Predicting Tribological Performance of Graphite-Plugged Bronze Bushings Using an Adaptive Neuro-Fuzzy Inference System

Published: 1 June 2026| Version 1 | DOI: 10.17632/wjk345ykjd.1
Contributors:
Ali Khalaf,
,

Description

This dataset contains the raw and processed experimental results from a full-factorial dry sliding tribological study of graphite-plugged CuAl10Fe5Ni5 bronze bushings. The bushings ran against an AISI 410 stainless steel shaft under four input variables: graphite surface coverage (10%, 20%, 30%), plug diameter (8, 10, 12 mm), rotational speed (250, 500, 750 rpm), and applied load (50, 100, 150 kg). A total of 81 unique test conditions were performed (3⁴ full factorial). For each condition, the coefficient of friction (COF, dimensionless) and material loss (g) were measured using a custom journal-bearing tribometer. Data are provided in two tables: Table 1 presents all 81 COF measurements (experimental vs. ANFIS-predicted); Table 2 presents all 81 material loss measurements (experimental vs. ANFIS-predicted). A separate validation dataset (three additional operating conditions not used in training) is also included. The data support the development and validation of two independent ANFIS predictive models reported in the associated article.

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Engineering, Materials Science, Tribology, Bronze Metallurgy

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