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The purpose of this paper is to evaluate the performance of the semi-active fluid damper. It is recognized that the performance of such a damper depends upon the magnetic and hydraulic circuit design. These dampers are generally used to control the vibrations in various applications in machine tools and robots. The present paper deals with the design of magneto-rheological (MR) damper. A finite element model is built to analyze and understand the performance of a 2D axi-symmetric MR damper. Various configurations of damper with modified piston ends are investigated. The input current to the coil and the piston velocity are varied to evaluate the resulting change in magnetic flux density (B), magnetic field (H), field dependent yield stress and magnetic force vectors. The simulation results of the various configurations of damper show that higher magnetic force is associated with plain piston ends. The performance of filleted piston ends is superior to that of other configurations for the same magnitude of coil current and piston velocity.

The damper design is done based on the fact that mechanical energy required for yielding of MR fluid increases with increase in applied magnetic field intensity. In the presence of magnetic field, the MR fluid follows Bingham’s plastic flow model, given by the equation τ = η γ•+τ _y (H) τ > τ _y . The above equation is used to design a device which works on the basis of MR fluid. The total pressure drop in the damper is evaluated by summing the viscous component and yield stress component which is approximated as ΔP = 12_ηQL/g3W + C_τyL/g, where the value of the parameter, C ranges from a minimum of 2 (for ΔP_τ ΔP_η less than approximately 1) to a maximum of 3 (for ΔP_τ/ΔP_η greater than approximately 100). To calculate the change in pressure on either side of the piston within the cylinder, yield stress is required which is obtained from the graph of yield stress vs magnetic field intensity provided by Lord Corporation for MR fluid −132 DG.

In this work, three different finite element models of MR damper piston are analyzed. The regression equations, contour plots and surface plots are obtained for different parameters. This study can be used as a reference for selecting the parameters for meeting different requirements. It is observed from the simulation of these models that the plain ends model gave optimum magnetic force and 2D flux lines with respect to damper input current. This is due to the fact that the plain ends model has more area when compared with that of other models. It is also observed that filleted ends model gave optimum magnetic flux density and yield stress. As there is reduced pole length in the filleted ends model, the MR fluid occupies vacant area, and hence results in increased flux density and yield shear stress. The filleted ends assist the formation of dense magnetic flux lines thereby increasing the flux density and yield stress. This implies that higher load can be carried by the filleted ends damper even with a smaller size.

This work is carried out to manufacture different capacities of the dampers. This can be applied as vibration controls.

The purpose of the study is to classify the radiographic images into three categories such as fillings, cavity and implant to identify dental diseases because dental disease is a very common dental health problem for all people. The detection of dental issues and the selection of the most suitable method of treatment are both determined by the results of a radiological examination. Dental x-rays provide important information about the insides of teeth and their surrounding cells, which helps dentists detect dental issues that are not immediately visible. The analysis of dental x-rays, which is typically done by dentists, is a time-consuming process that can become an error-prone technique due to the wide variations in the structure of teeth and the dentist's lack of expertise. The workload of a dental professional and the chance of misinterpretation can be decreased by the availability of such a system, which can interpret the result of an x-ray automatically.

This study uses deep learning (DL) models to identify dental diseases in order to tackle this issue. Four different DL models, such as ResNet-101, Xception, DenseNet-201 and EfficientNet-B0, were evaluated in order to determine which one would be the most useful for the detection of dental diseases (such as fillings, cavity and implant).

Loss and accuracy curves have been used to analyze the model. However, the EfficientNet-B0 model performed better compared to Xception, DenseNet-201 and ResNet-101. The accuracy, recall, F1-score and AUC values for this model were 98.91, 98.91, 98.74 and 99.98%, respectively. The accuracy rates for the Xception, ResNet-101 and DenseNet-201 are 96.74, 93.48 and 95.65%, respectively.

The present study can benefit dentists from using the DL model to more accurately diagnose dental problems.

This study is conducted to evaluate dental diseases using Convolutional neural network (CNN) techniques to assist dentists in selecting the most effective technique for a particular clinical condition.