In this paper, the drilling behavior of a new class of composite materials has been experimentally investigated. The composite laminates have been manufactured using glass fibers, epoxy resin, and filler materials. The abundantly available agro-based waste materials (coconut coir, rice husk, and wheat husk) have been used as filler materials. The drilling experiments have been performed at several levels of feed (0.03 to 0.3 mm/rev.) and speed (90 to 2800 RPM) using different types of drill bits. The effect of these parameters on the drilling forces (axial thrust and torque) has been analyzed for all types of laminates under investigation. The artificial neural network-based models have also been proposed to compute the drilling forces. The fitness of the models has been measured in terms of mean percentage error between the predicted and actual values. From the investigation, it has been found that the drilling forces computed by the neural network models were quite close to the experimental values.

中文翻译：

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神经网络模型对填充农业基废料的玻璃/环氧树脂复合材料进行钻孔的力

本文通过实验研究了一类新型复合材料的钻孔性能。使用玻璃纤维，环氧树脂和填充材料制造了复合层压板。大量可用的农业废料（椰子壳，稻壳和小麦壳）已用作填充材料。使用不同类型的钻头，已在几种进给水平（0.03至0.3 mm / rev。）和速度（90至2800 RPM）下进行了钻孔实验。对于正在研究的所有类型的层压板，已经分析了这些参数对钻孔力（轴向推力和扭矩）的影响。还提出了基于人工神经网络的模型来计算钻井力。模型的适用性已根据预测值与实际值之间的平均百分比误差进行了度量。从调查中发现，由神经网络模型计算出的钻孔力与实验值非常接近。