Smart Structures and Applications

A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "A:Physics".

Deadline for manuscript submissions: closed (15 September 2023) | Viewed by 1626

Special Issue Editor


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Guest Editor
Department of Solid Mechanics, Faculty of Mechanical Engineering, University of Kashan, Kashan 87317-51167, Iran
Interests: piezoelectric and piezomagnetic materials; sensors and actuators; energy harvesting and control of systems and devices; micro and nano mechanics

Special Issue Information

Dear Colleagues,

The development of smart materials and structures and their applications in electromechanical and electromagnetomechanical systems has encouraged designers, scientists, and engineers to introduce innovative and novel systems and structures.

The scope of this Special Issue involves the presentation of novel structures and systems composed of piezoelectric and piezomagnetic materials and structures as sensors or actuators in electromechanical and electromagnetic systems based on advanced methods of analysis and investigation. Smart materials may be used in various geometries, configurations, and patterns, such as rods, beams, plates, shells, and sandwich structures. The significant effects of multi-field loads on the smart and intelligent structures have the potential to serve as important contributions in the wider context of mechanical engineering. This Special Issue invites submissions of papers discussing the size-dependent analysis of intelligent systems and structures in micro- and nanoscales using innovative and relevant theories.

The Special Issue also welcomes research that addresses the various applications of intelligent material and structures, as well as the experimental works in relation to the control of systems and energy-harvesting applications.

Dr. Mohammad Arefi
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Micromachines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • piezoelectric and piezomagnetic materials
  • sensors and actuators
  • energy harvesting and control of systems and devices
  • micro and nano mechanics

Published Papers (1 paper)

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Research

18 pages, 6725 KiB  
Article
FOSS-Based Method for Thin-Walled Structure Deformation Perception and Shape Reconstruction
by Huifeng Wu, Rui Dong, Qiwei Xu, Zheng Liu and Lei Liang
Micromachines 2023, 14(4), 794; https://doi.org/10.3390/mi14040794 - 31 Mar 2023
Viewed by 1166
Abstract
To improve the accuracy of deformation perception and shape reconstruction of flexible thin-walled structures, this paper proposes a method based on the combination of FOSS (fiber optic sensor system) and machine learning. In this method, the sample collection of strain measurement and deformation [...] Read more.
To improve the accuracy of deformation perception and shape reconstruction of flexible thin-walled structures, this paper proposes a method based on the combination of FOSS (fiber optic sensor system) and machine learning. In this method, the sample collection of strain measurement and deformation change at each measuring point of the flexible thin-walled structure was completed by ANSYS finite element analysis. The outliers were removed by the OCSVM (one-class support vector machine) model, and the unique mapping relationship between the strain value and the deformation variables (three directions of x-, y-, and z-axis) at each point was completed by a neural-network model. The test results show that the maximum error of the measuring point in the direction of the three coordinate axes: the x-axis is 2.01%, the y-axis is 29.49%, and the z-axis is 15.52%. The error of the coordinates in the y and z directions was large, and the deformation variables were small, the reconstructed shape had good consistency with the deformation state of the specimen under the existing test environment. This method provides a new idea with high accuracy for real-time monitoring and shape reconstruction of flexible thin-walled structures such as wings, helicopter blades, and solar panels. Full article
(This article belongs to the Special Issue Smart Structures and Applications)
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