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

作     者：Rajesh EGALA G V JAGADEESH Srinivasu Gangi SETTI Rajesh EGALA;G V JAGADEESH;Srinivasu Gangi SETTI

作者机构：Department of Mechanical EngineeringNational Institute of Technology RaipurChhattisgarh 492010India Department of Mechanical EngineeringGudlavalleru Engineering CollegeGudlavalleruAndhra Pradesh 521356India 

出 版 物：《Friction》 (摩擦（英文版）)

年 卷 期：2021年第9卷第2期

页      面：250-272页

核心收录：

学科分类：08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 080502[工学-材料学] 

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

摘      要：The present study aims at introducing a newly developed natural fiber called castor oil fiber,termed ricinus communis,as a possible reinforcement in *** short castor oil fiber reinforced epoxy resin composites of different fiber lengths with 40%volume fraction were fabricated using hand layup *** 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 *** worn surfaces were analyzed under scanning electron ***,the tribological behavior of the composites was predicted using regression,artificial neural network(ANN)-single hidden layer,and ANN-multi hidden layer *** confirmatory test results show the reliability of predicted *** with multi hidden layers are found to predict the tribological performance accurately and then followed by ANN with single hidden layer and regression model.