Showing 5 results for Yousefieh
M. Yousefieh, M. Tamizifar, S.m.a. Boutorabi, E. Borhani,
Volume 3, Issue 2 (Journal OF Welding Science and Technology of Iran 2018)
Abstract
In the present research, the parameters of FSW process were optimized for the mechanical properties of thin aluminum- scandium alloys by a design of experiment (DOE) technique. The optimum conditions providing the highest mechanical properties were found by this method. Among the three factors and three levels tested, it was concluded that the tool rotational speed had the most significant effect on the mechanical properties and the travel speed had the next most significant effect. The effect of tool tilt angle was less important when compared to the other factors. The EBSD results demonstrated a recrystallized equi axial structure and the existence of a mixture of B and Ccomponents in the weld nugget.
M. Yousefieh, A. Jabbari,
Volume 6, Issue 2 (Journal OF Welding Science and Technology 2020)
Abstract
In this study, the temperature in friction stir welding of duplex stainless steel has been investigated. At first, temperature estimation was modeled and estimated at different distances from the center of the stir zone by the multivariate Lagrangian function. Then, the linear extrapolation method and multiple linear regression method were used to estimate the temperature outside the range and center of the stir zone. Temperature estimation is based on three parameters rotational speed, welding speed and distance from the center of stir zone. In the first method, by generalizing the multivariate Lagrangian method, the multivariate Lagrangian temperature function was generalized according to the above parameters. In the second method, in order to investigate the effect of the variables in the regression model, a comparison of two complete models and a reduced model based on the sum of squares errors was used. Then, by analyzing the multiple regression equations governing the output variable, a multiple linear regression function was introduced. Since the temperature of the stir zone is not measurable by the thermocouple, so in general the best fit curve for estimating the function is when the modeling is based on parameters that minimize the error function.To implement the multiple linear regression method, the error function was introduced to minimize the sum of the error squares and the error derivative was calculated in relation to the parameters of tool rotation speed, welding speed and distance from the center of the stir zone. Therefore, multiple linear regression method was considered as the basic method and as a criterion with other methods. According to the results obtained from the prediction in the center of the stir zone, the temperature difference in all three methods is desirable and negligible. The maximum temperature difference of multiple linear regression method with multivariate Lagrangian method in all nodes was 18.8 oC and multiple linear regression method with linear extrapolation method was 26.36 oC. Therefore, the multivariate Lagrangian interpolation method is less different than the linear extrapolation method in the center of the stir zone and is more accurate.
Dr. Mohammad Yousefieh,
Volume 7, Issue 1 (Journal OF Welding Science and Technology 2021)
Abstract
In this paper, using the Taguchi method, the parameters affecting the toughness of super duplex stainless steels in friction stir welding were optimized. In order to achieve optimal conditions, maximum toughness, the quality characteristic was used as the higher the better. Analysis of Taguchi results showed that in order to achieve optimal conditions in super duplex stainless steel weldments must have a tool rotational speed of 500 rpm, a welding speed of 60 mm / min, an initial pressure of 70 MPa and a tool tilt angle with the workpiece is equal to 3 degrees. Under optimal conditions, the toughness obtained from the confirmation test was 61 J, which was very close to the predicted toughness (58 J). Analysis of variance was also performed on the results of signal to noise (S/N) ratio. According to the results of analysis of variance, the tool rotational speed parameter with an influence percentage of 64% was the most effective parameter on toughness in friction stir welding of super duplex stainless steels. On the other hand, the parameters of welding speed (with an influence percentage of 17 %), initial 2 pressure (with an influence percentage of 16%) and tool tilt angle to the workpiece (with an influence percentage of 3%) were in the next ranks. Also, SEM micrographs from fracture surface of the samples in the impact test proved that the sample that had the least toughness in the impact test had a cleavage morphology and as a result, brittle fracture. This was while the morphology of the fracture surface of the tested sample under optimal conditions (with the highest toughness in this study = 61 J) had a large amount of fine and deep dimples. The presence of these dimples in large quantities indicated ductile fracture and eventually reaching the highest toughness.
M.r. Maraki, M. Mahmoodi, M. Yousefieh, H. Tagimalek,
Volume 8, Issue 2 (Journal OF Welding Science and Technology 2023)
Abstract
In Wire and arc additive manufacturing (WAAM) based on Gas metal arc welding (GMAW) is one of the methods of manufacturing metal layer by layer. One of this method's basic steps is predicting the welding geometry created in each welding step. In the current research, an experimental study was conducted in this field considering the effective parameters of welding geometry. For this purpose, three parameters of voltage, welding speed, and wire feeding speed were considered as effective parameters on the welding geometry of the process. The width and height of the weld bead was selected as the answer according to the type and application of the research. The least squares support vector machine was used to model the welding geometry in the process. The results obtained from the regression (R2) of train, test, validation, and total were 0.945, 0.793, 0.894, and 0.881 respectively. The comparison between the experimental data and the model data shows the significance of the proposed model.
M. R. Maraki, H. Tagimalek, Dr M. Yousefieh, A. Aghaeifar, A. Foorginejad,
Volume 10, Issue 1 (Journal OF Welding Science and Technology 2024)
Abstract
Society's great and growing demand for buildings and structures has created the need to apply new construction methods to shorten construction times, make buildings lighter, extend their useful life, and make them more earthquake-proof. In the long term, the new methods will lead to structural optimization, increased building performance, and the achievement of optimal operating conditions. New technologies are meeting society's increasing need for special structures more than ever. Additive manufacturing is based on gas metal arc welding as one of the fastest and most cost-effective manufacturing methods for primary metal structures. For this purpose, the three parameters voltage, wire feed speed, and welding speed were considered initial parameters affecting the width and height of the welding flux. To investigate the effects of the process,
16 experiments with input parameters were evaluated. The width and height of the sweat pollen were determined by experimental investigations. Subsequently, the resulting welding geometry is modeled using three numerical modeling methods, including intensive learning machines, relevence vector machine, and fuzzy logic. The comparison between the experimental data and the results of the three generated models shows that fuzzy logic comes closest to the experimental data of the welding geometry of the modeling methods. For example, the test data of the generative fuzzy model resulted in an average error for height and width of 0.667 and 0.5477, respectively, and a root mean square error for height and width of 0.0046 and 0.3, respectively, which expresses the generalization ability and reliability compared to other modeling methods in this process. Finally, a metal pattern of a special structure was produced based on arc and wire additive manufacturing based gas metal arc welding.
