Experimental investigation of machinability, mechanical characteristics, and wear behavior in TiO 2 /glass fiber hybrid composites with regression-based machinability prediction

This study investigates the effects of nano TiO 2 addition on the mechanical, wear, and machinability properties of plain-woven glass fiber-reinforced epoxy composites (GFRCs). For this purpose, the effect of 1 wt% nano TiO 2 addition on flexural, tensile, wear, and machinability properties was examined. The incorporation of TiO 2 nanoparticles moderately improved tensile, compressive, and flexural strengths of GFRCs; this enhancement is attributed to strengthened matrix/fiber interface bonding resulting from homogeneous dispersion of nanoparticles. In wear tests, an increase in sliding speed led to higher coefficients of friction (COF) and wear rates. The worn surface morphology of 1 wt% TiO 2 -GFRC changed with sliding speed and applied load; the presence of TiO 2 strengthened the fiber–matrix–filler interface, and enhanced overall wear resistance. In the milling of GFRCs, increasing the feed rate increased the resultant cutting force (F R ) and surface roughness (Ra), whereas increasing the cutting speed reduced both. The addition of TiO 2 filler further decreased F R and Ra. Cryogenic treatment of cutting tools also had a reducing effect on these values. SEM observations of chips produced from TiO 2 -filled GFRCs showed shorter and more uniformly distributed fiber fragments. Moreover, the chip morphology of GFRCs machined using cryogenically treated tools exhibited small fiber fragments along with clustered fiber bundles. The comparative analysis showed that all optimized models achieved high predictive accuracy, with ANN providing the best overall agreement with the experimental results and GBR demonstrating similarly strong performance.

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Publication Details

Journal
Journal of Composite Materials
Published
2026-11-02
DOI
https://doi.org/10.1177/00219983261505002
Primary Topic
Advanced machining processes and optimization
Type
article
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article

Experimental investigation of machinability, mechanical characteristics, and wear behavior in TiO 2 /glass fiber hybrid composites with regression-based machinability prediction

Hüseyin Gürbüz, Şehmus Baday, Mehmet Emin Demir, Muhammed Taka
Journal of Composite Materials
Advanced machining processes and optimization
article

Experimental investigation of machinability, mechanical characteristics, and wear behavior in TiO 2 /glass fiber hybrid composites with regression-based machinability prediction

Hüseyin Gürbüz, Şehmus Baday, Mehmet Emin Demir, Muhammed Taka
article en

Abstract

This study investigates the effects of nano TiO 2 addition on the mechanical, wear, and machinability properties of plain-woven glass fiber-reinforced epoxy composites (GFRCs). For this purpose, the effect of 1 wt% nano TiO 2 addition on flexural, tensile, wear, and machinability properties was examined. The incorporation of TiO 2 nanoparticles moderately improved tensile, compressive, and flexural strengths of GFRCs; this enhancement is attributed to strengthened matrix/fiber interface bonding resulting from homogeneous dispersion of nanoparticles. In wear tests, an increase in sliding speed led to higher coefficients of friction (COF) and wear rates. The worn surface morphology of 1 wt% TiO 2 -GFRC changed with sliding speed and applied load; the presence of TiO 2 strengthened the fiber–matrix–filler interface, and enhanced overall wear resistance. In the milling of GFRCs, increasing the feed rate increased the resultant cutting force (F R ) and surface roughness (Ra), whereas increasing the cutting speed reduced both. The addition of TiO 2 filler further decreased F R and Ra. Cryogenic treatment of cutting tools also had a reducing effect on these values. SEM observations of chips produced from TiO 2 -filled GFRCs showed shorter and more uniformly distributed fiber fragments. Moreover, the chip morphology of GFRCs machined using cryogenically treated tools exhibited small fiber fragments along with clustered fiber bundles. The comparative analysis showed that all optimized models achieved high predictive accuracy, with ANN providing the best overall agreement with the experimental results and GBR demonstrating similarly strong performance.

Journal of Composite Materials
Batman University (TR)
Openalex Percentile: Top 24%
Advanced machining processes and optimization
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Experimental investigation of machinability, mechanical characteristics, and wear behavior in TiO 2 /glass fiber hybrid composites with regression-based machinability prediction — Hüseyin Gürbüz, Şehmus Baday, et al. · Journal of Composite Materials (2026) | TGRS Research Map | TGRS