Selection of optimal machining conditions for the composite materials by using Taguchi and GONNs

ERKAN Ö., Demetgul M., Isik B., Tansel I. N.

MEASUREMENT, cilt.48, ss.306-313, 2014 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 48
  • Basım Tarihi: 2014
  • Doi Numarası: 10.1016/j.measurement.2013.11.011
  • Dergi Adı: MEASUREMENT
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.306-313
  • Anahtar Kelimeler: GFRP, Milling, Neural network, Genetic algorithm, GONNs, FIBER-REINFORCED PLASTICS, CARBON-CARBON COMPOSITE, SURFACE-ROUGHNESS, OPERATING-CONDITIONS, GFRP COMPOSITES, HAND-LAY, MACHINABILITY, OPTIMIZATION, PREDICTION, PARAMETERS
  • Marmara Üniversitesi Adresli: Evet

Özet

Milling has been widely used in industry for machining parts to their final dimensions without requiring additional operations. Extensive experimental work is necessary to determine the optimal cutting conditions of glass-fiber reinforced polymer composite (GFRP) materials to achieve the desired surface quality. In this study, a series of machining operations were done for data collection by varying the flute number, feed rate, depth of cut and cutting speed. The relationship between the cutting parameters of end milling operations and the surface roughness of the machined surface was studied. For the analysis of the data and selection of the optimal cutting parameters the Taguchi method and genetically optimized neural network systems (GONNs) were used. Published by Elsevier Ltd.