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Masood Aghakhani,Mohammad Reza Ghaderi,Maziar Mahdipour Jalilian,Ali Ashraf Derakhshan 대한기계학회 2013 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.27 No.7
Submerged arc welding (SAW) is a high-quality arc welding process used in heavy industries for welding thick plates. In this process,selecting appropriate values for the input parameters is required for high productivity and cost effectiveness. A very important weld quality characteristic affected by welding input parameters is the hardness of melted zone (HMZ). This paper reports the applicability of fuzzy logic (FL) to predict HMZ in the SAW process which is affected by the combined effect of TiO2 nano-particles and welding input parameters. The arc voltage, welding current, welding speed, contact tip-to-plate distance, and TiO2 nano-particles were used as input parameters and HMZ as the response to develop FL model. A five-level five-factor central composite rotatable design (CCRD) was used in the experiments to generate experimental data. Experiments were performed, and HMZs were measured. The predicted results from FL were compared with the experimental data. The correlation factor value obtained was 99.99% 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.
Mohammad Reza Ghaderi,Masood Aghakhani,Amir Hossein Eslampanah,Kianoosh Ghaderi 대한기계학회 2015 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.29 No.1
We used a novel optimization algorithm based on the imperialist competitive algorithm (ICA) to optimize the deposition rate in thesubmerged arc welding (SAW) process. This algorithm offers some advantages such as simplicity, accuracy and time saving. Experimentswere conducted based on a five factor, five level rotatable central composite design (RCCD) to collect welding data for depositionrate as a function of welding current, arc voltage, contact tip to plate distance, welding speed and thickness of TiO2 nanoparticles coatedon the plates of mild steel. Furthermore, regression equation for deposition rate was obtained using least squares method. The regressionequation as the cost function was optimized using ICA. Ultimately, the levels of input variables to achieve maximum deposition ratewere obtained using ICA. Computational results indicate that the proposed algorithm is quite effective and powerful in optimizing thecost function.