Volume 48, No 2, 2026, Pages 392-417
Download full text in PDFPredictive Modeling and Parametric Analysis of Three-Body Abrasive Wear in Hybrid CNF-GF/PPS Nanocomposites
Authors:
Y.C. Arun 
,
R. Ravishankar ,
B. Suresha 
,
V.G. Pradeep Kumar ,
M.N. Thejaswini ,
V.M. Mahesh
DOI: 10.24874/ti.2157.03.26.05
Received: 19 March 2026
Revised: 24 April 2026
Accepted: 25 May 2026
Published: 15 June 2026
Abstract:
Three-body abrasive wear is a major degradation mechanism in fiber-reinforced thermoplastic composites used in demanding automotive and aircraft components. Under abrasive wear circumstances, this work examines the wear performance and predictive modeling of carbon nanofiber (CNF) filled short glass fiber/polyphenylene sulfide (GF/PPS) hybrid nanocomposites. To assess the impact of CNF content on microstructure, density, hardness, interlaminar shear strength, and abrasive wear behavior, nanocomposites with varying CNF levels (0–1 wt%) were produced using extrusion and injection molding. The microstructural data are in support of the integration of CNF to strengthen the fiber/matrix interface and boost the load transmission and crack-bridging features by the formation of a well-dispersed reinforcing structure at the nanoscale. Density study revealed that excessive loading led to agglomeration and increased porosity whereas moderate CNF addition (0.6 and 0.8 wt%) reduced void content and enhanced packing effectiveness. The baseline composite had a Barcol hardness of 44.2, which improved to 54.3 with 0.8 wt% CNF. Box–Behnken design was planned for the abrasive wear trials considering load, abrading distance and filler content and response surface methodology was used for the analysis and optimization. Statistical analysis indicates that load and CNF content have a significant impact on wear loss. Additionally, compared to the GF/PPS composite, the addition of 0.8 wt% CNFs greatly increased the abrasion resistance of baseline composites, as evidenced by the development of very small and shallow grooves, smoother worn surfaces, and decreased wear loss. ANOVA and response surface analysis revealed that CNF content and applied load were the most significant parameters determining wear loss, while the proposed model exhibited good predictive accuracy (R2=86.87%, adjusted R2=83.29%). A dependable framework for creating wear-resistant GF/PPS hybrid nanocomposites for cutting-edge tribological applications is provided by the established prediction model.
Keywords:
Polyphenylene sulfide hybrid nanocomposites, Carbon nanofibers, Three-body abrasive wear, Box–Behnken design, ANOVA, Worn surface morphology
