Prediction of Weld Bead Formation of Duplex Stainless Steel Fabricated by Wire Arc Additive Manufacturing Based on the PSO-BP Neural Network

Abstract

Duplex stainless steel was formed through welding wire and arc additive manufacturing (WAAM) using tungsten inert gas. The effects of wire feeding speed (WFS), welding speed (WS), welding current, and their interaction on the weld bead width and height were discussed. Back-propagation (BP) neural network algorithm prediction model was established by taking the bead width and height as the output layer, and the network weight and threshold values were optimized using the particle swarm optimization (PSO) algorithm to obtain the prediction model of bead width and height. The predicted results were verified by experiments. Results show that the weld bead width increases with the increase in WFS and the welding current and decreases with WS. The smaller the WFS, the faster the WS, which is beneficial for the generation of equiaxed crystals. The smaller the welding current, the faster the cooling speed of the metal melt, which is conducive to the formation of dendrites. The interaction among WS, wire feed speed, and welding current has a significant effect on the bead width. The weld bead height is positively correlated with the wire feed speed and negatively correlated with the WS and current. The interaction between the wire feed speed and WS is significant. The optimized WAAM process parameters for duplex stainless steel are a wire feed speed of 200 cm/min, WS of 24 cm/min, and welding current of 160 A. The maximum error of the BP neural network in predicting the weld bead width and height is 7.74%, and the maximum error between the predicted and experimental values of the BP-PSO neural network is 4.27%. This finding indicates that the convergence speed is fast, improving the prediction accuracy.

Keywords

duplex stainless steel / wire arc additive manufacturing / bead forming / prediction model / neural network

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