Ceramics (Feb 2023)

Microstructure, Process Optimization, and Strength Response Modelling of Green-Aluminium-6061 Composite as Automobile Material

  • Abayomi Adewale Akinwande,
  • Olanrewaju Seun Adesina,
  • Adeolu Adesoji Adediran,
  • Oluwatosin Abiodun Balogun,
  • David Mukuro,
  • Oluwayomi Peter Balogun,
  • Kong Fah Tee,
  • M. Saravana Kumar

DOI
https://doi.org/10.3390/ceramics6010023
Journal volume & issue
Vol. 6, no. 1
pp. 386 – 415

Abstract

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The use of ashes derived from various waste sources as supplements to synthesized ceramic reinforcement in metal matrices has been established. However, studies involving a combination of particulates from three different sources are rare. In a bid to further knowledge in this aspect of research and develop a green aluminium composite for automobile applications, the present investigation studied the implication of adding palm kernel shell ash (PKA), rice husk ash (RHA), and waste steel particles (STP) to the morphology and strength behaviour of Al-6061-T6 alloy. The experimental design was undertaken via the Box–Behnken design (BBD) of the response surface method. A 4% STP at a constant dose was mixed with PKA and RHA at varying proportions and stirring temperatures according to the BBD. The experimental outcome revealed that the responses were greatly influenced by microstructural evolution. From the surface plots, 2–4% RHA and PKA enhanced tensile and flexural strengths, while 4–6% led to a decline in strength. Meanwhile, 2–6% of the particles are favourable to the enhancement of tensile and compressive strengths and moduli. Temperatures between 700 and 800 °C favored response improvement, whereas temperatures between 800 and 900 °C were detrimental to responses. Developed regression models for the responses were validated to be good representations of the experimental outcomes. The optimum mix was obtained at 4.81% PKA, 5.41% RHA, and a stirring temperature of 803 °C. The validation experiment conducted portrayed reliable responses with <5% deviation from the predicted values, thereby certifying the models to be statistically fit for future predictions.

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