Modeling of thrust force and torque in drilling aluminum 7050


Aslan E., Gürkan Kocataş D., UZUN G.

Materialpruefung/Materials Testing, cilt.66, sa.4, ss.513-525, 2024 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 66 Sayı: 4
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1515/mt-2023-0335
  • Dergi Adı: Materialpruefung/Materials Testing
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex
  • Sayfa Sayıları: ss.513-525
  • Anahtar Kelimeler: artificial neural network, drilling, finite element analysis, thrust force, torque
  • Gazi Üniversitesi Adresli: Evet

Özet

The aluminum AA7050 alloy has high toughness and high strength. Despite the high machinability of the AA7050 alloy, hole quality can vary according to tool geometry and drilling parameters. This study investigated the effects of different cutting parameters and three different drill point angles on thrust force and torque. Numerical analyses for thrust force and torque were performed using the finite element method. The lowest thrust force and the highest torque were obtained with the drill at 130° drill point angle, while the highest cutting force and lowest torque were obtained with the drill at 118° drill point angle. There is an average difference of 5.37 and 6.9 % between the experimental and analysis values for thrust forces and torque, respectively, and the applicability of the finite element model has been proven. In the last part of the study, thrust force and torque are modeled with artificial neural networks. The statistical accuracy (R2) values for the learning and testing values in the thrust force of the equation are 0.997797 and 0.995739, respectively. Torque’s learning and testing accuracy values are 0.987247 and 0.937909, respectively. The obtained equations have a high accuracy rate.