Development of regression models to predict and optimize the composition and the mechanical properties of aluminium bronze alloy

In this present work, aluminium bronze was doped at a percentage of 1-10 chemical composition of alloying additives (V, Mn, Nb, Ni and Cr) prepared using a sand casting method. The study targeted at improving the mechanical properties of aluminium bronze with alloying additives and using response surface methodology to develop a predictive model. The statistical analysis was done singly, as the alloying elements were added separately into Cu-10%Al alloy. Five alloying elements under 11 experimental runs were designated as independent variables and mechanical properties namely., ultimate tensile strength, %elongation, hardness, and impact strength were set as the response variables in the experimental design matrix. The results obtained from mechanical analytical tests were optimized and a predictive regression model developed using optimal custom design of RSM-Design Expert software.  The developed model through statistical analysis of variance (ANOVA) revealed that the alloying elements significantly improved the mechanical properties haven shown a significant p-value of <0.05. The model effectively predicted an optimal composition factor level of the 3.00% vanadium, 1.00% manganese, 7.00% niobium, 2.00% nickel, and 9.00% chromium at the best desirability of 1.00. The predictive model developed in this work will help to achieve appropriate output for aluminium bronze component improvement.