Predicting the combined effect of TiO2 nano-particles and welding input parameters on the hardness of melted zone in submerged arc welding by fuzzy logic

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.