Artificial neural network modeling for prediction of cutting forces in turning unreinforced and reinforced polyamide

Özden G., Mata F., Öteyaka M. Ö.

Journal of Thermoplastic Composite Materials, cilt.34, ss.353-363, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 34
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1177/0892705719845712
  • Dergi Adı: Journal of Thermoplastic Composite Materials
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Communication Abstracts, Compendex, INSPEC, Metadex, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.353-363
  • Anahtar Kelimeler: Polyamide, carbon fiber, machining, artificial neural network, composite
  • Eskişehir Osmangazi Üniversitesi Adresli: Evet

Özet

© The Author(s) 2019. Cutting force measurement in manufacturing is very important to optimize the machining process. The parameters, such as the type of material, feed rate, cutting speed, and cutting tool, affect the cutting forces in the turning operation. In this study, an artificial neural network (ANN) model is used to predict the cutting forces during the turning operation of unreinforced and reinforced polyamide (PA) with 30 v/v% carbon fibers using the cutting tools K15 and polycrystalline diamond (PCD). The cutting speed (50–200 m/min), feed rate (0.05–0.2 mm/rev), type of material, and cutting tools (K15 and PCD) are defined as input parameters of the system. The predicted values obtained from ANN model and experimental results are compared in terms of the coefficient of determination (R 2 ) and the mean absolute percentage error. The results of the model are in good agreement with the experimental data and show the effectiveness of the ANN method in predicting cutting forces in the turning operation of PA.