غلامرضا مرامی

Argon or Gas-Tungsten Arc welding (GTAW) is known as an important method of joining metals in industrial applications. Due to the importance of the controlling welding-parameters to prevent any defect formation during the process; in this research, weld-current, pre-heating temperature and electrode-traveling speed are adopted as the input welding-parameters. Then, using the Grey Analysis Method, the effect of the aforementioned welding-parameters on the ultimate-strength, hardness and angular distortions of the welded joints are obtained and optimized. Grey-diagrams are plotted by calculating the Grey-ratio, Grey coefficient and Grey-degree. Finally, based on these diagrams, the effect of the welding-input variables is established in percent and the optimum values are obtained with the effect of input parameters on the output values. Experimental tests are carried out based on the optimum values for the input and output parameters to verify the results of the Grey analyses.

In this research, effects of introducing 0.5wt% reduced graphene oxide (RGO) reinforcement on the creep behavior of the adhesivelybonded joints are investigated at 25oC,55oC. To this aim, uni-axial creep tests are conducted,creep deformations of the bulkspecimens produced via the neat adhesive,adhesive-RGO composite are recorded. Also, creep tests are performed to obtaincreep behavior of the single lap joints. Furthermore, results of the bulk specimen tests are used in a finite element based numericalanalysis to simulate the creep behavior of the joints. Results show that, a significant decrease in the creep deformations along with aremarkable increase of 30% in the creep life time occurs in the reinforced joints. Finally, comparing the numerical results andexperimental data shows that the numerical analysis accurately simulates the creep behavior of the joints.

The quality of parts in green sand mold casting mostly depends on the green sand properties such as compressive strength، permeability، mould hardness، etc. These parameters depend on input parameters like water، size and shape of the sand، percentage of bentonite and so on. The compressive strength of sand of different moisture contents which is required for modeling، obtained by experimental tests، and a research method، based on Artificial Neural Network (ANN). The predicted values of the compressive strength obtained by the ANN and new experiments found to be in good agreement with each other and showed that by using Artificial Neural Networks one can estimate the compressive strength of green sand precisely before molding.

Gas tungsten arc welding (GTAW), is one of the important methods used in industry and it is an arc welding process that uses a no consumable tungsten electrode to produce the weld. GTAW is most commonly used to weld thin sections of stainless steel and non-ferrous metals such as aluminum, magnesium, and copper alloys. In this research the authors have designed and manufactured an automatic welding arm in which its feed is controlled by a microcontroller. In different conditions like feed, ampere and pre-heat temperature practical tests have done and tensile strength of the welded parts has measured. Then by using this data an artificial neural network designed and tested. The inputs of the network are adjustable parameters of the welding process and the output is the tensile strength. The results show that by using neural networks we can determine the tensile strength precisely before the process.