Vibrating screens are subject to high levels of acceleration and often impart significant dynamic loads on plant buildings, especially in cases where more than one of these screens are employed in process applications. To avoid premature failure and reduce plant building construction costs in critical cases, a variety of techniques are applied in practice. Most of these require substantially increased system mass, which is undesirable because of cost and static loading. With this work the Magnetorheological (MR) fluids damper was used to alter the parameter of vibrating screen and improve the screening efficiency. The displacement signals of the vibrating screen with MR fluid damper were analyzed by using bispectrum (frequency domain) and time domain methods. It is demonstrated that damping and screening efficiency could be improved by using a MR Fluid damper for changing structural damping ratio. The results show that the vibrating screen has a better quality with improved screening efficiency and decreased vibration of the base frame.
Least squares support vector machines (LS-SVM) were developed for the analysis and prediction of the relationship between the cutting conditions and the corresponding fractal parameters of machined surfaces in face milling operation. These models can help manufacturers to determine the appropriate cutting conditions, in order to achieve specific surface roughness profile geometry, and hence achieve the desired tribological performance (e.g. friction and wear) between the contacting surfaces. The input parameters of the LS-SVM are the cutting parameters: rotational speed, feed, depth of milling. The output parameters of the LS-SVM are the corresponding calculated fractal parameters: fractal dimension D and vertical scaling parameter G. The LS-SVM were utilized successfully for training and predicting the fractal parameters D and G in face milling operations. Moreover, Weierstrass-Mandelbrot(W–M )fractal function was integrated with the LS-SVM in order to generate an artificially fractal predicted profiles at different milling conditions. The predicted profiles were found statistically similar to the actual measured profiles of test specimens and there is a relationship between the scale-independent fractal coefficients(D and G).
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