Prediction of the Tensile Load of Drilled CFRP by Artificial Neural Network
AbstractThe application areas of carbon fiber reinforced plastics (CFRP) have been increasing day by day. The machining of CFRP with incorrect machining parameters leads in huge loss cost and time. Therefore, it is very important that the composite materials are machined with correct machining parameters. The aim of this paper is to examine the influence of drilling parameters on tensile load after drilling of CFRP. The drilling operations were carried out on Computer Numerical Control (CNC) by Tungsten Carbide (WC), High Speed Steel (HSS) and Brad Spur type drill bits with spindle speeds of 1000, 3000 and 5000 rpm and feed rates of 0.05, 0.10 and 0.15 mm/rev. The results indicate that the surface roughness, delamination and thrust force, were affected by drilling parameters therefore tensile load was also affected by the same parameters. It was observed that increase in surface roughness, delamination and thrust force all lead to the decrease of tensile load of CFRP. If the correct drilling parameters are selected; the decrease in tensile load of CFRP can be saved up to 25%. Furthermore, an artificial neural network (ANN) model has been used to predict of tensile load. The results of the ANN model are in close agreement with the experimental results. View Full-Text
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Yenigun, B.; Kilickap, E. Prediction of the Tensile Load of Drilled CFRP by Artificial Neural Network. Appl. Sci. 2018, 8, 549.
Yenigun B, Kilickap E. Prediction of the Tensile Load of Drilled CFRP by Artificial Neural Network. Applied Sciences. 2018; 8(4):549.Chicago/Turabian Style
Yenigun, Burak; Kilickap, Erol. 2018. "Prediction of the Tensile Load of Drilled CFRP by Artificial Neural Network." Appl. Sci. 8, no. 4: 549.
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