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

• Published in: Advances in materials and processing technologies (Abingdon, England) England), 2022-10, Vol.8 (sup3), p.1227-1244, Article 1227
• Main Authors: Nwaeju, C.C., Edoziuno, F.O., Adediran, A.A., Nnuka, E.E., Akinlabi, E.T., Elechi, A.M.
• Format: Article
• Language: English
• Subjects: Alloying elements; mechanical properties; optimisation and predictive modelling; response surface methodology
• ISSN: 2374-068X; 2374-0698; 2374-0698
• DOI: 10.1080/2374068X.2021.1939556

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