An Analysis of the Sliding Wear Characteristics of Epoxy-Based Hybrid Composites Using Response Surface Method and Neural Computation

ABSTRACT

Hybrid composites are advanced composites in which the required characteristics of the material can be obtained easily by choice of suitable combinations of fibers and fillers. In this study, sliding wear characteristics of a class of hybrid composite consisting of epoxy, short human hair fibers (SHF), and solid glass microspheres (SGM) are being investigated. SGM (10wt. %) filled epoxy matrix composites are prepared by solution casting method with four distinct fiber loading. Dry sliding wear tests are performed on these composites using a pin-on-disc wear test rig by following ASTM G99-05. A mathematical model is created as per face-centered central composite design (FCCCD) in Response Surface Methodology (RSM), and the adequacy of the model was verified using analysis of variance (ANOVA). An artificial neural network (ANN) approach is also applied to predict the wear rate of the composite. The test results are compared with those obtained for composites with only SHF reinforcement under similar test conditions. It is found that while reinforcement of short hair fiber enhances the wear performance of epoxy, the addition of SGM further improves it. Worn surfaces of selected samples are investigated by scanning electron microscopy (SEM) to identify the wear mechanism of the composite.

摘要

混杂复合材料是一种先进的复合材料，通过选择合适的纤维和填料组合，可以很容易地获得材料所需的性能. 研究了环氧树脂、人发短纤维（SHF）和固体玻璃微珠（SGM）复合材料的滑动磨损特性. 采用溶液浇铸法制备了SGM（10wt%）填充环氧基复合材料. 根据ASTM G99-05，使用销盘磨损试验台对这些复合材料进行干滑动磨损试验. 根据响应面法（RSM）中面心中心复合材料设计（FCCCD）建立了数学模型，并用方差分析（ANOVA）验证了模型的正确性. 采用人工神经网络（ANN）方法对复合材料的磨损率进行了预测. 在相同的试验条件下，将试验结果与纯SHF增强复合材料的试验结果进行了比较. 结果表明，短纤维增强环氧树脂的耐磨性，而SGM的加入进一步提高了其耐磨性. 用扫描电子显微镜（SEM）对所选试样的磨损表面进行了研究，以确定复合材料的磨损机理.

KEYWORDS:

• Hybrid composites
• epoxy
• hair fibers
• solid glass microsphere
• sliding wear
• response surface method

关键词:

• 混杂复合材料
• 环氧树脂
• 头发纤维
• 固体玻璃微球
• 滑动磨损
• 响应面法