Study on mechanical, corrosion and fractured surface of n-Mn modified Al5052 alloy: an ANOVA and ANN prediction of wear rate

https://doi.org/10.3221/IGF-ESIS.79.03

Aluminum (Al) 5052 alloys are extensively utilized in marine environments, pressure vessels, and automotive parts due to their better strength and low density. However, more investigation is required to comprehend how the nanoscale manganese (n-Mn) element affects their mechanical, tribological, corrosion, and machinability. The development of Al5052 alloy modified with n-Mn by increasing the weight percentage in 0.25% increments from 0 to 1% through stir casting is the main focus of this study. The nano-composite showed notable improvements in hardness (23.72%), tensile strength (11.01%), and corrosion resistance (57.69%). However, increasing the n-Mn content also led to a 77.35% increase in machining force and a 69.23% increase in surface roughness (Ra). Taguchi and ANOVA approaches were used to analyze the effects of n-Mn (wt. %), sample temperature (ºC), and sliding distance (m) on wear loss (g). The improved alloy’s wear rate was largely influenced by its n-Mn concentration. Wear rate confirmation tests showed that maximum errors for the n-Mn modified Al5052 alloy were within permissible bounds. Additionally, the wear loss was successfully predicted using the proposed 3-4-1 Artificial Neural Network (ANN) model. For the wear attributes taken into consideration, the predicted performance metrics produced mean squared errors (MSE) of 4.925*10-5 and R-squared values (R2) of 0.9361.