Abstract 

In the present study, composites were prepared by Hand lay-up molding. The composites constituents were epoxy resin as a matrix, 6% volume fractions of glass fibers (G.F) as reinforcement and 3%, 6% volume fractions of preparation natural material (Rice Husk Ash, Carrot Powder, and Sawdust) as filler. Studied the erosion wear behavior and coating by natural wastes (Rice Husk Ash) with epoxy resin after erosion. The results showed the non – reinforced epoxy have lower resistance erosion than natural based material composites and the specimen (Epoxy+6%glass fiber+6%RHA) has higher resistance erosion than composites reinforced with carrot powder and sawdust  at 30cm , angle 60

Undoubtedly, rutting in asphalt concrete pavement is considered a major dilemma in terms of pavement performance and safety faced by road users as well as the road authorities. Rutting is a bowl-shaped depression in the wheel paths that develop gradually with the increasing number of load applications. Heavy axle loadings besides the high pavement summer temperature enhance the problem of rutting. According to the AASHTO design equation for flexible pavements, a 1.1 in rut depth will reduce the present serviceability index of relatively new pavement, having no other distress, from 4.2 to 2.5. With this amount of drop in serviceability, the entire life of the pavement in effect has been lost. Therefore, it is crucial to look at the mechani

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

The main aim of this research paper is investigating the effectiveness and validity of Meso-Scale Approach (MSA) as a modern technique for the modeling of plain concrete beams. Simply supported plain concrete beam was subjected to two-point loading to detect the response in flexural. Experimentally, a concrete mix was designed and prepared to produce three similar standard concrete prisms for flexural testing. The coarse aggregate used in this mix was crushed aggregate. Numerical Finite Element Analysis (FEA) was conducted on the same concrete beam using the meso-scale modeling. The numerical model was constructed to be a bi-phasic material consisting of cement mortar and coarse aggregate. The interface between the two c

Overlapped have been prepared from epoxy resin material added to carbon Nanotube and percentages weight (0.1, 0.05, 0.01) % Studied the mechanical properties of the composite (bending, tensile an d hardness) has been found that the Flexural and tensile modulus of the composites were higher than the pure epoxy resin this may be due to the high mechanical strength of carbon nano tube (CNT). The hardness of the epoxy carbon Nanotube composites increased and the reason is due to increased overlap and stacking between the additives and material basis, which reduces the movement of polymer molecules leading to increased resistance to scratching material and cutting, will become more resistance to plastic deformation.

BP algorithm is the most widely used supervised training algorithms for multi-layered feedforward neural net works. However, BP takes long time to converge and quite sensitive to the initial weights of a network. In this paper, a modified cuckoo search algorithm is used to get the optimal set of initial weights that will be used by BP algorithm. And changing the value of BP learning rate to improve the error convergence. The performance of the proposed hybrid algorithm is compared with the stan dard BP using simple data sets. The simulation result show that the proposed algorithm has improved the BP training in terms of quick convergence of the solution depending on the slope of the error graph.