Abstract
In this study, according to the experimental results related to the dissimilar laser welding of brass-stainless steel 308, a performance approximation method called artificial neural network (ANN) was used. Welding speed, focal length, peak power, pulse width, and frequency were taken as input parameters, and temperature and melting ratio were considered as target characteristics. The ANN results were compared with the experimental results and the error percentage between them was obtained. Maximum relative errors were 9.63%, 10.55%, and 6.13% for brass alloy temperature, stainless steel, and melt ratio, respectively. Based on this comparison, the percentage of error between the experimental data and the ANN was at a reasonable level; so, this numerical method could be used with low time and cost. Also, by considering seven and five neurons in the hidden layer, the lowest mean squared error was obtained for temperature and melting ratio, respectively.
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