Development and Characterization of Al6065 Aluminum Matrix Composites Reinforced with Almond Shell Ash |
Author(s): |
| D Narasimha Murthy , Seshadri Rao Gudlavalleru Engineering College, Gudlavalleru; Ujwal Teja Mallampalli, Seshadri Rao Gudlavalleru Engineering College, Gudlavalleru; A. Raheem, Seshadri Rao Gudlavalleru Engineering College, Gudlavalleru; A. Ram Kumar , Seshadri Rao Gudlavalleru Engineering College, Gudlavalleru |
Keywords: |
| Aluminum Matrix Composites, Silicon Carbide, Cenosphere, Corn Cob Ash, Stir Casting, Ultrasonic Casting, Wear Behaviour, Corrosion Resistance, ANFIS |
Abstract |
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Aluminum matrix composites (AMCs) have gained considerable attention in recent years owing to their superior strength-to-weight ratio, enhanced wear resistance, and improved corrosion performance compared to conventional alloys. This study presents a comprehensive comparative analysis of aluminum-based composites reinforced with silicon carbide (SiC), cenospheres, and corn cob ash (CCA), fabricated using both conventional stir casting and ultrasonic-assisted stir casting techniques. The influence of reinforcement type, weight fraction, and processing method on key properties such as tensile strength, hardness, corrosion resistance, and tribological behavior is systematically evaluated. The results indicate that the incorporation of hard ceramic particles significantly enhances tensile strength and hardness through effective load transfer and grain refinement mechanisms. However, excessive reinforcement leads to particle agglomeration, resulting in localized defects and reduced mechanical performance. Corrosion resistance is observed to improve due to the formation of a stable oxide layer and improved interfacial bonding. In terms of tribological performance, optimal wear resistance is achieved at moderate reinforcement levels, particularly in composites containing agro-waste-based reinforcements such as corn cob ash. Furthermore, the application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) demonstrates high accuracy in predicting wear behavior, highlighting its potential for advanced material design and optimization. |
Other Details |
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Paper ID: IJSRDV14I20191 Published in: Volume : 14, Issue : 2 Publication Date: 01/05/2026 Page(s): 271-276 |
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