Combined effect of TiO2 nanoparticles and input welding parameters on the weld bead penetration in submerged arc welding process using fuzzy logic

These days, the trend in every manufacturing industry including welding is to automate the processes in order to increase productivity. To achieve this objective, it is therefore necessary to make use of models to relate the input parameters with the responses. This paper reports on the applicability of fuzzy logic to predict the weld bead penetration in submerged arc welding process as affected by input welding parameters. Fuzzy logic is a computer technique which allows expressing, evaluating, and simplifying complexities in regard to the relationships in a process by describing the dependencies between output and input parameters in a linguistic form. To develop the fuzzy logic model, the arc voltage, welding current, welding speed, electrode stick-out, and thickness of TiO2 nanoparticles were taken as the input parameters and the weld bead penetration as the response. In order to generate experimental data, a five-level five-factor rotatable central composite design of experiments was employed. Experiments were performed, and the weld bead penetrations were measured. The predicted results using fuzzy logic were compared with the experimental ones. The correlation coefficient value obtained was 99.99 % between the measured and predicted values of weld bead penetration. The results show that the fuzzy logic is an accurate and reliable technique used in predicting the weld bead penetration due to its low error rate.