Epoxy composite laminates reinforced with carbon black (6 wt.%), graphite (4 wt.%), and multi-walled carbon nanotubes (MWCNTs, 1.5 wt.%) were synthesized by compression molding to evaluate their dry sliding wear characteristics. These wt.% of filler contents were selected based on prior optimization reported in the literature. The effects of the type of composite, sliding speed, track diameter, and applied load on specific wear rate (SWR), frictional force (FF), and coefficient of friction (COF) were systematically analyzed. A Box–Behnken design within the Response Surface Methodology framework (BRSM) was used, and statistical significance was evaluated using ANOVA at a 95% confidence level. Composite type emerged as the most influential factor, contributing 54.13% and 48.02% to FF and COF, respectively. Sliding velocity had a marked effect on COF (32.31%), while the second-order term of normal load significantly impacted FF (24.20%). Although track diameter exhibited a comparatively lower main effect, its second-order terms notably impacted SWR (21.58%) and FF (5.12%). An Accelerated Particle Swarm Optimization Algorithm (BRAPSOA) was implemented and compared with BRSM under single and multi-response optimization. BRAPSOA yielded better prediction accuracy, reducing errors and enhancing SWR (16%) under the optimized parameter setting. Multi-response optimization resulted simultaneous reduction of SWR, FF, and COF by 33.36%, 10.03%, and 6.13%, respectively. The morphological analysis revealed that adhesive wear, fatigue cracks, micro-plowing, and localized epoxy softening influenced the composite’s wear performance. Functionalized MWCNT-reinforced epoxy composite exhibited the most favorable wear characteristics, showing smoother surfaces and reduced abrasive damage, confirming BRAPSOA’s effectiveness in optimizing tribological behavior.
Mishra et al. (2026) studied this question.