Prediction of surface roughness in longitudinal turning process by a genetic learning algorithm
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Tarih
2014
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Carl Hanser Verlag
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The surface roughness is one of the major parameters for determining the level of machining quality. The cutting parameters and conditions have great importance to achieve the desired values during the turning process. In the present work, a new approach was considered for modelling the effect of various turning process parameters and conditions on surface roughness. The experimental studies about the surface roughness after the turning process documented in the literature were collected and compiled into a model based on a genetic learning algorithm. As input parameters for modeling the work piece alloy type, tool type, tool tip radius, tool coating type, cooling conditions, cutting speed, feed rate, and cut depth were used in the study and were comprehensivly compiled.
Açıklama
Anahtar Kelimeler
Materials Testing, Genetic Algorithm, Turning, Surface Roughness
Kaynak
Materialpruefung/Materials Testing
WoS Q Değeri
N/A
Scopus Q Değeri
Q2
Cilt
56
Sayı
5
