Modeling of Penetration Depth in Submerged Arc Welding UsingArtificial Neural Network

The penetration depth, which is the distance from the surface of the plate to the bottom ofthe pool or the bottom edge where melting took place, will have a decisive importance inthe strength of the weld metal. Submerged arc welding is a manufacturing process that isdirectly affected by various input parameters and interactions, and these effects directlyaffect the penetration depth. This research used an artificial neural network with twohidden layers to find the relationship between process inputs and their effects on weldpenetration depth. Arc voltage (V), electric current intensity (I), electrode stick-out (N),welding speed (S), and the thickness of the layer of nanoparticles (F) were selected asinput layer neurons and penetration depth as output layer neurons. Also, the investigationof the effect of the input parameters on the penetration depth showed that the increasedintensity of the electric current increases the heat input to the welding pool. This, inaddition to the rise in the melting of the base metal, also increases the penetration depth.Increasing the arc voltage increases the amount of heat input to the welding pool, but themelting speed of the electrode does not change much, so the penetration depth increasesslightly.