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.

本研究旨在引入一种新开发的天然蓖麻油纤维，称为蓖麻油，作为蓖麻油复合材料的增强材料。采用手糊法制备了不同纤维长度，体积分数为40％的单向短蓖麻油纤维增强环氧树脂复合材料。基于全因子实验设计（DoE）在四根光纤长度（5、10、15和20 mm），三个法向载荷（15、30和45）的情况下，在针盘式摩擦计上进行了干式滑动磨损测试N），以及三个滑动距离（1,000、2,000和3,000 m）。使用方差分析（ANOVA）研究了各个参数对磨损量，界面温度和摩擦系数的影响。纤维长度为5毫米的复合材料提供的摩擦学性能优于10、15 和20毫米纤维长度的复合材料。在扫描电子显微镜下分析磨损的表面。同样，使用回归，人工神经网络（ANN）-单隐藏层和ANN-多隐藏层模型来预测复合材料的摩擦学行为。验证性测试结果表明了预测模型的可靠性。发现具有多个隐藏层的人工神经网络可以准确地预测摩擦学性能，然后再使用具有单个隐藏层的人工神经网络和回归模型。