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Friction

Article Title

Experimental investigation and prediction of tribological behavior of unidirectional short castor oil fiber reinforced epoxy composites

Keywords

natural fiber, castor oil fiber, epoxy composite, full factorial design of experiments (DoE), analysis of variance (ANOVA), prediction, regression, artificial neural network (ANN)

Abstract

The present study aims at introducing a newly developed natural fiber called castor oil fiber, termed ricinus communis, as a possible reinforcement in tribo-composites. Unidirectional short castor oil fiber reinforced epoxy resin composites of different fiber lengths with 40% volume fraction were fabricated using hand layup technique. Dry sliding wear tests were performed on a pin-on-disc tribometer based on full factorial design of experiments (DoE) at four fiber lengths (5, 10, 15, and 20 mm), three normal loads (15, 30, and 45 N), and three sliding distances (1,000, 2,000, and 3,000 m). The effect of individual parameters on the amount of wear, interfacial temperature, and coefficient of friction was studied using analysis of variance (ANOVA). The composite with 5 mm fiber length provided the best tribological properties than 10, 15, and 20 mm fiber length composites. The worn surfaces were analyzed under scanning electron microscope. Also, the tribological behavior of the composites was predicted using regression, artificial neural network (ANN)-single hidden layer, and ANN-multi hidden layer models. The confirmatory test results show the reliability of predicted models. ANN with multi hidden layers are found to predict the tribological performance accurately and then followed by ANN with single hidden layer and regression model.

Publisher

Tsinghua University Press

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