Modeling of cutting parameters in turning of PEEK composite using artificial neural networks and adaptive-neural fuzzy inference systems

Özden G., Öteyaka M. Ö., Cabrera F. M.

JOURNAL OF THERMOPLASTIC COMPOSITE MATERIALS, cilt.36, sa.2, ss.493-509, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 36 Sayı: 2
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1177/08927057211013070
  • 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.493-509
  • Anahtar Kelimeler: Polyetheretherketone (PEEK), Artificial Neural Networks (ANN), Adaptive-Neural Fuzzy Inference System (ANFIS), Turning, MACHINABILITY ASPECTS, PREDICTION, FORCE, ANN, STRENGTH, ANFIS
  • Eskişehir Osmangazi Üniversitesi Adresli: Evet

Özet

Polyetheretherketone (PEEK) and its composites are commonly used in the industry. Materials with PEEK are widely used in aeronautical, automotive, mechanical, medical, robotic and biomechanical applications due to superior properties, such as high-temperature work, better chemical resistance, lightweight, good absorbance of energy and high strength. To enhance the tribological and mechanical properties of unreinforced PEEK, short fibers are added to the matrix. In this study, Artificial Neural Networks (ANNs) and the Adaptive-Neural Fuzzy Inference System (ANFIS) are employed to predict the cutting forces during the machining operation of unreinforced and reinforced PEEK with30 v/v% carbon fiber and 30 v/v% glass fiber machining. The cutting speed, feed rate, material type, and cutting tools are defined as input parameters, and the cutting force is defined as the system output. The experimental results and test results that are predicted using the ANN and ANFIS models are compared in terms of the coefficient of determination (R-2) and mean absolute percentage error. The test results reveal that the ANFIS and ANN models provide good prediction accuracy and are convenient for predicting the cutting forces in the turning operation of PEEK.