Ti6Al4V alloy is widely used in aerospace and medical applications. It is classified as a difficult to machine material due to its low thermal conductivity and high chemical reactivity. In this study, hybrid intelligent models have been developed to predict surface roughness when end milling Ti6Al4V alloy with a Physical Vapor Deposition PVD coated tool under dry cutting conditions. Back propagation neural network (BPNN) has been hybridized with two heuristic optimization techniques, namely: gravitational search algorithm (GSA) and genetic algorithm (GA). Taguchi method was used with an L27 orthogonal array to generate 27 experiment runs. Design expert software was used to do analysis of variances (ANOVA). The experimental data were

This paper describes the transport of Alkaloids through Rotating Discs Contactor (RDC) using n-decane as a liquid membrane. The transport of Pelletierine Alkaloid from a source phase through bulk liquid membrane to the receiving phase has been investigated. The general behaviour of Pertraction process indicates that% Extraction of pelletierine Alkaloid increased with increase in the number of stages and the agitation speed but high agitation speed was not favoured due to the increased risk of droplet formation during the operation. The pH of source and receiving phases were also investigated. The effect of organic solvent membrane on the extraction of Pelletierine was evaluated using ndecane, n-hexane and methyl cyclohexane. The results sho

In the literature, several correlations have been proposed for hold-up prediction in rotating disk contactor. However,
these correlations fail to predict hold-up over wide range of conditions. Based on a databank of around 611
measurements collected from the open literature, a correlation for hold up was derived using Artificial Neiral Network
(ANN) modeling. The dispersed phase hold up was found to be a function of six parameters: N, vc , vd , Dr , c d m / m ,
s . Statistical analysis showed that the proposed correlation has an Average Absolute Relative Error (AARE) of 6.52%
and Standard Deviation (SD) 9.21%. A comparison with selected correlations in the literature showed that the
developed ANN correlation noticeably

Cutting forces are important factors for determining machine serviceability and product quality. Factors such as speed feed, depth of cut and tool noise radius affect on surface roughness and cutting forces in turning operation. The artificial neural network model was used to predict cutting forces with related to inputs including cutting speed (m/min), feed rate (mm/rev), depth of cut (mm) and work piece hardness (Map). The outputs of the ANN model are the machined cutting force parameters, the neural network showed that all (outputs) of all components of the processing force cutting force FT (N), feed force FA (N) and radial force FR (N) perfect accordance with the experimental data. Twenty-five samp

Near surface mounted (NSM) carbon fibers reinforced polymer (CFRP) reinforcement is one of the techniques for reinforcing masonry structures and is considered to provide significant advantages. This paper is composed of two parts. The first part presents the experimental study of brick masonry walls reinforced with NSM CFRP strips under combined shear-compression loads. Masonry walls have been tested under vertical compression, with different bed joint orientations 90° and 45° relative to the loading direction. Different reinforcement orientations were used including vertical, horizontal, and a combination of both sides of the wall. The second part of this paper comprises a numerical analysis of unreinforced brick masonry (URM) wa

الخلاصة

تعد الانتخابات بمثابة الطريق المؤدي إلى الديمقراطية كونها النمط الأكثر شيوعاً لمشاركة المواطنين في الحياة السياسية للبلدان واختيار ممثليهم في المجالس التشريعية، حيث أن مطلب إجراء انتخابات حرة ونزيهة لم يعد مطلباً داخلياً فحسب بل مطلباً دولياً يصرّ المجتمع الدولي على الوفاء به وهذا يلقي على عاتق كل دولة أن تضع من الضمانات ما يكفل ممارسة هذه الانتخابات ب

This research depends on the relationship between the reflected spectrum, the nature of each target, area and the percentage of its presence with other targets in the unity of the target area. The changes occur in Land cover have been detected for different years using satellite images based on the Modified Spectral Angle Mapper (MSAM) processing, where Landsat satellite images are utilized using two software programming (MATLAB 7.11 and ERDAS imagine 2014). The proposed supervised classification method (MSAM) using a MATLAB program with supervised classification method (Maximum likelihood Classifier) by ERDAS imagine have been used to get farthest precise results and detect environmental changes for periods. Despite using two classificatio