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Utilization of ANN for the Prediction of Mechanical Properties in AlP0507-MWCNT-RHA Composites

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Abstract

Achieving optimal results for enhanced performance of aluminum metal matrix necessitated a substantial amount of effort, as it entailed investing additional resources in terms of time, finances, and rigorous testing. The objective of the proposed work is to examine and analyze the impact of various stir-casting parameters, including the proportion of reinforcement, stirring time, stirring speed, and processing temperature, on the composite AlP0507-RHA-MWCNT. The Taguchi technique is used to identify the optimized parameters for the optimization of toughness, tensile strength, and hardness of stir-casted composite specimens. The analysis of the signal-to-noise ratio (S/N ratio) reveals that the samples containing 6% RHA and 2% MWCNT, subjected to a stirring speed of 100 rpm, a stirring time of 10 min, and a processing temperature of 800 °C, exhibit the highest tensile strength. The study on the S/N ratio reveals that the samples containing 6% RHA and 2% MWCNT, subjected to a stirring speed of 100 rpm, a stirring time of 10 min, and a processing temperature of 850 °C, exhibit favorable hardness values. The S/N ratio indicates that the hybrid composite having 4% RHA and 2% MWCNT stirred for 5 min with 300 rpm and 800 °C processing temperature exhibits the highest toughness. Using ANN, the mean regression coefficient was found to be 0.99477, which was very close to 1, which shows a strong association between expected and observed experimental output.

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Abbreviations

AlP0507:

Aluminium P0507 alloy

RHA:

Rice husk ash

MWCNT:

Multiwall carbon nanotube

S/N ratio:

Signal-to-noise ratio

ANN:

Artificial neural network

HAMMC:

Hybrid aluminium metal matrix composites

UTS:

Ultimate tensile strength

ANOVA:

Analysis of variance

MLP:

Multi-layer perceptron

ASTM:

American Society for Testing and Materials

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Authors and Affiliations

  1. Department of Mechanical Engineering, Sharda University, Greater Noida, U.P, India

    Nitin Srivastava & Lavish Kumar Singh

  2. Department of Mechanical Engineering, Ajay Kumar Garg Engineering College, Ghaziabad, U.P, India

    Manoj Kumar Yadav

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  1. Nitin Srivastava
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  2. Lavish Kumar Singh
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Correspondence to Lavish Kumar Singh.

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Srivastava, N., Singh, L.K. & Yadav, M.K. Utilization of ANN for the Prediction of Mechanical Properties in AlP0507-MWCNT-RHA Composites. Met. Mater. Int. 30, 1106–1122 (2024). https://doi.org/10.1007/s12540-023-01552-1

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