Prediction of and experimental study on cutting force of austempered vermicular graphite cast iron using artificial neural network

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MAVİ A., Ozden S., KORKUT İ.

MECHANIKA, cilt.23, sa.1, ss.153-159, 2017 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 23 Sayı: 1
  • Basım Tarihi: 2017
  • Doi Numarası: 10.5755/j01.mech.23.1.13699
  • Dergi Adı: MECHANIKA
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.153-159
  • Gazi Üniversitesi Adresli: Evet

Özet

In this study, a technique was proposed to predict cutting force of austempered vermicular graphite cast irons (VGCI) by using neural network. The effect of austempering heat treatment on the cutting force was experimentally achieved. The samples were austenitized at 900 degrees C for 90 minutes and then austempered at different temperatures (320 degrees C and 370 degrees C) for 60, 90, and 120 minutes. Machina-bility tests were carried out under dry conditions at the CNC machining center with the cutting parameters selected in accordance with ISO 3685. In the experiment, cutting force depending on hardness, cutting speed, and feed rate were measured. These results were used for input parameters (training, testing, and validation) of Artificial Neural Network (ANN) and prediction model was developed. The output value of ANN and experimental results were compared and accuracy of ANN was found to be 99.99% and 99.62% for training and test values, respectively.