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<p>The growing demand for wear-resistant materials have driven investigators to create substitute materials which incorporate various fillers and reinforcements. Recent research indicates that the amalgamation natural fibers with ceramic fillers had produced HCs with enhanced tribological properties for aerospace applications and automotive. Despite the drawbacks of natural fibers, the synergistic impact of ceramics and natural fibers, when paired with appropriate multi-response optimization approaches can mitigate these limits and yield a composite with superior tribological properties and mechanical at a reduced expenditure. This study investigates wear characteristics of a hybrid composite composed of zinc oxide (ZnO) and luffa cylindrica/cassia fistula bark fibers. The design of tests utilized Taguchi L27 orthogonal array to achieve smallest coefficient of friction (COF) and wear rate. This research employed multicriteria decision-making (MCDM) methodologies to address the challenge of selecting optimal estimates across diverse opportunities. The ideal combinations for favorable wear performance of the composite were determined utilizing a performance selection index (PSI) methodology. The research focuses on the creation and validation of a predictive model in machine learning utilizing random forest (RF) algorithm, designed to predict two essential behaviour metrics: the coefficient of friction (COF) and the specific wear rate (SWR). This investigation revealed that the peak performance selection index (PSI) and random forest (RF) values were 0.8761 and 0.7695, respectively, for determining the ideal operating condition of 30 wt% reinforcement, parameters of 5 wt% ZnO, 18&#xa0;N load, 800&#xa0;m sliding distance and 0.6&#xa0;m/s sliding speed. The optimal mix of particle hybrid composite were evaluated for mechanical properties under both wet and dry circumstances.</p>

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Multi-response optimization and machine learning prediction of tribological and mechanical performance of ZnO-modified luffa cylindrica/cassia fistula fiber hybrid composites

  • 期刊论文

  • 发表日期：

    2026年5月8日

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  • Prasanth P.,
  • Kannakumar K.,
  • Santhiya N.,
  • Ch V. S. Satyamurty,
  • Karthikeyan R.,
  • Tejas Rajendra Patil

摘要

The growing demand for wear-resistant materials have driven investigators to create substitute materials which incorporate various fillers and reinforcements. Recent research indicates that the amalgamation natural fibers with ceramic fillers had produced HCs with enhanced tribological properties for aerospace applications and automotive. Despite the drawbacks of natural fibers, the synergistic impact of ceramics and natural fibers, when paired with appropriate multi-response optimization approaches can mitigate these limits and yield a composite with superior tribological properties and mechanical at a reduced expenditure. This study investigates wear characteristics of a hybrid composite composed of zinc oxide (ZnO) and luffa cylindrica/cassia fistula bark fibers. The design of tests utilized Taguchi L27 orthogonal array to achieve smallest coefficient of friction (COF) and wear rate. This research employed multicriteria decision-making (MCDM) methodologies to address the challenge of selecting optimal estimates across diverse opportunities. The ideal combinations for favorable wear performance of the composite were determined utilizing a performance selection index (PSI) methodology. The research focuses on the creation and validation of a predictive model in machine learning utilizing random forest (RF) algorithm, designed to predict two essential behaviour metrics: the coefficient of friction (COF) and the specific wear rate (SWR). This investigation revealed that the peak performance selection index (PSI) and random forest (RF) values were 0.8761 and 0.7695, respectively, for determining the ideal operating condition of 30 wt% reinforcement, parameters of 5 wt% ZnO, 18 N load, 800 m sliding distance and 0.6 m/s sliding speed. The optimal mix of particle hybrid composite were evaluated for mechanical properties under both wet and dry circumstances.