Mechanical and corrosion characterization of aluminium 6063 composite reinforced with teak wood saw ash
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Chithambaram Karunanithi
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Utkrishth Nandan
Abstract
Aluminium alloy is valued for its recyclability, lightweight, and strength, making it ideal for architectural and vehicle applications, while metal matrix composites further enhance its performance. Teak wood resists decay and insects, making it a durable choice for outdoor furniture and construction. Its high tensile strength allows it to support heavy loads, making it suitable for demanding structural applications. This study investigates the use of the stir casting method to reinforce Al6063 alloy with the teak wood ash powder in varying proportions (2 %, 4 %, and 6 %). Mechanical characterizations were performed including tensile, compression and hardness tests along with a 96-h immersion corrosion test in NaCl solution to determine corrosion rates. Characterization techniques such as X-ray diffraction, scanning electron microscopy, field emission scanning electron microscopy were utilized to examine surface morphology and tensile fractography. Among the different proportions, the sample with 2 % reinforcement exhibited the best tensile property of 175 MPa, while the 4 % reinforcement sample showed the highest compression strength of 466 MPa. Micro-CT analysis of porosity revealed that the 2 % reinforced composite exhibited the lowest porosity percentage, measuring 0.45 %, in comparison to the other reinforcement proportions. The corrosion test results revealed that the 2 % reinforcement sample had the lowest corrosion rate at approximately 2.313 × 10−4 (mm/y), whereas the 6 % reinforcement sample had the highest corrosion rate at about 3.161 × 10−3 (mm/y). The modifications in Al6063 with teak wood ash powder lead to grain refinement and the creation of a more consistent and tightly packed microstructure, leading to superior strength.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: The authors are grateful to Vellore institute of technology (VIT) and Department of Science and Technology, new Delhi, India for providing financial support to acquire “Micro CT Scan Facility” through “Promotion of University Research and Scientific Excellence (PURSE)” under grant No. SR/PURSE/2020/34 (TPN 56960) and carry out the necessary facilities for this research work.
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Data availability: Not applicable.
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