Parameter Optimization of SAW in Hardfacing Process Using Hybrid Approach of Adaptive Stimulated Annealing and Neural Networks

Abstract

This paper details the application of ANN in hardfacing technique to determine the optimal process parameters for submerged arc welding (SAW). The planned experiments are conducted on the semiautomatic submerged arc welding machine. The relationships between process parameters (arc current, arc voltage, welding speed, electrode protrusion, and preheat temperature) and welding performance (deposition rate, hardness, and dilution) are established. A Adaptive Simulated Annealing (ASA) optimization algorithm with a performance index is then applied to the neural network for searching the optimal process parameters. Experimental results have shown that welding performance can be enhanced by using this new approach