Modelling of Drill Bit Temperature and Cutting Force in Drilling Process Using Artificial Neural Networks

Cakiroglu R., YAĞMUR S., ACIR A., ŞEKER U.

JOURNAL OF POLYTECHNIC-POLITEKNIK DERGISI, cilt.20, sa.2, ss.333-340, 2017 (ESCI) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 20 Sayı: 2
  • Basım Tarihi: 2017
  • Doi Numarası: 10.2339/2017.20.2.333-340
  • Dergi Adı: JOURNAL OF POLYTECHNIC-POLITEKNIK DERGISI
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.333-340
  • Anahtar Kelimeler: Artificial Neural Network, Drill Bit Temperature, Cutting Force, Machining, MULTIPLE-REGRESSION, HEAT, PARAMETERS
  • Gazi Üniversitesi Adresli: Evet

Özet

This study applied artificial neural networks (ANN) to estimate the drill bit temperature and cutting force in drilling process using Firex (R) coated carbide and uncoated drills. Also, the effects of the different network structures in the modeling the drill bit temperature and cutting force were also investigated. The numbers of neuron in network structure of ANN models are 2-6-2, 2-5-2, 2-3-5-2, 2-5-4-2, 2-3-4-4-2 and 2-2-4-3-2 structures. The best ANN model, the 2-5-2 network structures in predicting the drill bit temperatures were obtained whereas; the 2-2-4-3-2 structures were found in predicting the cutting force. The empirical equations for the best ANN models in the prediction of drill bit temperature and cutting force were developed and the obtained results were confirmed. When the results of mathematical modelling are examined, the computed the drill bit temperature and cutting forces are observed to be apparently within acceptable values.