부산대학교 도서관

In the present study, a predictive analysis was performed to investigate the effect of droplet size, section size and type of the primary and secondary AlSi10MnMg alloys manufactured by vacuum-assisted high pressure die casting on wettability of the cast samples with water, since wettability influences corrosion resistance. Additionally, corrosion resistance of samples was studied using a linear polarization experiment. Contact angle (CA) measurements were performed on the specimens using a goniometer. An Artificial Neural Network was then developed to predict the contact angle values as a function of the predictor variables. The developed model was able to predict unseen CA values with excellent accuracy with the Pearson correlation coefficient of 0.96 between the predicted and observed CA. The modeling results show that the type of alloy (primary or secondary) is the most significant factor affecting CA, where almost 80% of CA variation is the result of changing the type of alloy. Confocal microscopy images demonstrate that this is attributed to the change in the heterogeneity of the surface, which affects contact angle values. The corrosion studies reveal that corrosion resistance is dependent on the type of alloy and surface roughness. The primary alloy possesses more corrosion resistance than the secondary alloy. This is due to the larger fraction of intermetallic compounds in the microstructure of the secondary alloy, which serve as galvanic sites in the corrosion reaction accelerating corrosion rate. Moreover, the non-uniformity induced by larger surface roughness is detrimental to the corrosion resistance of the samples. These results indicate that the data-driven approach used in this research is very promising not only to predict the performance, but also to optimize and design high-performance corrosion resistant surfaces of cast aluminum alloys.