TY - JOUR
T1 - Development of regression models to predict and optimize the composition and the mechanical properties of aluminium bronze alloy
AU - Nwaeju, C. C.
AU - Edoziuno, F. O.
AU - Adediran, A. A.
AU - Nnuka, E. E.
AU - Akinlabi, E. T.
AU - Elechi, A. M.
N1 - Funding information: The authors appreciate the funding opportunity from University of Johannesburg, South Africa.
PY - 2022/10/31
Y1 - 2022/10/31
N2 - 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.
AB - 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.
KW - Alloying elements
KW - mechanical properties
KW - optimisation and predictive modelling
KW - response surface methodology
UR - http://www.scopus.com/inward/record.url?scp=85112525721&partnerID=8YFLogxK
U2 - 10.1080/2374068X.2021.1939556
DO - 10.1080/2374068X.2021.1939556
M3 - Article
AN - SCOPUS:85112525721
SN - 2374-068X
VL - 8
SP - 1227
EP - 1244
JO - Advances in Materials and Processing Technologies
JF - Advances in Materials and Processing Technologies
IS - sup3
ER -