I. Khodai Delouei, H. Sabet , V. Abouei Mehrizi,
Volume 4, Issue 2 (1-2019)
Abstract
Friction Stir Welding is one of the solid-state processes and today it has been used to join different types of materials. Friction stir welding does not have many problems and limitations due to melting and solidification of weld metal and by controlling its variables, the microstructure and desired mechanical properties can be achieved at the joint. Recently, in most industrial areas, due to its lightness and energy saving, much attention has been paid to the joining of aluminum alloys. The present study investigates the microstructure and evaluation of mechanical properties of friction stir welding in AA2024 and AA6061butt welds. A cylindrical threaded tool was used to join 5 mm thick plates at rotational speeds of 800, 1000 and 1200 rpm and traverse speeds of 30, 50, 70, 90 and 110 mm / min. In order to perform the necessary investigations, metallurgical observations were performed by optical microscope and scanning electron microscope equipped with a chemical analysis system of the elements, as well as mechanical tests of tensile strength and micro hardness. The results showed that the difference between the two alloys causes hardness variations in the nugget zone and a large hardness drop at the transition between the zone composed of both alloys and the 6061 zone. By increasing the traverse speed from 30 to 110 mm / min at constant rotational speeds of 800, 1000 and 1200 rpm, due to reduced input heat, the grain size decreases and the hardness and strength increase. Also, the highest tensile strengths and hardness were 221.6 Mpa and 111.05 Vickers, respectively, for a sample welded at a rotational speed of 1000 rpm and a traverse speed of 110 mm / min.
M. Bozorgmehr, A. Heidari, K. Amini, M. Loh Mousavi, F. Gharavi,
Volume 9, Issue 2 (1-2024)
Abstract
In the present study, friction stir process (FSP) was used to produce AL/ZrO2/ZrSiO4 surface hybrid composite at a fixed rotation speed of 1400 rpm and traverse speeds of 20, 25, 31.5 and 40 mm/min. Therefore, the purpose of the mentioned study is to investigate the effect of tool traverse speed on the microstructure, hardness and wear behavior of the above-mentioned surface hybrid composite and compare it with base material aluminum 5052. Investigations showed that as a result of FSP operation, a fine-grained structure is created, which improves the hardness and wear resistance of the samples compared to the base sample with the presence of ZrO2 and ZrSiO4 particles. Also, the results showed that among the FSP samples, the sample with a speed of 20 mm/min has the highest hardness and wear resistance. The reason for this is that in this sample, due to the lower traverse speed compared to other samples, more heat has been generated, which has led to more suitable particle distribution and more fine particles. Therefore, in the sample with the traverse speed of 20 mm/min, the hardness and wear resistance increases by 27.3% and 68.9% respectively compared to the base material sample. Also, the examination of the wear surfaces of the samples showed that the wear mechanism in the base sample is strong adhesive wear, and as a result of the FSP operation and surface compositing due to the fineness of the grains and the increase in hardness, the wear mechanism has become weak adhesive, so the wear resistance of the sample is FSPs have been improved.
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.
