The influence of reinforcement, alkali treatment, compression pressure and temperature in fabrication of sisal/coir/epoxy composites: GRA and ANN prediction

• Published in: Polymer bulletin (Berlin, Germany) Vol. 77; no. 9; pp. 4609 - 4629
• Main Authors: Sumesh, K. R., Kanthavel, K.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-09-2020; Springer Nature B.V
• Subjects: Aerospace industry; Artificial neural networks; Characterization and Evaluation of Materials; Chemistry; Chemistry and Materials Science; Coir; Complex Fluids and Microfluidics; Composite materials; Contact angle; Content analysis; Design of experiments; Epoxy resins; Fibers; Impact tests; Mechanical properties; Optimization; Optimization techniques; Organic Chemistry; Original Paper; Physical Chemistry; Polymer Sciences; Predictions; Pressure molding; Sisal; Sodium hydroxide; Soft and Granular Matter; Straight lines; Taguchi methods; Working conditions; Mechanical properties; Artificial neural network (ANN) technique; Natural fibers; Particle reinforcement; Optimization
• ISSN: 0170-0839; 1436-2449
• DOI: 10.1007/s00289-019-02988-5

In this study, sisal and coir fiber-based epoxy composites were fabricated using compression molding route. Optimization technique using Taguchi and grey relational analysis (GRA) was employed to find the best condition for tensile, flexural and impact properties. Sisal (0–20 wt%), coir fibers (0–20 wt%), NaOH treatment (0–8 wt%), compression pressure (10–16 MPa) and temperature (100–120 °C) are the different factors with various levels. Multi-response optimization using GRA showed significance of both the fibers in enhancing mechanical properties. The response table of GRA concluded that combination of sisal 20%/coir 15%/NaOH treatment 5%/compression pressure 10 MPa and temperature 120 °C has the best mechanical results. SEM images indicated the advancement in adhesion of reinforcement and epoxy resin by hybridization of fibers. The artificial neural network (ANN) prediction states predicted and experimental values for various trials are almost same, and regression graph showed a straight line with
R
-value 97.321%. These fabricated composites can be used in aerospace and automobile industries for their interior parts.