A simple and closed form solution for the vibration response of finite ribbed plates to point force/moment excitations is presented in this paper. This solution shows that input mobilities of finite ribbed plates are bounded by the input mobilities of the uncoupled plate and beam that form the ribbed plate. It is found that point force input mobilities of a finite ribbed plate are controlled by the plate bending stiffness when the excitation force is more than a quarter wavelength away from the beam. The input mobilities are mainly dominated by the beam flexural stiffness when the force acts on or very close to the beam, and when the beam flexural stiffness is far greater than the plate bending stiffness. Similar result is found in the moment excitation case when the moment axis is perpendicular to the beam neutral axis (bending moment excitation). In contrast, the input mobilities of the ribbed plate do not vary much from that of the corresponding uncoupled plate when the moment axis parallels to the beam’s neutral axis (torsional moment excitation) where the input mobilities are mainly dominated by the plate bending stiffness. The reductions in plate kinetic energy due to beam insertions are discussed.

A closed form solution for the dynamic response of finite ribbed plates

Flexible stimuli-responsive materials are deformable, stretchable, light-weight, and desirable for smart personal protective equipment (PPE), and as the primarily functional components in the wearable system which spontaneously respond to surrounding variations. These materials enable the traditional PPE who provide passive protection to be smart with the abilities of sensing, actuating, surface changing and self-healing, which enhances the protection and reduces the unintentional occupation injuries. This article presents a critical review of the structure, properties, fundamental mechanisms and current development of flexible stimuli-responsive materials and their potential/present applications to smart PPE, covering strain, pressure, temperature, and gas sensors, biopotential electrodes, exosystems, switchable wetting surfaces and biosafety masks. Scientific and practical challenges along with critical issues and opportunities are also discussed.

Flexible stimuli-responsive materials for smart personal protective equipment

Why do guitars sound like they do, and how do you set about making one? These are central questions addressed by Richard French in this attractive new book. He is both an assistant professor of engineering and also a guitar builder and player, so that one could hardly imagine anybody better equipped to write a book with this wide coverage.

Engineering the guitar : theory and practice.

With the ever-increasing importance of dairy and meat production, precision livestock farming (PLF) using advanced information technologies is emerging to improve farming production systems. The latest automation, connectivity, and artificial intelligence developments open new horizons to monitor livestock in the pasture, controlled environments, and open environments. Due to the significance of livestock detection and tracking, this systematic review extracts and summarizes the existing deep learning (DL) techniques in PLF using unmanned aerial vehicles (UAV). In the context of livestock recognition studies, UAVs are receiving growing attention due to their flexible data acquisition and operation in different conditions. This review examines the implemented DL architectures and scrutinizes the broadly exploited evaluation metrics, attributes, and databases. The classification of most UAV livestock monitoring systems using DL techniques is in three categories: detection, classification, and localization. Correspondingly, this paper discusses the future benefits and drawbacks of these DL-based PLF approaches using UAV imagery. Additionally, this paper describes alternative methods used to mitigate issues in PLF. The aim of this work is to provide insights into the most relevant studies on the development of UAV-based PLF systems focused on deep neural network-based techniques.

/pdf/a-systematic-literature-review-on-the-use-of-deep-learning-3jbiagqm.pdf

A Systematic Literature Review on the Use of Deep Learning in Precision Livestock Detection and Localization Using Unmanned Aerial Vehicles

Bearings are a key component of rotating machines, and their fault diagnosis is critical for safe operation of rotating machines. Since bearings often work under variable speed conditions and their vibrations contain rich information of health conditions, time–frequency analysis (TFA) of vibration signals has been shown to be an effective way to perform bearing fault diagnosis. However, applications of traditional TFA methods for analyzing vibrations from bearings are often constrained by limited time variability and smearing effects. This article proposes an instantaneous frequency (IF) synchronized-generalized stepwise demodulation transform (IFS-GSDT) method for TFA of nonstationary vibration signals. Demodulators of the proposed IFS-GSDT method are first derived as functions of inclined angles formed by IF lines of windowed signals; thus, IF preestimation is no longer required. A spectral kurtosis-guided strategy is then developed to determine optimal inclined angles. To effectively tackle multicomponent signals, the proposed IFS-GSDT method explores a new linear transforming kernel that synchronizes the demodulators to all signal components, and an iteration procedure can be avoided. The proposed method also allows for the signal to be reconstructed when the window length under analysis is fixed. The effectiveness of the proposed method is validated using simulations and measured vibration data. Comparisons between the proposed method and other popular TFA methods are also conducted to demonstrate the superior characteristics of the proposed method.

Instantaneous Frequency Synchronized Generalized Stepwise Demodulation Transform for Bearing Fault Diagnosis

Simplified setup for the vibration study of plates with simply-supported boundary conditions.

Coiled polymer actuators are fabricated by heating a twisted nylon fishing filament. This paper provides a detailed review of their manufacturing process and proposes a method which manufactures these actuators consistently to improve the predictability of their behavior. Two devices are presented: a device that prepares consistent filament sections, and another that twists and coils the filament. Seven successful actuators are produced using the proposed method and compared with seven actuators produced using a method featured in other studies. The behavior of both sets of actuators is then compared using tensile tests on an Instron universal testing machine. The actuators manufactured using the device demonstrate a lower fail rate and produce more force on average. However, the average force produced by each actuator using the proposed method varied slightly. It is believed this fluctuation is due to inconsistent resistance wire lengths and the actuator end loop creation technique. Both of which would require additional improvement.

Consistent Manufacturing Device for Coiled Polymer Actuators

Effects of using scalloped shape braces on the natural frequencies of a brace-soundboard system

This paper demonstrates the differences between popular transformation-based input representations for vibration-based machine fault diagnosis. This paper highlights the dependency of different input representations on hyperparameter selection with the results of training different configurations of classical convolutional neural networks (CNNs) with three common benchmarking datasets. Raw temporal measurement, Fourier spectrum, envelope spectrum, and spectrogram input types are individually used to train CNNs. Many configurations of CNNs are trained, with variable input sizes, convolutional kernel sizes and stride. The results show that each input type favors different combinations of hyperparameters, and that each of the datasets studied yield different performance characteristics. The input sizes are found to be the most significant determiner of whether overfitting will occur. It is demonstrated that CNNs trained with spectrograms are less dependent on hyperparameter optimization over all three datasets. This paper demonstrates the wide range of performance achieved by CNNs when preprocessing method and hyperparameters are varied as well as their complex interaction, providing researchers with useful background information and a starting place for further optimization.

https://www.mdpi.com/2571-631X/4/2/19/pdf

Exploring the Relationship between Preprocessing and Hyperparameter Tuning for Vibration-Based Machine Fault Diagnosis Using CNNs

Although many improvements in the manufacturing of guitars have been made recently, one aspect that has often been overlooked is that of the acoustical consistency of the final manufactured product. The aim of this paper is to create a better understanding of the effect of a brace on the frequencies of vibration of the brace-soundboard system. This paper seeks to shed light on why a luthier ‘tunes’ braces when a guitar soundboard is hand-manufactured. A simple analytical model of a rectangular brace and soundboard is derived from first principles using Kirchhoff plate theory in order to develop insight into the effect of the soundboard’s stiffness and brace thickness on the frequencies of the combined system. Natural frequencies and modeshapes of the combined system are calculated via the assumed shape method. Results show that by adjusting the thickness of the brace in order to compensate for the stiffness of the plate, one of the natural frequencies of the combined system can be adjusted to meet a desired value. However, simultaneously adjusting several natural frequencies cannot be done with a rectangular brace. Therefore modifications to the shape of the brace are explored.

/pdf/can-a-brace-be-used-to-control-the-frequencies-of-a-plate-2mgmgoicu0.pdf

Can a brace be used to control the frequencies of a plate

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