Recieved:

22/12/2025

Accepted:

04/04/2026

Page: 

doi:

http://dx.doi.org/10.17515/resm2026-1428ma1222rs

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6

A factorial analysis of material composition and operating parameters on tribological properties in graphite-plugged bronze bushings

Amir Alsammarraie1, Maki H. Zaidan1, Ali K. A. Aljboury1

1Department of Mechanical Engineering/ College of Engineering/ Tikrit University, Tikrit, Iraq

Abstract

This study investigates the tribological performance of self-lubricating bronze bushings (CuAl10Fe5Ni5) with graphite plugs using a 3⁵ full factorial design (243 conditions). Effects of graphite content (10-30%), plug diameter (8-12 mm), applied load (50-150 kg; 0.61-1.84 MPa), sliding speed (250-750 rpm), and sliding time (10-30 min) on coefficient of friction (COF), wear loss, and temperature were quantified. ANOVA with effect size analysis (η²p) reveals each response is dominated by a distinct factor: applied load controls COF (η²p = 0.947, 54.65% contribution); graphite content controls temperature (η²p = 0.696, 44.0%); and sliding speed controls wear loss (η²p = 0.838, 45.53%). Plug diameter exhibits negligible practical significance (η²p ≤ 0.038, ≤ 0.62% contribution) despite marginal statistical significance for wear (p = 0.010), establishing substantial design freedom. Increasing graphite content to 30% reduces COF by 29% and wear loss by 58%. Higher loads (150 kg) reduce COF by 54% but increase wear by 59%, revealing a critical multi-objective trade-off. Increasing the sliding speed from 250 to 750 rpm causes thermal degradation (ΔT = 42.6°C), which increases wear loss by 357%. Optimal transient performance—30% graphite, 150 kg (1.84 MPa), 250 rpm—yields COF = 0.150-0.170 and wear loss < 0.100 g. Crucially, steady-state friction was not attained within 2826 m; all values represent running-in behavior. These findings provide quantitative, response-specific design guidelines enabling engineers to prioritize competing performance objectives.

Keywords

Self-lubricating bearings; Factorial design; effect size; Multi-objective optimization; Transient tribology

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