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سال:1397 | دوره: | شماره: |تعداد مقالات:7

This paper reports nano-structured SiO2-TiO2 coatings using the sol-gel technique on 316L steel substrates. Nanoindentation, surface analysis and corrosion resistance tests were performed on different samples. The nanomechanical tests allowed to compare the uncoated steel samples (Eavg = 193. 24GPa and the mean hardness of 2. 63GPa and coated steel samples (Eavg = 287. 38GPa and mean hardness of 5. 74GPa), resulting in an improvement of the resistance and modulus of elasticity of the coated steel substrates. From a surface analysis an average thickness of 1. 12μ m was obtained in the coated samples, presenting a dense and consolidated coating. Polarization resistance (PR) and electrochemical impedance spectroscopy (EIS) tests were performed. The PR tests showed a resistance of 2. 11  105 (Ω cm2) for the uncoated steel, while the coated steel showed a resistance of 3. 46  105 (Ω cm2), indicating an increase in resistance compared to the bare steel. The EIS tests showed greater resistance by the coated steel (5. 8 105 (Ω cm2)) compared with the bare steel (2. 8 105 (Ω cm2)). The effects of the electrolyte in both conditions were observed by SEM after immersion for 24 h, showing pitting by the bare steel and good protection by the coated steel.

In the past, damaged tissue was removed from the patient's body. But now tissue regeneration using scaffolds and implants are used to repair the damaged tissue and organs. Besides the mechanical properties of metallic biomaterials, they suffer from bioinertness. Using some surface treatment techniques, the bioactivity and also corrosion resistance of titanium implants could be improved. In this study, the effect of H2O2 and alkali treatments on the corrosion behavior of titanium implant in the artificial saliva, surface morphology and phases formed on the surface, was investigated using electrochemical corrosion test, scanning electron microscopy (SEM) and thin film x-ray diffractometry (TF-XRD), respectively. The results indicated that on the surface of H2O2 and alkali treated titanium samples, fine particles of anatase and fine wires of rutile were formed, respectively. The results revealed that the corrosion resistance of alkali treated titanium in the artificial saliva was higher than that of the H2O2 treated titanium sample. The corrosion current density for the untreated, H2O2 and alkali treated titanium samples was about 0. 6×10-8, 5×10-8, 3×10-8A/cm2, respectively.

Since tubes have numerous industrial applications, many attempts have focused on the Severe Plastic Deformation (SPD) processes of tubes. Tube Channel Pressing (TCP) is an attractive process since it can be used for processing different sizes of tubes. However, more attempts are needed to improve the outcomes of TCP. For example, imposing of greater strain besides reductions of strain heterogeneity are the challenges of this process. This work is aimed to optimize the die geometry of TCP in order to increase the imposed strain as well as to decrease strain heterogeneity through an FEM simulation method verified by experiment. The results showed that the increase of die curvature radius causes the decrease of imposed plastic strain and the increase of strain heterogeneity. In addition, the minimum amount of die convex height for imposing of reasonable strain through TCP was calculated considering tube thickness and channel angle. Besides, the optimum die geometry is recommended in order to minimize the strain heterogeneity.

In the present investigation, an artificial neural network (ANN) model is developed to predict the isothermal hot forging behavior of AlCuMgPb aluminum alloy. The inputs of the ANN are deformation temperature, frictional factor, ram velocity and displacement whereas the forging force, barreling parameter and final shape are considered as the output variable. The developed feed-forward back-propagation ANN model is trained with Leven berg– Marquardt learning algorithm. Since the finite element (FE) simulation of the process is a time-consuming procedure, the ANN has been designed and the outputs of the FE simulation of the hot forging are used for training the network and then, the network is employed for prediction of the behavior of the output parameters during the isothermal forging process. Experimental data is compared with the FE predictions to verify the model accuracy. The performance of the ANN model is evaluated using a wide variety of standard statistical indices. Results show that the ANN model can efficiently and accurately predict isothermal hot forging behavior of AlCuMgPb alloy. Finally the extrapolation ability and noise sensitivity of the ANN model are also investigated. It is found that the extrapolation ability is very high in the proximity of the training domain, and the noise tolerance ability very robust.

Compound casting refers to a process that is used to produce bimetals. This study investigates the interface of Mo40/C93200 that is produced by compound casting process. In this research, molten bronze is poured around the steel core, and interaction between liquid and solid creates a diffusion zone and is followed by a transition layer which leads to the creation of a diffused region between the interfaces of metal layers. The results of micro-hardness and macro hardness tests were used to complete the studies. The results of the hardness of the Mo40 alloy revealed that the alloy micro-hardness was almost 308 Vickers which confirms the ferrite-pearlite state of microstructures. The results of metallography revealed that the boundary between steel and bronze alloys due to the difference in electric potential during etching evolved a galvanic cell and one section was formed as the cathode and the other section as the anode. In this situation, steel was corroded and bronze was protected. Also, the results of SEM show that the boundaries between two alloys have an acceptable adhesion and the strength of interface is sufficient. The result of tensile test indicates that the final yield strength was about 800 MPa and the elongation increases by 2%, which is an acceptable value. It is also observed that the failure is a soft defect type and a sufficient connection between steel and bronze is formed.

This paper presents the study on influence external pressures on vibration of functionally graded materials thin-walled cylindrical shell supported. The functionally graded materials (FGMs) properties are graded in the thickness direction of the shell. FGMs are advanced composite materials, consisting of different types of materials, in which the properties shift continuously from one material on the one side to another material on the other side with a specific gradient. The FGM cylindrical shell supported equations with external pressure are established based on classical shell theory with beam functions as axial modal function. The governing equations of motion were employed, using energy functional and by applying the Ritz method. The boundary conditions represented by end conditions of the FGM structure which are sliding-sliding, clamped-free and clamped-simply supported being considered. This problem was solved with computer programming using MAPLE package. Comparison results are carried out to verify the validity with published papers. The influence of the external pressures, loop support and effect of the different boundary conditions on natural frequencies of FGM thin-walled cylindrical shell are studied.

In the present study, prediction of alumina recovery efficiency (A. R. E), the amount of produced red mud (A. P. R), red mud settling rate (R. S. R) and bound-soda losses (B. S. L) in Bayer process red mud has been carried out for the first time in the field. These predictions are based on lime to bauxite ratio and chemical analyses of bauxite and lime as the Bayer process feed materials. Radial basis function (RBF) and multilayer perceptron (MLP) as artificial neural network and the multiple linear regression (MLR) method have been used to predict these parameters in Iran Alumina Company. According to the obtained results, it is evident that the RBF method has outperformed the other two methods in the prediction of A. R. E, A. P. R and B. S. L. However, the multilayer perceptron (MLP) method can produce better and more precise results in the prediction of R. S. R. This research also exposes more effective variables on A. R. E, A. P. R, R. S. R, and B. S. L.