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Material selection of natural fibre using a stepwise regression model with error analysis

机译:使用带有误差分析的逐步回归模型选择天然纤维的材料

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The nature of natural fibre such as it is lightweight, recyclable, biodegradable and gives a high performance in relation to its mechanical properties makes this material an excellent alternative to currently used materials in the manufacture of automotive components. The significant mechanical properties are identified using the best statistical model suggested by stepwise regression in this study. The estimation and error analysis of the response variables are discussed to select the best natural fibre for automotive component applications. The results using statistical measurement indicate that tensile strength is the most significant mechanical property for all the selected natural fibres. The final ranking that considered high performance score and minimum error analysis for a hand-brake lever application found that coir, kenaf and cotton are the top three candidates with average scores of 4, 4.5 and 5, respectively. The statistical model presented in this study can be used in multiple applications. In fact, this approach is helpful to the design engineer when huge amounts data are involved.
机译:天然纤维的性质,例如重量轻,可回收,可生物降解,以及就其机械性能而言具有很高的性能,使这种材料成为汽车部件制造中目前使用的材料的极佳替代品。通过本研究中逐步回归所建议的最佳统计模型,可以识别出显着的机械性能。讨论了响应变量的估计和误差分析,以选择用于汽车零部件应用的最佳天然纤维。使用统计测量的结果表明,拉伸强度是所有选定天然纤维的最重要的机械性能。最终的排名考虑了高性能性能和对手刹杠杆应用的最小误差分析,发现椰壳纤维,洋麻和棉花是平均得分分别为4、4.5和5的前三名。本研究中提出的统计模型可用于多种应用。实际上,当涉及大量数据时,这种方法对设计工程师很有帮助。

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