Modeling of dynamic mechanical curves of kenaf/polyester composites using surface response methodology

The environmental and social concerns regarding environmental‐friendly materials lead to alternatives in replacing synthetic fibers for natural ones on polymeric composites. This study focused on modeling dynamic mechanical curves of kenaf/polyester composites using response surface methodology (RSM...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of applied polymer science 2022-05, Vol.139 (18), p.n/a
Hauptverfasser: Ornaghi, Heitor Luiz, Neves, Roberta Motta, Monticeli, Francisco Maciel, Thomas, Sabu
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:The environmental and social concerns regarding environmental‐friendly materials lead to alternatives in replacing synthetic fibers for natural ones on polymeric composites. This study focused on modeling dynamic mechanical curves of kenaf/polyester composites using response surface methodology (RSM). Composites with three different reinforcement contents (13.5, 22.33, and 36.27 vol%) were produced and subjected to the dynamic mechanical analysis (DMTA). From the experimental DMTA curves, a 3D surface plot using RSM was done. The results showed that the fiber dynamic mechanical behavior and fiber/matrix interface had a low influence on the glass transition temperature but significantly changed the tan δ peak height. On the other hand, the kenaf fibers presented an enormous difference in the elastomeric region. The constrained region (calculated using the tan delta height) increased ~4 times for the composite reinforced with 36.27 vol% when compared to the composite reinforced with 13.5 vol%. The RSM enabled the viscoelastic modeling using different fiber volumes with high reliability and low error (R2 > 0.99). The RSM approach proved to be an intelligent and reliable technique to access a higher range of results, reducing experimental time and cost and keeping statistical significance. Also, the present methodology can be extended to model other properties and/or optimize parameters.
ISSN:0021-8995
1097-4628
DOI:10.1002/app.52078