Artificial neural network and regression models for performance prediction of abrasive waterjet in rock cutting

AYDIN G., KARAKURT İ., Hamzacebi C.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, cilt.75, ss.1321-1330, 2014 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 75
  • Basım Tarihi: 2014
  • Doi Numarası: 10.1007/s00170-014-6211-y
  • Dergi Adı: INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1321-1330
  • Anahtar Kelimeler: Abrasive waterjet, Rock, Kerf angle, Artificial neural networks, Regression analysis, SURFACE-ROUGHNESS, KERF ANGLE, GRANITE
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

An experimental study is carried out for modeling the rock cutting performance of abrasive waterjet. Kerf angle (KA) is considered as a performance criteria and modeled using artificial neural network (ANN) and regression analysis based on operating variables. Three operating variables, including traverse speed, standoff distance, and abrasive mass flow rate, are studied for obtaining different results for the KA. Data belonging to the trials are used for construction of ANN and regression models. The developed models are then tested using a test data set which is not utilized during construction of models. Additionally, the regression model is validated using various statistical approaches. The results of regression analysis are also used to determine the significant operating variables affecting the KA. Furthermore, the performances of derived models are compared for showing the accuracy levels in prediction of the KA. As a result, it is concluded that both ANN and regression models can give adequate prediction for the KA with an acceptable accuracy level. The compared results reveal also that the corresponding ANN model is more reliable than the regression model. On the other hand, the standoff distance and traverse speed are statistically determined as dominant operating variables on the KA, respectively.