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Licensed Unlicensed Requires Authentication Published by De Gruyter Oldenbourg 2018

6. Analysis and optimization of hole quality parameters in cenosphere-multiwall carbon nanotube hybrid composites drilling using artificial neural network and gravitational search technique

From the book Drilling Technology

  • V. N. Gaitonde , Shashikant , Anand Lakkundi , S. R. Karnik , A. S. Deshpande and J. Paulo Davim


Analysis and optimization of hole quality parameters in drilling of cenosphere- multiwall carbon nanotubes (MWCNTs) - epoxy composite materials have been presented in this chapter. The hybrid composite material is being prepared with 40% by weight of cenosphere with varying 0.2%, 0.3% and 0.4% of MWCNT as a filler and epoxy as matrix. The full factorial design (FFD) was planned to reduce the drilling experiments. The influence of four factors: explicit cutting speed, feed, % weight of MWCNT and drill diameter of hole quality parameters such as circularity error, drilled hole surface roughness and delamination factor have been studied. The artificial neural network (ANN)-based modeling analysis indicates that an addition of MWCNT reinforced with cenosphere-epoxy resin decreases the circularity error and surface roughness, whereas delamination is found to be minimal for 0.2% of MWCNT reinforcement drilling. To reduce the circularity error, 0.3% MWCNT reinforcement is desirable for drill diameters in the range 8-16 mm. For a particular drill size and MWCNT combination, the concurrent increase in cutting speed with feed has visible consequence for reducing the surface roughness. With 0.4% MWCNT reinforcement drilling, more delamination is observed for all the specified speed-feed combinations. ANN models were later used for gravitational search (GS) technique to decide the best combinations of cutting conditions for a particular drill diameter and % MWCNT for minimal circularity error, surface roughness and delamination factor.

© 2018 Walter de Gruyter GmbH, Berlin/Munich/Boston
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