82 shared publications
Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Hong Kong 999077, SAR, China
70 shared publications
Dipartimento di Ingegneria Civile ed Ambientale, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
56 shared publications
Institute of Structural Engineering, Department of Civil, Environmental and Geomatic Engineering, ETH Zürich, Zürich, Switzerland
49 shared publications
Department of Civil and Environmental Engineering, Politecnico di Milano, Milano, Italy
37 shared publications
Politecnico di Milano
(1970 - 2018)
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.
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.
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.
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.