Analysis and prediction of the tensile strength of aluminum alloy composite using statistical and artificial neural network technique

In this research work, aluminum alloy (Al-20Fe-5Cr) matrix-based aluminum oxide (Al₂O₃) reinforced composites were developed through the powder metallurgy (P/M) process. Effect of compaction pressure (200, 250 & 300 MPa) and wt.% of Al₂O₃ (0, 10, 20 & 30 wt.) on tensile strength and percentage elongation has been analyzed through statistical and artificial neural network techniques (ANN). The mixture of Al-alloy powder particles and Al₂O₃ particles were synthesized in a centrifugal ball mill for 20 min. Compaction of synthesized powder was carried in the standard tensile die using a uniaxial hydraulic pressing machine. Sintering was performed at temperature 580 ± 20 °C for one hour in an argon gas environment using an electric tubular furnace. It was found that tensile strength enhanced significantly with the addition of Al₂O₃up to 20 wt.% and then declined sharply for the 30 wt.% of Al₂O₃at all compaction pressures. The highest tensile strengths were found for each wt.% of Al₂O₃ at compaction pressure 300 MPa compare to other compaction pressures. Tensile strength increased from 105 to 158 MPa with the addition of 20 wt.% Al₂O₃ and decreased to 142 MPa for 30 wt.% at 300 MPa compaction pressure. The improvement resulted from better compaction, leading to more plastic deformation, better packing, and high effective contact area. However, the percentage of elongation decreased from 23.2% to 2.2% with an increment of wt.% of Al₂O₃ for compaction pressure 200 MPa, while for 300 MPa, its value drops from 25.8% to 6.5%. This depreciation can be reasoned for the reduction in ductile matrix content and dilute flowability of the Al matrix, which occurred due to brittle Al₂O₃. The statistical analysis using ANOVA revealed that the compaction pressure is the primary control factor influencing tensile strength by 90.3%. The feed-forward network with a back-propagating gradient-descent error minimization training approach and mean squared error (MSE) as performance function was employed to model and predict tensile strength. The developed 3-layered multilayer perceptron (MLP) with 2–10–2 network architecture established a correlation between the inputs and outputs with minimum error (MSE) below 1% and maximum correlation coefficient (R) close to 1.