Application of gene expression programming in hot metal forming for intelligent manufacturing
[ X ]
Tarih
2018
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Design of the die in hot metal forming operations depends on the required forming load. There are several approaches in the literature for load prediction. Artificial neural networks (ANNs) have been successfully used by a few researches to estimate the forming loads. This paper aims at using the effectiveness of a new evolutionary approach called gene expression programming (GEP) for the estimation of forging load in hot upsetting and hot extrusion processes. Several parameters such as angle (alpha), L/D ratio (R), friction coefficient (A mu), velocity (v) and temperature (T) were used as input parameters. The accuracy of the developed GEP models was also compared with ANN models. This comparison was evidenced by some statistical measurements (R (2), RMSE, MAE). The outcomes of the study showed that GEP can be used as an effective tool for representing the complex relationship between the input and output parameters of hot metal forming processes.
Açıklama
Anahtar Kelimeler
Metal Forming, Gep, Ann, Upsetting, Extrusion, Forging
Kaynak
Neural Computing & Applications
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
30
Sayı
3
