Machine Learning-Guided Evolutionary Optimization of Compression Molding Parameters for Graphene-Enhanced Jute Fiber Composites 


Vol. 26,  No. 7, pp. 3123-3145, Jul.  2025
10.1007/s12221-025-01011-1


PDF
  Abstract

This study aims to optimize the mechanical properties of graphene-enhanced jute fiber epoxy composites by simultaneously improving open-hole tensile strength (OHTS), elasticity modulus (EM), and failure strain (FS) using a hybrid machine learning approach. A central composite design (CCD) was employed to explore the effects of molding pressure (1–6 MPa), molding temperature (140–190 °C), and graphene content (0.1–2.0 wt.%) on the composite performance. An Artificial Neural Network (ANN) model trained on experimental data achieved high predictive accuracy (R2 = 0.92–0.94), capturing non-linear dependencies among processing parameters and mechanical responses. Multi-objective optimization using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) integrated with ANN-predicted optimal conditions of 5.967 MPa pressure, 140.176 °C temperature, and 1.980 wt.% graphene. The predicted tensile strength (339.05 MPa), modulus (20.19 GPa), and failure strain (FS) (0.0244 mm/mm) closely matched experimental values with < 9% error. SEM analysis revealed fracture mechanisms including fiber pull-out, delamination, and void formation, emphasizing the need for improved interfacial adhesion and graphene dispersion. This study establishes a robust ANN-NSGA-II framework for accelerating process optimization in bio-based nanocomposites, with potential scalability to diverse sustainable material systems.

  Statistics
Cumulative Counts from November, 2022
Multiple requests among the same browser session are counted as one view. If you mouse over a chart, the values of data points will be shown.


  Cite this article

[IEEE Style]

P. Hariharasakthisudhan, M. Kandasamy, K. Logesh, K. Sathickbasha, "Machine Learning-Guided Evolutionary Optimization of Compression Molding Parameters for Graphene-Enhanced Jute Fiber Composites," Fibers and Polymers, vol. 26, no. 7, pp. 3123-3145, 2025. DOI: 10.1007/s12221-025-01011-1.

[ACM Style]

P. Hariharasakthisudhan, M. Kandasamy, K. Logesh, and K. Sathickbasha. 2025. Machine Learning-Guided Evolutionary Optimization of Compression Molding Parameters for Graphene-Enhanced Jute Fiber Composites. Fibers and Polymers, 26, 7, (2025), 3123-3145. DOI: 10.1007/s12221-025-01011-1.

Search
(IN TITLE, AUTHOR, ABSTRACT,KEYWORDS)

Advanced Search ▼

POPULAR KEYWORDS
(TOP 10 KEYWORDS)

Recent Publications
(LAST 3 YEARS)

pISSN : 1229 - 9197
eISSN : 1875 - 0052

Impact Factor(2022): 2.5