Characterization and Optimization of Dry Sliding Wear Properties of Partially Biodegradable Hybrid Polymer Composites

被引:0
|
作者
Itishree Rout [1 ]
Mayuri Singh [2 ]
Trupti Ranjan Mahapatra [1 ]
Punyapriya Mishra [3 ]
Sushmita Dash [2 ]
Debadutta Mishra [1 ]
机构
[1] Veer Surendra Sai University of Technology,Department of Production Engineering
[2] GITA Autonomous College,Department of Mechanical Engineering
[3] Veer Surendra Sai University of Technology,Department of Mechanical Engineering
关键词
Natural fiber-reinforced polymer composites; Human hair; Luffa cylindrica fibers; Incense stick ash; Mechanical properties; Thermal stability; Tribological analysis;
D O I
10.1007/s40735-025-00979-w
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摘要
The influence of the hybridization of NaOH-treated human hair fibers, Luffa cylindrica fibers, and varying amounts of Incense Stick Ash (ISA) as a filler on the mechanical, thermal, and wear characteristics of the resulting composite has been investigated. The synthesis process involved an Ultrasonicator-assisted hand lay-up method. The 10 wt.% ISA-filled composite exhibited superior mechanical properties, including tensile strength (19.040 MPa), shear strength (0.3335 MPa), impact strength (39.6 J/cm2), flexural strength (30.140 MPa), and microhardness (22.288 Hv). The storage modulus increased by 52.92% and 153.85% with 5 wt.% and 15 wt.% ISA filler, respectively. The 15 wt.% ISA composites showed the highest energy dissipation (tan δ = 0.54), while 5 wt.% and 10 wt.% ISA composites exhibited moderate viscoelasticity (tan δ = 0.46). TGA analysis indicated minimal mass loss below 200 °C, with the lowest first-stage peak temperature (364 °C) for 10 wt.% ISA composites. The dry abrasion wear behavior of the composites is evaluated using a Pin-on disk tribometer following a central composite design-based response surface methodology (CRSM). Analysis of variance (ANOVA) indicated that the applied normal load and ISA concentration suggestively influenced the wear characteristics. Abrasive wear, adhesive wear, plastic deformation, fatigue wear, and debris formation are observed to be the primary wear mechanisms. Optimization via a gray relational analysis (GRA) based Whale Optimization Algorithm (GWOAL) improved wear by 6.65% and 112.35% and specific wear rate by 17.81% and 3.26%, compared to CRSM and GRA, with a 0.64% deviation for frictional force.
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