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

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.

A machine learning approach to estimate the strain energy absorption in expanded polystyrene foams

“…20 On foam materials, a more recent study by Weng et al, presents the application of feed-forward neural network models to describe the stress/ strain relation in grey cast iron during nanoindentation. Examples of the use of ANNs on modeling different material parameters than the stress/strain relationship, within the mechanics and materials science topics, can be found in the works by Mortazavi and Ince (mechanical fatigue), 22 Setti and Rao, Egala et al, (tribological behavior), 23,24 and Tabaza et al (dynamic viscoelastic material properties). 25 More recently, in 2020 Dudzik and Stre Rk 26 presented a study that gives neural network material modeling specifications to address the stress/strain response of a foam aluminum alloy material.…”

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“…Thereby, the ANN was able to predict the specific wear rate with low errors between 2.5% and 6.9% for composites without BFS and between 0.9% and 5.1% for composites with BFS. Epoxy composites were also investigated recently by Egala et al [40] with newly developed natural short castor oil fibers (ricinus communis) as unidirectional reinforcements of different lengths and at a constant volume fraction of 40%. The database consisting of 36 data points was acquired from experiments utilizing a flat pin-on-disk tribometer under dry sliding conditions against a hardened steel disk as a counter-body.…”

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“…Also, the optimum fiber length was found to be 5mm. At 5mm fiber length, the lowest wear was found to be IOP Publishing doi:10.1088/1757-899X/1225/1/012008 2 2.05mg and CoF 0.239 [7]. As it was impossible to perform experimentation at every condition, soff computing techniques such as Fuzzy Interface system (FIS), Artificial Neural Networks (ANN) has been employed to predict and analyze the output responses at unknown test cases.…”