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Dry sliding wear behavior of epoxy composite reinforced with short palmyra fibers

机译:用短棕榈纤维加固环氧复合材料的干式滑动磨损行为

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The present work explores the possibility of using palmyra fiber as a replacement for synthetic fiber in conventional polymer composites for application against wear. An attempt has been made in this work to improve the sliding wear resistance of neat epoxy by reinforcing it with short palmyra fibers (SPF). Epoxy composites with different proportions (0, 4, 8 and 12 wt. %) of SPF are fabricated by conventional hand lay-up technique. Dry sliding wear tests are performed on the composite samples using a pin-on-disc test rig as per ASTM G 99-05 standards under various operating parameters. Design of experiment approach based on Taguchi's L_(16) Orthogonal Arrays is used for the analysis of the wear. This parametric analysis reveals that the SPF content is the most significant factor affecting the wear process followed by the sliding velocity. The sliding wear behavior of these composites under an extensive range of test conditions is predicted by a model based on the artificial neural network (ANN). A well trained ANN has been used to predict the sliding wear response of epoxy based composites over a wide range.
机译:本作者探讨了使用Palmyra纤维作为常规聚合物复合材料中合成纤维的替代物的可能性,用于防止磨损。通过用短棕榈虫纤维(SPF)加强甘露出剂,已经在这项工作中提高了整个环氧树脂的滑动耐磨性。具有不同比例(0,4,8和12重量%)的SPF的环氧复合材料通过传统的手叠层技术制造。在各种操作参数下,使用销钉盘试验台在复合样品上进行干滑动磨损试验。基于Taguchi的L_(16)正交阵列的实验方法设计用于磨损的分析。该参数分析表明,SPF含量是影响磨损过程的最重要因素,然后是滑动速度。通过基于人工神经网络(ANN)的模型来预测这些复合材料的滑动磨损行为。训练有素的ANN已被用于预测环氧基复合材料的滑动磨损响应在宽范围内。

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