Effect of laser parameters on melting ratio and temperature distribution in dissimilar laser welding of brass and SS 308 using the artificial neural network model

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.

Effect of anodic potential on the characteristics of passive films grown on a brass alloy in a soil environment

“…Cu-Zn binary alloys are applied for coinage, chip design for computers, and heat exchanger and exchange pipes in refrigeration systems due to their appealing visual appearance, antibacterial, corrosion resistance, high thermal conductivity, and low cost. [1][2][3] However, for brass metal cultural relics, whether in excavation or in the later preservation process, it is inevitable that corrosion or continuous corrosion will occur on the surface of the alloy. It has a major impact on the cultural value and historical significance of the metal cultural relics.…”