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Optimization of Tribological Properties of Powder Metallurgy-Processed Aluminum7075/SiC Composites Using ANOVA and Artificial Neural Networks

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Abstract

This paper investigates the mechanical and tribological characteristics of aluminum alloy 7075 (AA7075) composites reinforced with (2, 4 & 6) wt% of silicon carbide fabricated using powder metallurgy technique. The microstructural examination confirms the even distribution of SiC particles in the AA7075 matrix. An increase in hardness and density is observed with an increase in reinforcement Wt%. This is mainly due to the formation of interfacial bonds. Tribological examinations are carried on pin-on-disk equipment under dry conditions at three different loads (10, 20, and 30 N), three sliding distances (500, 1000, and 1500 m) at a constant sliding speed of 1 m/s. Volume loss is considered as output response in this research. Experiments have been conducted using L27 complete factorial design. The effect of sliding factors on volume loss is studied using analysis of variance and artificial neural networks. Analysis of variance (ANOVA) identifies load as a primary contributing parameter for volume loss followed by sliding distance and wt% of SiC. Load at level-1(10 N), the sliding distance at level-1(500 m), and wt% of SiC at level-3(6wt%) are identified as optimal sliding parameters for minimum volume loss. The application of artificial neural networks resulted in MSE, RMSE, and MAE values of 0.11380, 0.3373, and 0.1768, respectively are extremely low, which implies that the predicted results from the model are in good agreement.

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Authors and Affiliations

  1. Department of Mechanical Engineering, GITAM (DEEMED TO BE UNIVERISTY), Hyderabad, 502329, Telangana, India

    Mulugundam Siva Surya, A. Kiran Kumar & V. K. Sridhar

  2. Department of Mechanical Engineering, JNTU Anantapur, Ananthapuramu, Andhra Pradesh, 515002, India

    G. Prasanthi

  3. Department of Mechanical Engineering, NIT Andhra Pradesh, Tadepalligudem, Andhra Pradesh, 534101, India

    S. K. Gugulothu

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  1. Mulugundam Siva Surya
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  2. G. Prasanthi
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  5. S. K. Gugulothu
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Surya, M.S., Prasanthi, G., Kumar, A.K. et al. Optimization of Tribological Properties of Powder Metallurgy-Processed Aluminum7075/SiC Composites Using ANOVA and Artificial Neural Networks. J Bio Tribo Corros 7, 161 (2021). https://doi.org/10.1007/s40735-021-00600-w

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  • DOI: https://doi.org/10.1007/s40735-021-00600-w

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