Machine Learning and Predictions on Effect of Fiber Size, Hybridization on the Mechanical Properties of Coir/Luffa Reinforced Hybrid Composite

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@article{1703984,
      author = {Chibueze Ikechukwu Godswill, Anukwonke Maxwell .C., Imah Adindu, Umebamalu Chinedu, Otamiri Humble. C},
      title = {Machine Learning and Predictions on Effect of Fiber Size, Hybridization on the Mechanical Properties of Coir/Luffa Reinforced Hybrid Composite},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {6},
      number = {7},
      pages = {372-389},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1703984.pdf},
      abstract = {This study is to further investigate at what percentage of coir/luffa fibers will generate hybrid composite with the best mechanical behavior while maintaining 30% fiber weight with 70% epoxy resin polymer. The composites were produced by varying the fiber weight fractions and the fiber size. Each composite comprises 30% fiber and 70% epoxy resin. At corresponding fiber sizes of 200 ?m, 400 ?m, and 600 ?m, hybridization of the fibers was carried out in the following weight ratios: 0:30, 10:20, 15:15, 20:10, and 30:0 wt/wt of Coir/Luffa. When the tensile strength, flexural strength, and impact strength of the composites were examined, it was found through controlled experiments that these properties increased as the fiber size increased. Additionally, it was found that sample R [15%wt Coir and 15%wt Luffa] provided the optimum mechanical qualities across a range of fiber sizes because the fibers were distributed evenly throughout the matrix.  Machine learning tools such as artificial neural networks [ANN] and fuzzy logic designers were utilized to model and foretell the experimental outcomes of the hybrid composites. The model's output variables were tensile strength, flexural strength, and impact strength; the input variables were the weights of coir and luffa and the fiber size. Based on their coefficient of determination [R2], the two analyzed models' performances and appropriateness were compared. Tensile strength, flexural strength, and impact strength all have fuzzy logic coefficients of determination [R2] of 0.9752, 0.9773, and 0.9730, respectively. Similar to this, the ANN coefficients of determination [R2] for tensile strength, flexural strength, and impact strength are 0.9608, 0.904, and 0.9378, respectively. Based on this statistical analysis, the fuzzy logic designer produced a much more accurate prediction than the ANN in terms of the coefficient of determination [ R2] and mean square error [MSE] values.},
      keywords = {Fuzzy logic designers, Artificial Neural Network, Mean square error, Hybrid Composite, Coefficient of determination, contour plot},
      month = {January},
  }