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首页> 外文期刊>The International Journal of Advanced Manufacturing Technology >Experimental investigation and optimization of delamination factors in the drilling of jute fiber-reinforced polymer biocomposites with multiple estimators
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Experimental investigation and optimization of delamination factors in the drilling of jute fiber-reinforced polymer biocomposites with multiple estimators

机译:黄麻纤维增强聚合物生物复合材料钻孔分层因子的实验研究与优化

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摘要

Currently, the manufacture of composite structures often requires material removal operations using a cutting tool. Indeed, since biocomposites are generally materials that do not conduct electricity, electro-erosion cannot be utilized. As a result, the processes that can be used not only the unconventional method of abrasive water jet but also conventional machining, such as drilling. Delamination factors (F-d) are widely recognized for controlling the damaged area (delamination) induced by drilling in industry. As discussed in the literature, several approaches are available to evaluate and quantify the delamination surrounding a hole. In this context, the objective of this study is to compare the three F-d evaluation methods that have been most frequently used in previous investigations. To this end, three spindle and feed speeds and three Brad and Spur drills (BSD) tool diameters were selected (L-27) for drilling 155-g/m(2) density jute fabric-reinforced polyester biocomposites. The desirability function (DF) was further made to optimize the drilling parameters. The response surface methodology (RSM) and artificial neural networks (ANNs) were applied to validate the results obtained experimentally as well as to predict the behavior of the structure depending on the cutting conditions.
机译:目前,复合材料结构的制造通常需要使用切削工具进行材料去除操作。事实上,由于生物复合材料通常是不导电的材料,因此无法利用电蚀。因此,这些工艺不仅可以用于非常规的磨料水射流方法,还可以用于常规加工,例如钻孔。分层因子(F-d)被广泛认为可以控制工业钻井引起的损伤区域(分层)。正如文献中所讨论的,有几种方法可用于评估和量化孔周围的分层。在这种情况下,本研究的目的是比较以前调查中最常用的三种 F-d 评估方法。为此,选择了三种主轴和进给速度以及三种布拉德和正向钻头 (BSD) 刀具直径 (L-27) 来钻削密度为 155 g/m(2) 的黄麻织物增强聚酯生物复合材料。进一步利用合意函数(DF)对钻井参数进行优化。应用响应面法(RSM)和人工神经网络(ANN)来验证实验结果,并根据切削条件预测结构的行为。

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