This chapter examines belt and chain drives. The chain transmission is the most common transmission method, with a simple structure, easy maintenance, and cheap price, but it has quite a few disadvantages, such as the fact that chain wear will elongate it and need to be adjusted, maintenance is required during use, the noise is obviously too great, and motion inertia is large. The belt transmission is more common in cars, especially the toothed belt. The belt transmission has many advantages, such as stable transmission, low noise, maintenance-free nature, long life, and small motion inertia (toothed belt). There is almost no maintenance in the later stage, but this type of belt does not resist stones and dust like chains. In this chapter we discuss belt and chain selection, and share the belt and chain selection process from the aspects of belt and chain charts and tables, to help in understanding belts and chains correctly. Examples and solved/unsolved problems for selection of belt and chain size are also presented. This chapter can be used as a teaching tool for courses on machine design for mechanical and industrial engineering and materials engineering majors in colleges and universities, and can also be used as a reference for scientific and technical personnel engaged in scientific and engineering calculations.
The applications of robotics in medical field have increased extensively in the last two decades. This paper aims at studing the effect of trajectory planning method on dynamic response of six degrees of freedom micro-robot intended for surgery applications. The kinematic equations of motion were obtained using Denavit-Hartemberg representation. The dynamic equations of motion, which are important for the proper design of robot controller, were first derived using the Lagrangian-Euler technique. Then the required hub torque to move each joint was calculated for the motor selection. The trajectory planning was derived using two different methods of trajectory planning. These methods are the fifth-order polynomial and soft motion trajectory planning. A comparison of the dynamic response was carried out to choose the best method that gives the smooth set trajectory planning and best performance of the robot under investigation. The simulation results were obtained using MATLAB software.
Microencapsulated phase change materials have better thermal stability, and are widely used in the field of energy storage. In this paper, the core and wall material characteristics of microencapsulated phase change materials are reviewed. Several preparation methods, such as in-situ polymerisation, interfacial polymerisation, and suspension-like polymerisation, are analysed and the applications of microencapsulated phase change materials in fibre fabric, building, military camouflage and heat conduction are summarised.
In the last few years, SARS-CoVID-19 pandemic was the main cause of millions of deaths around the world and was the main reason for global economic recession.It is vital to explore noninvasive procedures to detect and eliminate this pandemic expeditious.In this research, we introduce a technique for detecting and quarantine the SARS-CoVID-19 by designing a piezoelectric device with a natural frequency that is equal to the virus structural natural frequency.Because of the resonance effect, the vibration of the device increases, and the power output produced by the piezoelectric layer in the harvester is used to detect the COVID-19 by powering a small LED.The light of the LED is a sign of infection.Furthermore, when the virus's natural frequency is determined, an ultrasonic resonance device can be used to eliminate the virus.Polymerase chain reaction test (PCR) is costly and time-consuming.Our proposed technique is both fast and inexpensive.The installing of the device, the modeling, and the treatment potentiality are discussed in this paper.To maximize the output power of the device at the virus natural frequency, three optimization algorithms are performed.It is observed that Bound Optimization by Quadratic approximation (BOBYQA) optimization algorithm enhances the output power.The analytical model of the proposed sensing device is derived based on the theory of Euler-Bernoulli.The voltage and power frequency response for open and closed circuits are analytically derived.A Finite Element Method (FEM) for the proposed sensing device is developed and analytically verified.
The present work develops an expert system for detecting and predicting the crude oil types and properties at normal temperature <TEX>${\theta}=25^{\circ}C$</TEX>, by evaluating the dielectric properties of the fluid transfused inside glass fiber reinforced epoxy (GFRE) composite pipelines, by using electrical capacitance sensor (ECS) technique, then used the data measurements from ECS to predict the types of the other crude oil transfused inside the pipeline, by designing an efficient artificial neural network (ANN) architecture. The variation in the dielectric signatures are employed to design an electrical capacitance sensor (ECS) with high sensitivity to detect such problem. ECS consists of 12 electrodes mounted on the outer surface of the pipe. A finite element (FE) simulation model is developed to measure the capacitance values and node potential distribution of ECS electrodes by ANSYS and MATLAB, which are combined to simulate sensor characteristic. Radial Basis neural network (RBNN), structure is applied, trained and tested to predict the finite element (FE) results of crude oil types transfused inside (GFRE) pipe under room temperature using MATLAB neural network toolbox. The FE results are in excellent agreement with an RBNN results, thus validating the accuracy and reliability of the proposed technique.
Electrical Potential Change (EPC)-based faults detection is a promising methods in new ways. In this paper, BFRP composite pipeline system is used for analysis by using Electrical Capacitance Sensor (ECS) for delamination monitoring inside layers of pipe. The dielectric properties of the pipe material are gauged for intact and delamination stats. The array and capacitance value of the electrical contact, the change of capacitance and the changes in the distribution of node potential are tested. The results show that these diagrams can reflect the delamination information of the pipeline according to the author’s previous work. However, because the model is 3D and has different bending and twisting effects, these diagrams may not be a good choice to accurately detect delamination information. So, a novel Convolutional Neural Network (CNN) algorithm is employed to train and test the EPC diagrams to detect the delamination. The current technique have high values of accuracy rate (P%), recall rate (R%), and F-score (F%) equal to 95.2%, 93.7%, and 90.9% respectively, this indicates that the current technique show the recognition efficiency and accuracy.
Earth buildings are common types of structures in most rural areas in all developing countries. Catastrophic failure and destruction of these structures under seismic loads always result in loss of human lives and economic losses. Wall is an important load-bearing component of raw soil buildings. In this paper, a novel approach is proposed to improve the strength and ductility of adobe walls. Three types of analyses, material properties, mechanical properties, and dynamic properties, are carried out for the seismic performance assessment of the adobe walls. These performed studies include that, material properties of the earth cylinder block, mechanical properties of adobe walls under quasi-static loads, and dynamic performance of adobe walls excited by seismic waves. On investigation of material properties, eighteen cylindrical specimens with a diameter of 100 mm and a height of 110 mm were divided into three groups for compressive, tensile, and split pull strength tests, respectively. The results of the three groups of tests showed that the yield strength ratios of compressive, tensile, and shear strength were about 1:0.3:0.2. In order to study the performance of structural components, three 1/3 scale model raw soil walls with a dimension of 1,200 mm in width, 1,000 mm in height, and 310 mm in thickness were tested under cyclic loading. The average wall capacity of the wall obtained by the test was about 13.5 kN and the average displacement angle was about 1/135. The numerical simulation experiment is used to explore the mechanism of structural failure. A three-dimensional finite element model is established by choosing the material parameters based on the above test outcomes. The accuracy of the numerical simulation experiment is verified by simulation and comparison of the above quasi-static test results. Further, the collapse process of raw soil wall under a seismic wave is simulated for exploring the response and damage mechanism of structure. Based on those systematically analyzed, some useful suggested guidelines are provided for improving the seismic performance of raw soil buildings.
One of major errors in flow rate measurement for two-phase flow using an Electrical Capacitance Sensor (ECS) concerns sensor sensitivity under temperature raise. The thermal effect on electrical capacitance sensor (ECS) system for air-water two-phase flow monitoring include sensor sensitivity, capacitance measurements, capacitance change and node potential distribution is reported in this paper. The rules of 12-electrode sensor parameters such as capacitance, capacitance change, and change rate of capacitance and sensitivity map the basis of Air-water two-phase flow permittivity distribution and temperature raise are discussed by ANSYS and MATLAB, which are combined to simulate sensor characteristic. The cross-sectional void fraction as a function of temperature is determined from the scripting capabilities in ANSYS simulation. The results show that the temperature raise had a detrimental effect on the electrodes sensitivity and sensitive domain of electrodes. The FE results are in excellent agreement with an experimental result available in the literature, thus validating the accuracy and reliability of the proposed flow rate measurement system.
Rapid progress in the field of sensor technology has led to acquisition of massive amounts of measured data from structures being monitored. The data, however, contain inevitable measurement errors which often cause quantitative damage assessment to be ill conditioned. Attempts to incorporate a probabilistic method into a model have provided promising solutions to this problem by treating the uncertainties as random variables usually modeled with Gaussian distribution. However, the success enjoyed by the probabilistic method is limited by the lack of adequate information to obtain an unbiased probabilistic distribution of uncertainties. In addition, the probabilistic surrogate models involve complex and expensive computations, especially when generating output data. In this study, a non-probabilistic surrogate model based on wavelet weighted least squares support vector machine (WWLS-SVM) is proposed to address the problem of uncertainty in vibration based damage detection. The input data for WWLS-SVM consists of selected wavelet packet decomposition (WPD) features of the structural response signals, and the output is the Young's modulus of structural elements. This method calculates the lower and upper boundaries of the changes in the Young's modulus based on an interval analysis method. Considering the uncertainties in the input parameters, the surrogate model is used to predict the output of this interval bound. The proposed approach is applied to detect simulated damage in the four-story benchmark structure of IASC-ASCE SHM group. The results show that the proposed method can perform well in uncertainty-based damage detection of structures with less computational efforts compared to direct finite element model.
