Experimental and Regression-Based Wear Analysis of MWCNT Reinforced AA7075 Using Box-Behnken Design

The research analyzes the wear characteristics of MWCNT-reinforced AA7075 metal matrix composites under different combinations of MWCNT volume fraction (2–6 wt%), operating temperature (80–120°C) and applied force (40–60 N). The wear resistance of composites produced by stir-casting fabrication received analysis through ANOVA combined with regression modeling after testing their wear resistance properties. A combination of 6% reinforcement with 100°C temperature under 40 N load proved to be the optimal conditions according to the desirability function approach which led to a wear rate of 3.349 Nm/mm³ and 0.826 in desirability. The studies reveal that reinforcement percentage served as the key variable (p = 0.004) which decreased wear by 25% when using 2% MWCNTs. Performance outcomes were most significantly improved through moderation of temperature conditions at 100°C combined with loading at 40 N. A developed regression model demonstrated the capability to predict wear rates with less than 5% error accuracy following validation through experimental confirmation. The obtained results can directly help engineers build high-wear-resistant composites for industries focused on aerospace and automotive manufacturing.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-03-29
DOI
https://doi.org/10.5281/zenodo.15348294
Primary Topic
Energy Harvesting in Wireless Networks
Type
article
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article

Experimental and Regression-Based Wear Analysis of MWCNT Reinforced AA7075 Using Box-Behnken Design

Zenodo (CERN European Organization for Nuclear Research)
Energy Harvesting in Wireless Networks
article

Experimental and Regression-Based Wear Analysis of MWCNT Reinforced AA7075 Using Box-Behnken Design

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Abstract

The research analyzes the wear characteristics of MWCNT-reinforced AA7075 metal matrix composites under different combinations of MWCNT volume fraction (2–6 wt%), operating temperature (80–120°C) and applied force (40–60 N). The wear resistance of composites produced by stir-casting fabrication received analysis through ANOVA combined with regression modeling after testing their wear resistance properties. A combination of 6% reinforcement with 100°C temperature under 40 N load proved to be the optimal conditions according to the desirability function approach which led to a wear rate of 3.349 Nm/mm³ and 0.826 in desirability. The studies reveal that reinforcement percentage served as the key variable (p = 0.004) which decreased wear by 25% when using 2% MWCNTs. Performance outcomes were most significantly improved through moderation of temperature conditions at 100°C combined with loading at 40 N. A developed regression model demonstrated the capability to predict wear rates with less than 5% error accuracy following validation through experimental confirmation. The obtained results can directly help engineers build high-wear-resistant composites for industries focused on aerospace and automotive manufacturing.

Zenodo (CERN European Organization for Nuclear Research)
Institute of Engineering (NP), National Institution for Transforming India Aayog (IN)
Industry, innovation and infrastructure
Openalex Percentile: Top 57%
Energy Harvesting in Wireless Networks
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