Study on Tensile Properties of Fly Ash, Sugarcane Fiber and Carbon Nanotube-Reinforced Polymer Matrix Composite Using Objective Evolutionary Algorithm

Gopalan Venkatachalam, Arunkumar Gopu, Pitchumani Shenbaga Velu, Neelanarayanan Venkataraman*, Dinesh Ramesh Salunke, Raghava Rao Mukkamala

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Composite materials have a wide range of applications in emerging eco-friendly environments. Composites that created from naturally available materials are easily decomposed over time and very cost-effective. Fly ash and sugarcane fiber are widely available waste materials produced on a massive scale. This research was aimed to find an optimal mixture of reinforced composites (fly ash, sugarcane fiber and CNTs) in order to maximize yield strength, ultimate tensile strength and Young’s modulus using a Multi-Objective Evolutionary Algorithm with Decomposition (MOEA/D). Optimizing one objective may have a negative impact on another objective, so the authors used the sophisticated MOEA/D algorithm to simultaneously find optimal values on all three objectives. The Design of Experiments (DOE) method was performed using ANOVA, and then regression equations were generated. The regression equations were optimized using the MOEA/D algorithm to obtain optimal values. Using the optimal compositional values produced by the algorithm, materials were fabricated. The fabricated materials were tested using a Shimadzu UTM machine to cross-validate the findings. A combination of 0.2 wt.% of fly ash, 2 wt.% of SCF, and 0.39 wt.% of CNTs showed a maximum yield strength of 7.52 MPa and Young’s modulus of 1281.18 MPa, with a quite considerable ultimate tensile strength of 10.54 MPa compared with the optimized results obtained through the response surface methodology.
Original languageEnglish
Article number4112
Issue number23
Number of pages15
Publication statusPublished - Dec 2022


  • Natural fibers
  • Carbon nanotubes
  • Mechanical properties
  • MOEA/D algorithm

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