ArticleScientific reports2026
Multi-criteria wear performance optimization of waste-reinforced hybrid aluminum alloys via RSM, Fuzzy AHP, and Fuzzy WASPAS.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
This study aims to address the challenges of improving the wear performance of aluminum alloys by incorporating waste-derived materials, such as Fly Ash and SiC, to enhance sustainability in industrial applications. The motivation behind this work is to explore how hybrid aluminum alloys reinforced with waste materials can achieve higher wear resistance, reduced friction, and improved sustainability in machining processes. The objective of this study is to optimize the wear performance of these alloys using a combination of Response Surface Methodology (RSM), Fuzzy Analytic Hierarchy Process (AHP), and Fuzzy Weighted Aggregated Sum Product Assessment (WASPAS) to determine the optimal experimental conditions. The input process parameters include load, sliding speed, and wear distance, while the output responses focus on wear rate, coefficient of friction, and wear resistance. The optimization process, aided by RSM, identified the key factors affecting wear performance and enabled the effective selection of material compositions. The validation approach included the use of a pin-on-disk apparatus to test wear under varying experimental conditions, with results validated through statistical methods like Analysis of Variance (ANOVA). The findings showed that Sample B, composed of 78% Al, 5% Fly Ash, 5% SiC, 2% Mn, and 10% Zn, exhibited a 23% reduction in wear rate, a 15% improvement in the coefficient of friction, and a 17% increase in wear resistance compared to Sample A. The optimized process parameters for best conditions were load: 30 N, sliding speed: 1.5 m/s, and wear distance: 1000 m, resulting in the best wear resistance and minimal wear rate. In contrast, the worst conditions with load: 50 N, sliding speed: 3.0 m/s, and wear distance: 1500 m showed significant wear degradation. These results highlight the potential of waste-derived reinforcements for improving wear performance and supporting sustainable manufacturing practices in various industries. By integrating waste-derived materials like Fly Ash and SiC, the study demonstrates how such materials can not only improve mechanical properties but also contribute to sustainability, making these alloys suitable for energy-efficient machining applications.
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