m. yousefieh

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.

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.

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.

In this study، the effects of heat treatment on the microstructure and pitting corrosion resistance of super duplex stainless steel welds have been investigated. The results showed that step annealing treatments in the temperature range of 550–1000 °C resulted in a precipitation of sigma phase and Cr2N along the ferrite/austenite and ferrite/ferrite boundaries. At this temperature range، the metastable pits mainly nucleated around the precipitates formed in the grain boundary and ferrite phase. Above 1050 °C، the microstructure contains only austenite and ferrite phases. At this condition، the critical pitting temperature of samples successively arrived to the highest value obtained in this study.