Neural network modeling of forces in drilling of glass/epoxy composites filled with agro-based waste materials
In this paper, the drilling behavior of a new class of composite materials has been experimentally investigated. Thecomposite laminates have been manufactured using glass fibers, epoxy resin, and filler materials. The abundantly availableagro-based waste materials (coconut coir, rice husk, and wheat husk) have been used as filler materials. The drillingexperiments have been performed at several levels of feed (0.03 to 0.3 mm/rev.) and speed (90 to 2800 RPM) using differenttypes of drill bits. The effect of these parameters on the drilling forces (axial thrust and torque) has been analyzed for alltypes of laminates under investigation. The artificial neural network-based models have also been proposed to compute thedrilling forces. The fitness of the models has been measured in terms of mean percentage error between the predicted andactual values. From the investigation, it has been found that the drilling forces computed by the neural network models werequite close to the experimental values.
Composites, Natural fillers, Drilling, Forces, Neural network
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