Extrusion load prediction of gear-like profile for different die geometries using ANN and FEM with experimental verification
Küçük Resim Yok
Tarih
2015
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer London Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
This paper deals with the extrusion of gear-like profiles and uses of finite element method (FEM) and artificial neural network (ANN) to predict the extrusion load. In the study, gear-like components has been manufactured by forward extrusion for the AA1070 aluminum alloy and the process was simulated by using a DEFORM-3D software package to establish a database in order to provide the data for ANN modeling. Serious experiments were performed for only one die set and four teeth gear profile to obtain data for comparing with DEFORM-3D results. After verifying a highly appropriate FEM simulation with the experiment at the same conditions, Results were enhanced for different die lengths, extrusion ratios, and two extra teeth number as three and six using FEM simulations. Subsequently, the data from the performed FEM simulations were submitted for the best obtained ANN model. Finally, a good agreement between FE-simulated and ANN-predicted results was obtained. The proposed ANN model is found to be useful in predicting the forming load of the different die set variations based on the reliable test data.
Açıklama
Anahtar Kelimeler
Gear Forming, Finite Element Method, Artificial Neural Network, Extrusion, Aluminum, Artificial Neural-Networks, Upper-Bound Solutions, Deformation-Behavior, Cold-Extrusion, Finite-Element, Strain, Temperature, Aluminum, Evaluate, Billets
Kaynak
International Journal Of Advanced Manufacturing Technology
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
76
Sayı
5-8
