Modeling the weld bead penetration in the presence of Cr₂O₃ nanoparticles in the submerged arc welding process using a modified neuro-fuzzy system

Submerged arc welding (SAW) is a widely used technique in various industries for welding thick plates. The quality of welded joints produced by this process depends on the selection of appropriate parameters that yield weldments with desirable mechanical properties. Among these parameters, weld bead penetration is a crucial indicator of weld quality. In this study, the effect of arc voltage, welding current, distance between the contact tip and the workpiece, and arc travel speed in the presence of Chromium oxide (Cr₂O₃) nanoparticles on penetration has been investigated. In addition, a new hybrid optimization algorithm has been developed by combining the differential evolution (DE) and wingsuit flying search (WFS) algorithms. The hybrid algorithm is evaluated by using standard benchmark functions and combined with the adaptive network-based fuzzy inference system (ANFIS) to create a new model. The model predicts weld bead penetration in the SAW process based on input parameters. The proposed hybrid algorithm improved the effectiveness of the main ANFIS in predicting weld bead penetration. The results showed that the addition of Cr₂O₃ nanoparticles to the weld pool increased its penetration.