ISSN:
1433-3015
Keywords:
Hardfacing
;
Neural network
;
Optimisation
;
Simulated annealing algorithms
;
Submerged arc welding
Source:
Springer Online Journal Archives 1860-2000
Topics:
Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
Notes:
Abstract In this paper, a feedforward neural network is used to model submerged arc welding (SAW) processes in hardfacing. 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, based on the neural network. A simulated annealing (SA) optimisation 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.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF01186928
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