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<p>In-situ composites are vital for achieving superior tribological properties due to their distinct advantages. This study focuses on developing ZA-27 based composites reinforced with titanium carbide&#xa0;(TiC) particles. The abrasive wear performance of the ZA-27/TiC composites was systematically evaluated. The findings revealed that increasing the TiC content significantly enhanced the wear resistance of the matrix alloy. Among the test samples, the ZA-27/10% TiC composite&#xa0;exhibited the lowest surface roughness (Ra = 1.488 µm) under a sliding speed of 3 m/s, an applied load of 15 N, and a grit size of 63 µm. The study introduces the Bat Algorithm, Butterfly Optimization Algorithm, and Genetic Algorithm for modeling the wear rate of test materials. Furthermore, a comparative analysis is conducted to evaluate the performance of these three algorithms, highlighting their effectiveness in enhancing wear performance. The results indicate that the Genetic Algorithm achieves the lowest validation mean squared error (MSE) of 0.0772 among the evaluated algorithms. Among all the test samples, the ZA-27/10% TiC composite&#xa0;demonstrated the most outstanding wear performance, making it well-suited for tribological applications such as bearings.</p>

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Experimental investigation and advanced tribological modeling of ZA-27/TiC composites using nature-inspired algorithms

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月30日

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  • Khursheed Ahmad Sheikh,
  • Mohammad Mohsin Khan

摘要

In-situ composites are vital for achieving superior tribological properties due to their distinct advantages. This study focuses on developing ZA-27 based composites reinforced with titanium carbide (TiC) particles. The abrasive wear performance of the ZA-27/TiC composites was systematically evaluated. The findings revealed that increasing the TiC content significantly enhanced the wear resistance of the matrix alloy. Among the test samples, the ZA-27/10% TiC composite exhibited the lowest surface roughness (Ra = 1.488 µm) under a sliding speed of 3 m/s, an applied load of 15 N, and a grit size of 63 µm. The study introduces the Bat Algorithm, Butterfly Optimization Algorithm, and Genetic Algorithm for modeling the wear rate of test materials. Furthermore, a comparative analysis is conducted to evaluate the performance of these three algorithms, highlighting their effectiveness in enhancing wear performance. The results indicate that the Genetic Algorithm achieves the lowest validation mean squared error (MSE) of 0.0772 among the evaluated algorithms. Among all the test samples, the ZA-27/10% TiC composite demonstrated the most outstanding wear performance, making it well-suited for tribological applications such as bearings.