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Research Article

Mechanical and Dry Sliding Wear Characterization of Marble Dust and Short Kenaf Fiber Reinforced Hybrid Polyester Composites

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Pages 9235-9247 | Published online: 30 Sep 2021
 

ABSTRACT

Polymer composites reinforced with natural fibers and/or waste particulate fillers have been gaining an increasing popularity over the last few decades because of their environmental friendliness, biodegradability, and lower cost. Considering this, the present work is focused on the development, characterization, and dry-sliding wear response of hybrid composites consisting of polyester as the matrix and marble dust (industrial/construction waste) along with short kenaf fibers as the reinforcements. The physical and mechanical characterizations of the hybrid composites are conducted following ASTM standards in regard to their density, tensile, flexural, and impact strength. Dry sliding wear trials on the composite samples are conducted as per ASTM G99-05 under different test conditions using Taguchi’s L25 orthogonal array. Analysis of test results reveals that the filler content in the composite followed by sliding velocity has a significant effect on the wear rate. Analyzing the micrographs of the worn composite surfaces, it is observed that microcracking, plastic deformation of matrix, formation of wear debris, plowing on the composite surface are the major causes of wear loss of the composites. A prediction model based on artificial neural networks (ANN) is used to predict the SWR of the composites in a range wider than the experimental domain.

摘要

近几十年来, 用天然纤维和/或废颗粒填料增强的聚合物复合材料因其环境友好, 可生物降解性和成本较低而越来越受欢迎. 考虑到这一点, 目前的工作重点是以聚酯为基体, 大理石粉尘 (工业/建筑垃圾) 以及短红麻纤维为增强体的混杂复合材料的开发, 表征和干滑动磨损响应. 混杂复合材料的物理和机械特性按照ASTM标准进行, 包括密度, 拉伸, 弯曲和冲击强度. 根据ASTM G99-05, 使用田口L25正交表在不同试验条件下对复合材料试样进行干滑动磨损试验. 试验结果分析表明, 复合材料中的填料含量和滑动速度对磨损率有显著影响. 通过对磨损复合材料表面显微照片的分析, 发现微裂纹, 基体的塑性变形, 磨屑的形成, 复合材料表面的犁削是造成复合材料磨损损失的主要原因. 基于人工神经网络 (ANN) 的预测模型用于在比实验范围更宽的范围内预测复合材料的SWR.

Disclosure statement

No potential conflict of interest was reported by the author(s).

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