فهرست مطالب مسعود آقاخانی

This paper reports an investigation carried out to determine the effect of aluminum oxide Nano-particles coated with manganese on the microstructure and mechanical properties of single-pass butt joint of low carbon steel plates of 6.0 mm thicknesses using gas metal arc welding process. After selecting appropriate welding parameters and adding 0.25 gr and 0.5 gr of Nano-particles into the joint line and carrying out the welding, the samples were prepared for micro hardness and tensile tests. Furthermore, the sample with optimum Nano-particles having the highest ultimate tensile strength and without having any visible and metallurgical defects in the microstructure was selected for further investigation.  Subsequently, microstructures of the weld without and with optimum Nano-particles were studied using optical microscopy and scanning electron microscopy (SEM) and the fractured surface of the weld obtained from tensile testing was investigated for the samples without Nano particles and the optimum sample with Nano materials were studied. The results show that Nano-particles added to the weld pool penetrated into the weld zone and helped in formation of acicular ferrite in the microstructure. Based on the results obtained, ultimate tensile strength and percentage of elongation of samples without Nano-particles and with optimum Nano-particles were increased from 387 MPa and 6.8% to 408 MPa and 13.6%, respectively. In addition, the average hardness of the weld metal without Nano-particles and with optimum Nano-particles were increased from 158 VHN to 172 VHN respectively

This paper reports the applicability of fuzzy logig (FL) to predict the hardness of melt zone (HMZ) during the gas metal arc welding (GMAW) process, which is affected by the combined effect of ZrO2 nano-particles and welding input parameters. The arc voltage, welding current, welding speed, stick-out, and ZrO2 nano-particles were used as the input parameters and HMZ as the response to develop FL model. The predicted results from FL were compared with the experimental data. The most important input parameter affecting the HMZs was the addition of ZrO2 nanoparticle coatings with a thickness of 1 mm, which increased the hardness from 78 to 84 HRB. The correlation factor value obtained was 99.98% between the measured and predicted values of HMZ. The results showed that FL is an accurate and reliable technique for predicting HMZ because of its low error rate. Also, the presence of ZrO2 nano-particles in the weld pool has increased the penetration up to 2 times.

In this research, coating of ZrO2 nano-particles with different thicknesses on the surface of Ti-6Al-4V sheet was used for surface melting using by TIG welding process and without filler metal. After the melting operation in the presence of zirconia nanoparticles, the depth of the melting surface and hardness was measured. Also, using of optical microscope and SEM microscope, the microstructure of the different melting surface areas was investigated. To defined the presence of nanoparticles in the melted area, chemical analysis of EDS and X-ray diffraction pattern (XRD) have been carried out to identify the phases formed in the samples from melting operations. Nano particles penetrate deeply from the melt surface, resulting in a acicular structure that is interlocked. The causes of the formation of this structure is the presence of nanoparticles during surface melting operations And the formation of nucleation centers to create appropriate buds in suitable places. The applied nanoparticles on the surface with the surface tension shift gradient mechanism have increased the depth of the penetration of the melt surface. The most important effect of nanoparticles on the surface was hardness. The hardness result is in the sample, which has the highest nanoparticles. The hardness of these samples is 2.5 times higher than the base metal. With the constant consideration of welding parameters and increasing the ZrO2 nanoparticles, the depth of the melting area and the hardness of the melting region increased compared to the base metal.

This paper investigates the effect of boehmite nano-particles surface adsorbed byboric acid (BNBA) along with other input welding parameters such as welding current, arc voltage, welding speed, nozzle-to-plate distance on weld penetration. Weld penetration modeling was carried out using multi-layer perceptron artificial neural network (MPANN) technique. For the sake of training the network, 70% of the obtained data from experimentation using five-level five-factor central composite rotatable design of experiments was used. The performance of the network shows a good agreement between the experimental data and the data obtained from the network. Hence, it is to be concluded that MPANN is highly accurate in predicting the weld penetration in SAW process.

One of the quality characteristics of welded joints in gas metal arc welding (GMAW) is weld height (WH). This paper highlights an experimental study carried out to develop a model using artificial neural network (ANN), to predict WH in GMAW in the presence of TiO2 nano-particles. For developing the model, the arc voltage, welding current, welding speed, percentage of Ar in Ar-CO2 mixture and thickness of TiO2 nano-particles were considered as input parameters and WBH as the response. A Doehlert design matrix was employed in the experiments to generate experimental data. The ANN model was developed and validated by conducting five extra runs. The remarkable outcome of this study is the mechanism of arc constriction due to interacting effects between welding input parameters and TiO2 nano-particles. Moreover, the results showed that increasing thickness of TiO2 nano-particles up to almost 0.9 mm increased weld height whereas, its further increase up to 1.0 mm, decreased weld height subsequently. In fact, this variation in weld height could be due to thermal dissociation of TiO2 nano-particles and CO2 releasing oxygen onto weld pool surface, which influenced its surface tension and consequently, changed direction of the Marangoni convection of fluid flow in weld pool and as a result, affected WH. For ANN technique, MSEtrain=0.0066, MSEvalidation=0.0063 and MSEtest=0.0093. Finally, it is to be concluded that ANN is an accurate technique for predicting weld height.