In this research work, aluminum alloy (Al-20Fe-5Cr) matrix-based aluminum oxide (Al 2 O 3 ) reinforced composites were developed through the powder metallurgy (P/M) process. Effect of compaction pressure (200, 250 & 300 MPa) and wt.% of Al 2 O 3 (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 Al 2 O 3 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 Al 2 O 3 up to 20 wt.% and then declined sharply for the 30 wt.% of Al 2 O 3 at all compaction pressures. The highest tensile strengths were found for each wt.% of Al 2 O 3 at compaction pressure 300 MPa compare to other compaction pressures. Tensile strength increased from 105 to 158 MPa with the addition of 20 wt.% Al 2 O 3 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 Al 2 O 3 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 Al 2 O 3 . The statistical analysis using ANOVA revealed that the compaction pressure is the primary control factor influencing tensile strength by 90.3%. The feedforward 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.

In the present research work predicted properties of Aluminium Alloy Composites prepared through powder metallurgy technique have been examined using Artificial Neural Network (ANN) approach. The aluminium alloy (Al-20Fe −5Cr) matrix reinforced with aluminium oxide (Al 2 O 3 ) varying from 0-30 wt% with a step of 10 wt% were prepared. The green compacts prepared at three different compaction pressures viz. 470, 550 and 600 MPa were sintered at 440 °C for 30 min. Pin-on-disc test was performed to evaluate wear loss of the composites prepared. The influence of alumina varying weight percentage and different compaction pressure had been analyzed and tests were performed according to full factorial design. Analysis of variance (ANOVA) had been employed to predict the percentage contribution of various process parameters. The results indicated that the wear loss was mainly influenced by alumina percentage followed by compaction pressure by 92% and 8% respectively. For modeling and prediction of wear loss, a feed forward back propagation neural network was constructed and compared with experimentally calculated values. Both experimental and ANN predicted values of wear loss were in close correlation with each other with approximately (1-3) % error. RECEIVED