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

Abstract

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.

Statistical analysis and ANN modelling of tribological performance of aluminium hybrid composites fabricated by PM method

“…The histogram using the standardised residual in this study suggests that there is reduced skewness and no outliers. The residuals have been seen from the smallest to the greatest range, confirming that the produced results are accurate and precise [3,30,31].…”

Optimizing strength of directly recycled aluminum chip-based parts through a hybrid RSM-GA-ANN approach in sustainable hot forging

“…Modeling and optimizing the processing parameters and chip morphology can significantly contribute to improving the chip-based recycled strength. Recently, there has been a growing interest in utilizing advanced techniques, such as Response Surface Methodology (RSM) and Machine Learning (ML), to model and optimize the performance of manufacturing processes [23][24][25][26][27]. The aluminum waste recycling parameters have been modeled using RSM [10,11,28,29] and ANN [26,[30][31][32][33][34].…”

“…Recently, there has been a growing interest in utilizing advanced techniques, such as Response Surface Methodology (RSM) and Machine Learning (ML), to model and optimize the performance of manufacturing processes [23][24][25][26][27]. The aluminum waste recycling parameters have been modeled using RSM [10,11,28,29] and ANN [26,[30][31][32][33][34]. Moghri et al [35] reported that RSM and genetic algorithm effectively identified the optimum process variables for maximum tensile modulus and tensile strength of PA-6/clay nanocomposite.…”

Prediction of age-hardening behaviour of LM4 and its composites using artificial neural networks

“…The results determined that the trained model effectively predicted the mass loss with the least amount of error. Mohsin et al [15] used ANN to predict the tensile properties of Al composite. Pressure and wt% of reinforcement were used as input parameters, UTS and % elongation were used as output parameters.…”