Showing 4 results for Fsw
M. Ansaripour, A. Soltanpoor, A. Ghasemi, M.r. Dehnavi,
Volume 2, Issue 1 (8-2016)
Abstract
The aim of this study was to evaluate the mechanical properties and corrosion behavior of friction stir welding (FSW) connection of A517 (B) steel plate. Mechanical properties and corrosion behavior of weld zone were evaluated after reaching to optimum FSW microstructure with the lowest martensite phase. Thus, after the identifying phase microstructure by SEM and XRD analysis, mechanical properties were analyzed by micro-hardness and tensile test. Micro hardness data shows slight increases in stir zone (SZ) compared with the base metal (BM); although a reduction of about 17% in hardness of heat-affected zone (HAZ) was sensible. Reduction of hardness in the HAZ was appeared as drop by about 12 percent of the yield strength and 19 percent of ultimate strength compared with BM. SEM images from fracture surface of the tensile sample showed bi-modal distribution of large and small Dimples being sings of softness of HAZ .Comparing corrosion behavior in solution consist of 3.5 wt% of NaCl showed that there was no passive layers to prevent dissolution of the metal in the SZ and BM. while BM and SZ had fairly similar corrosion rates, Difference of 50 mV between corrosion potential of SZ and BM showed that in galvanic condition, corrosion resistance of BM could be lower than SZ.
A.s. Razavi, H. Sabet,
Volume 4, Issue 1 (8-2018)
Abstract
In this research, the FSW Butt joint of commercial aluminum 1050 was investigated by using the 7075 aluminum alloy interlayer on the linear velocity of 30, 50 and 100 mm / min, and rotational speeds of 800 and 1200 rpm. A threaded cylindrical tool was used for joining of the 5 mm sheets. The OM, SEM, microhardness and tensile tests were done. The results shows that in sample with an interlayer at the condition of the 800 rpm and 30 mm/min the maximum tensile strength and hardness appeared and in the non-layered sample at the 800 rpm and 50 mm/min, the maximum tensile strength and hardness was obtained. The results shows that by using the interlayer the tensile strength and hardness were increase.
P. Chamani, H. Sabet, M. Ghanbari Haghighi,
Volume 9, Issue 2 (1-2024)
Abstract
In this study the effect of rotational speed and tool angle parameters on the microstructure and mechanical properties of the AZ91/CP-Ti joint was investigated, for this reason the sheets with 4 x 26 x 100 mm dimensions were prepared and joint by FSW with different rotational speed (800, 1200 and 2500 rpm) and the tool angle (0.5, 1 and 3 degrees). After joining, the samples were cut and prepared for study of microstructural and mechanical properties. OM and SEM examination shows that the structure of AZ91/CP-Ti nugget zone includes alpha grains and the microstructure of the mix zone on the AZ91 side includes α-magnesium coaxial grains with Mg17Al12 intermetallic compounds. The results of the tensile test show that the maximum tensile strength value (160 MPa) related to the rotation speed of 2500 rpm and the tool angle of 1 degree. It was also determined that the rotation speed of 800 rpm was not suitable for joining of AZ91/CP-Ti. On the other hand, it was observed that by increasing the tool angle the work piece, initially leads to an increases the strength from 141 MPa to 160 MPa and then decreases to 132 MPa. the results of the Vickers hardness test show that the average of the nugget zone hardness was to 173, which is higher than the hardness of AZ91 alloy (61 Vickers) and near to the hardness of CP-Ti (167 Vickers).
M. Mosallaee, A.h. Morshedy,
Volume 9, Issue 2 (1-2024)
Abstract
In this research, the optimization of the artificial neural network (ANN) capability for predecting the tensile strength and elongation of friction stir welded Al-5083 (FS-welded Al-5083) was carried out. The effective parameters of ANN, such as the number of layers, number of neurons in hidden layers, transfer function between layers, the learning algorithm and etc. were investigated and the efficient neural network was determined to predict the tensile properties of FS-welded Al-5083. The investigations revealed that the perceptron neural network with two hidden layers and 17 neurons numbers, Lunberg-Marquardt training algorithm and Logsig transfer function for the intermediate layers and Tansig transformation function for the output layer is the most optimized neural network for the prediction. The optimized network has an optimal structure based on the minimum value of the mean square error of 0.05, the maximum total correlation coefficient of 0.93 and the line regression with an angle of 45 degrees between the actual and estimated values. Therefore, this network has a good performance for training, generalizing and estimating of tensile strength and elongation of FS-welded Al-5083.
