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Stefano Mariani   Dr.  Research or Laboratory Scientist 
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Stefano Mariani published an article in November 2017.
Research Keywords & Expertise
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Top co-authors See all
Francesco Ciucci

82 shared publications

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

Anna Pandolfi

70 shared publications

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

Eleni N. Chatzi

56 shared publications

Institute of Structural Engineering, Department of Civil, Environmental and Geomatic Engineering, ETH Zürich, Zürich, Switzerland

Matteo Bruggi

49 shared publications

Department of Civil and Environmental Engineering, Politecnico di Milano, Milano, Italy

Alberto Corigliano

37 shared publications

Politecnico di Milano

79
Publications
143
Reads
17
Downloads
212
Citations
Publication Record
Distribution of Articles published per year 
(1970 - 2018)
Total number of journals
published in
 
33
 
Publications See all
CONFERENCE-ARTICLE 15 Reads 1 Citation Cost-benefit optimization of sensor networks for SHM applications Giovanni Capellari, Eleni Chatzi, Stefano Mariani Published: 14 November 2017
Proceedings, doi: 10.3390/ecsa-4-04891
DOI See at publisher website ABS Show/hide abstract

Structural health monitoring (SHM) is aimed to obtain information about the structural integrity of a system, e.g. via the estimation of its mechanical properties through observations
collected with a network of sensors. In the present work, we provide a method to optimally design sensor networks in terms of spatial configuration, number and accuracy of sensors. The utility of the sensor network is quantified through the expected Shannon information gain of the measurements with respect to the parameters to be estimated. At assigned number of sensors to be deployed over the structure, the optimal sensor placement problem is ruled by the objective function computed and maximized by combining surrogate models and stochastic optimization algorithms. For a general case, two formulations are introduced and compared: (i) the maximization of the information obtained through the measurements, given the appropriate constraints (i.e. identifiability, technological and budgetary ones); (ii) the maximization of the utility efficiency, defined as the ratio between the information provided by the sensor network and its cost. The method is applied to a large-scale structural problem, and the outcomes of the two different approaches are discussed.

Article 4 Reads 2 Citations Uncertainty Quantification of Microstructure—Governed Properties of Polysilicon MEMS Ramin Mirzazadeh, Stefano Mariani Published: 12 August 2017
Micromachines, doi: 10.3390/mi8080248
DOI See at publisher website ABS Show/hide abstract
In this paper, we investigate the stochastic effects of the microstructure of polysilicon films on the overall response of microelectromechanical systems (MEMS). A device for on-chip testing has been purposely designed so as to maximize, in compliance with the production process, its sensitivity to fluctuations of the microstructural properties; as a side effect, its sensitivity to geometrical imperfections linked to the etching process has also been enhanced. A reduced-order, coupled electromechanical model of the device is developed and an identification procedure, based on a genetic algorithm, is finally adopted to tune the parameters ruling microstructural and geometrical uncertainties. Besides an initial geometrical imperfection that can be considered specimen-dependent due to its scattering, the proposed procedure has allowed identifying an average value of the effective polysilicon Young’s modulus amounting to 140 GPa, and of the over-etch depth with respect to the target geometry layout amounting to O=−0.09μm. The procedure has been therefore shown to be able to assess how the studied stochastic effects are linked to the scattering of the measured input–output transfer function of the device under standard working conditions. With a continuous trend in miniaturization induced by the mass production of MEMS, this study can provide information on how to handle the foreseen growth of such scattering.
Article 3 Reads 0 Citations A Multiscale Approach to the Smart Deployment of Micro-Sensors over Lightweight Structures Giovanni Capellari, Francesco Caimmi, Matteo Bruggi, Stefano... Published: 15 July 2017
Sensors, doi: 10.3390/s17071632
DOI See at publisher website ABS Show/hide abstract
A topology optimization approach has been recently proposed to maximize the sensitivity to damage of measurements, collected through a network of sensors to be deployed over thin plates for structural health monitoring purposes. Within such a frame, damage is meant as a change in the structural health characterized by a reduction of relevant stiffness and load-carrying properties. The sensitivity to a damage of unknown amplitude and location is computed by comparing the response to the external actions of the healthy structure and of a set of auxiliary damaged structures, each one featuring reduced mechanical properties in a small region only. The topology optimization scheme has been devised to properly account for the information coming from all of the sensors to be placed on the structure and for damage depending on its location. In this work, we extend the approach within a multiscale frame to account for three different length scales: a macroscopic one, linked to the dimensions of the whole structure to be monitored; a mesoscopic one, linked to the characteristic size of the damaged region; a microscopic one, linked to the size of inertial microelectromechanical systems (MEMS) to be used within a marginally-invasive health monitoring system. Results are provided for a square plate and for a section of fuselage with stiffeners, to show how the micro-sensors have to be deployed to maximize the capability to detect a damage, to assess the sensitivity of the results to the measurement noise and to also discuss the speedup in designing the network topology against a standard single-scale approach.
CONFERENCE-ARTICLE 5 Reads 0 Citations Foreword: Proceedings of the 3rd International Electronic Conference on Sensors and Applications Stefano Mariani, Francesco Ciucci, Dirk Lehmhus, Thomas B. M... Published: 31 May 2017
Proceedings, doi: 10.3390/ecsa-3-03908
DOI See at publisher website ABS Show/hide abstract

This issue of Proceedings gathers the papers presented at the 3rd International Electronic Conference on Sensors and Applications (ECSA-3), held online on 15-30 November 2016 through the sciforum.net platform developed by MDPI. The annual ECSA conference was initiated in 2014 on an online basis only, to allow the participation from all over the world with no concerns of travel and related expenditures. This type of conference looks particularly appropriate and useful because research concerned with sensors is rapidly growing, and a platform for rapid and direct exchanges about the latest research findings can provide a further burst in the development of novel ideas.

Article 4 Reads 4 Citations Online damage detection via a synergy of proper orthogonal decomposition and recursive Bayesian filters Saeed Eftekhar Azam, Stefano Mariani, Nader Khajeh Ahmad Att... Published: 28 April 2017
Nonlinear Dynamics, doi: 10.1007/s11071-017-3530-1
DOI See at publisher website
CONFERENCE-ARTICLE 6 Reads 0 Citations <strong>Assessment of micromechanically-induced uncertainties </strong><strong>in the electromechanical response of MEMS... Ramin Mirzazadeh, Stefano Mariani Published: 14 November 2016
Proceedings, doi: 10.3390/ecsa-3-S1001
DOI See at publisher website ABS Show/hide abstract

Microelectromechanical systems (MEMS) have been already successfully commercialized for around 20 years. The design of novel MEMS sensors currently target two important features: smaller dimensions and higher reliability. As the characteristic size of the mechanical components of the devices decreases, uncertainties in the mechanical and geometrical properties induced by the microfabrication process become more and more important. To address these issues, an on-chip testing device has been proposed by the authors to avoid any visual inspection for the read-out. As the device has been obtained with a standard MEMS fabrication process, the experimentally tested conditions can be rather similar to those featured by the application systems. The spreading of the mentioned mechanical and geometrical features has been assessed thanks to a thin micro-cantilever, so as to magnify the effects of the microstructure on the overall MEMS behavior.

The electromechanical responses of ten nominally identical specimens have been recorded, and experimental data have shown a significant scattering due to the presence of the relevant uncertainty sources. To interpret the response of the device, an analytical reduced-order model and a finite element model of the whole device have been developed. The effects of random film morphology and of (over)etch depth have been then assessed through a Monte Carlo analysis. A genetic algorithm has been eventually adopted to identify features of the probability distributions of the mechanical and geometrical uncertainties in the batch of test structures.

Conference papers
CONFERENCE-ARTICLE 11 Reads 0 Citations <strong>Polysilicon MEMS sensors: sensitivity to sub-micron imperfections</strong> Aldo Ghisi, Marco Geninazzi, Stefano Mariani Published: 16 November 2018
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.

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