, through Monte Carlo analyses on statistical volume elements we show the effect of the grain morphology and orientation on the elastic effective properties of polysilicon beams constituting critical MEMS components. The extensive numerical investigation is summarized through statistical (lognormal) distributions for the elastic properties as a function of grain size morphology, quantifying therefore not only the expected mean values but also the also the spread around them. These (analytical) statistical distributions represent a simple while rigorous alternative to cumbersome numerical analyses. Their utility is testified through the analysis of a statically indeterminate MEMS structure, quantifying the possible initial offset away from the designed configuration due to residual stresses arising from the production process.

Article 0 Reads 0 Citations Cost–Benefit Optimization of Structural Health Monitoring Sensor Networks Giovanni Capellari, Eleni Chatzi, Stefano Mariani Published: 06 July 2018
Sensors, doi: 10.3390/s18072174
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Structural health monitoring (SHM) allows the acquisition of information on the structural integrity of any mechanical system by processing data, measured through a set of sensors, in order to estimate relevant mechanical parameters and indicators of performance. Herein we present a method to perform the cost–benefit optimization of a sensor network by defining the density, type, and positioning of the sensors to be deployed. The effectiveness (benefit) of an SHM system may be quantified by means of information theory, namely through the expected Shannon information gain provided by the measured data, which allows the inherent uncertainties of the experimental process (i.e., those associated with the prediction error and the parameters to be estimated) to be accounted for. In order to evaluate the computationally expensive Monte Carlo estimator of the objective function, a framework comprising surrogate models (polynomial chaos expansion), model order reduction methods (principal component analysis), and stochastic optimization methods is introduced. Two optimization strategies are proposed: the maximization of the information provided by the measured data, given the technological, identifiability, and budgetary constraints; and the maximization of the information–cost ratio. The application of the framework to a large-scale structural problem, the Pirelli tower in Milan, is presented, and the two comprehensive optimization methods are compared.
Article 4 Reads 3 Citations Structural Health Monitoring Sensor Network Optimization through Bayesian Experimental Design Giovanni Capellari, Eleni Chatzi, Stefano Mariani Published: 01 June 2018
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, doi: 10.1061/ajrua6.0000966
DOI See at publisher website
Article 5 Reads 2 Citations Mechanical Characterization of Polysilicon MEMS: A Hybrid TMCMC/POD-Kriging Approach Ramin Mirzazadeh, Saeed Eftekhar Azam, Stefano Mariani Published: 17 April 2018
Sensors, doi: 10.3390/s18041243
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Microscale uncertainties related to the geometry and morphology of polycrystalline silicon films, constituting the movable structures of micro electro-mechanical systems (MEMS), were investigated through a joint numerical/experimental approach. An on-chip testing device was designed and fabricated to deform a compliant polysilicon beam. In previous studies, we showed that the scattering in the input–output characteristics of the device can be properly described only if statistical features related to the morphology of the columnar polysilicon film and to the etching process adopted to release the movable structure are taken into account. In this work, a high fidelity finite element model of the device was used to feed a transitional Markov chain Monte Carlo (TMCMC) algorithm for the estimation of the unknown parameters governing the aforementioned statistical features. To reduce the computational cost of the stochastic analysis, a synergy of proper orthogonal decomposition (POD) and kriging interpolation was adopted. Results are reported for a batch of nominally identical tested devices, in terms of measurement error-affected probability distributions of the overall Young’s modulus of the polysilicon film and of the overetch depth.