Modeling and Optimization of Penetration Depth Using Genetic Algorithm,Focusing on Submerged Arc Welding

Submerged arc welding is a production process directly affected by various input parameters andinteractions, and these effects will directly affect the output results. This research investigated theimpact of arc voltage, electric current intensity, electrode stick-out, welding speed, and the thickness ofthe layer of nanoparticles on the penetration depth. In this study, Central Composite Rotatable Designand analysis of variance were used to examine the effect of input variables on output results and reducetime and costs. Ultimately, the Genetic Algorithm was used to find the optimal levels of selectedparents. The results indicate that increasing the arc voltage and the electric current intensity due to moreinput heat transfer caused more melting of the base metal and thus increased the penetration depth. Thedifference in thermal conductivity between the base metal and the nanoparticles results in less heattransfer to the inner layers of the workpiece; therefore, the high thickness of the layer of nanoparticlesreduces the penetration depth. On the other hand, increasing the thickness of the layer of nanoparticlesincreased the weld’s height and width.

Keywords:

Nanoparticles , Modeling and optimization , Central composite rotatable design (CCRD) , Submerged arc welding (SAW).