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

响应面法 材料科学 极限抗拉强度 去壳 微观结构 复合数 抗弯强度 抗压强度 复合材料 合金 陶瓷 棕榈仁 冶金 数学 化学 棕榈油 统计 生物 植物 食品科学
作者
Abayomi Adewale Akinwande,Olanrewaju Seun Adesina,Adeolu Adesoji Adediran,Oluwatosin Abiodun Balogun,David Mukuro,Oluwayomi Peter Balogun,Kong Fah Tee,M. Saravana Kumar
出处
期刊:Ceramics [MDPI AG]
卷期号:6 (1): 386-415 被引量:21
标识
DOI:10.3390/ceramics6010023
摘要

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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