Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/55245
Title: Neural network modeling of forces in drilling of glass/epoxy composites filled with agro-based waste materials
Authors: Dhawan, Vikas
Debnath, Kishore
Singh, Inderdeep
Singh, Sehijpal
Keywords: Composites;Natural fillers;Drilling;Forces;Neural network
Issue Date: Jun-2020
Publisher: NISCAIR-CSIR, India
Abstract: 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.
Page(s): 649-658
URI: http://nopr.niscair.res.in/handle/123456789/55245
ISSN: 0975-1017 (Online); 0971-4588 (Print)
Appears in Collections:IJEMS Vol.27(3) [June 2020]

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