Research demonstrates improved prediction accuracy in microhardness and element transfer via artificial neural network models in coatings.
This research investigates the impact of shielded metal arc welding (SMAW) electrode coating flux on microhardness, microstructure, and element transfer. A mixture design approach was employed to formulate the flux compositions, and multi-pass bead-on-plate experiments were conducted. Regression analysis was used to evaluate the relationship between microhardness and weld bead chemistry. The results indicated that the presence of CaO, Al 2 O 3 , and SiO 2 positively influences the transfer of nickel from the electrode to the weld pool. Increased levels of Na 3 AlF 6 , SiO 2 , and CaO positively affected chromium transfer, while Al 2 O 3 led to a decrease in chromium transfer. Manganese transfer was enhanced by CaO and Na 3 AlF 6 , but reduced with higher concentrations of Al 2 O 3 and SiO 2 . Furthermore, an increase in SiO 2 raised the oxygen potential, contributing to a loss of manganese in the Weld. The results indicate that increasing Al 2 O 3 positively affects iron transfer, while higher concentrations of Na 3 AlF 6 , SiO 2 , Al 2 O 3 , and CaO improve niobium transfer. Microhardness increases with higher levels of CaO, Na 3 AlF 6 , and SiO 2 , although Al 2 O 3 has a reducing effect. The welds were further analyzed through microstructural analysis. In addition, artificial neural network (ANN) models were developed, demonstrating improved prediction accuracy compared to traditional approaches.
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Gupta et al. (2025) studied this question.
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