4.6 Article

Optimizing reinforcement quantity and synthesizing parameters of iron/fly ash/CNT nanocomposite

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SPRINGER LONDON LTD
DOI: 10.1007/s00170-023-11842-y

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Sintering; Hardness; Compressive strength; Orthogonal array; Mathematical model

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The study focuses on the fabrication of a nanocomposite material with improved hardness and compressive strength by incorporating fly ash nanopowder and carbon nanotube (CNT) reinforcement into an iron matrix via the sintering process. Parameter optimization using the Taguchi technique with an L36 orthogonal array was conducted, and the optimized factors for maximum hardness and compressive strength were determined to be 2.5% fly ash volume percentage and 5% 50 nm size CNT volume percentage. Composite samples were produced using the identified optimal parameters, and a mathematical model was developed to predict the hardness and compressive strength of various combinations of factors. The models exhibited good agreement with the experimental results, as indicated by an R-2 value greater than 95%.
The nanocomposite consists of an iron matrix with fly ash nanopowder and carbon nanotube (CNT) reinforcement through the sintering process. Parameter optimization for enhancing hardness and compressive strength. There were six factors considered such as volume percentage of fly ash (2.5% and 5%) and CNT (2.5% and 5%), size of the CNT (50 nm and 100 nm), sintering temperature (800 & DEG;C, 900 & DEG;C, and 1000 & DEG;C), pressure (500 MPa, 600 MPa, and 700 MPa) and time (15 min, 30 min, and 45 min). Taguchi technique with L36 orthogonal array considered for factors optimization with the superior consequence is convincible condition. The optimized factors for maximum hardness as well as the compressive strength were as follows: fly ash 2.5 vol% and 50 nm size CNT of 5 vol%. The composite samples were produced employing the sintering process. The optimal parameters are the temperature of 1000 & DEG;C, sintering pressure of 600 MPa and 15 min. The mathematical model was developed for predicting the hardness and compressive strength for any combinations of factors. The R-2 value greater than 95% meant that the predicted models have good agreement with the experimented results and were accurate.

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