Cutting parameters optimisation in milling: expert machinist knowledge versus soft computing method
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Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7034
comunitat-uji-handle3:10234/8619
comunitat-uji-handle4:
INVESTIGACIONMetadatos
Título
Cutting parameters optimisation in milling: expert machinist knowledge versus soft computing methodAutoría
Fecha de publicación
2010Editor
InderscienceISSN
1753-1039; 1753-1047Cita bibliográfica
International Journal of Mechatronics and Manufacturing Systems (2010) vol. 3, no. 1/2, p. 3-24Tipo de documento
info:eu-repo/semantics/articlePalabras clave / Materias
Machining | Parameters optimisation | ANFIS | Adaptive neuro-fuzzy inference systems | Soft computing | GAs | Genetic algorithms | Multi-objective functions | Expert machinist | Desirability functions | Artificial intelligence--Industrial applications | Soft computing | Intel·ligència artificial--Aplicacions industrials | Informàtica tova
Resumen
In traditional machining operations, cutting parameters are
usually selected prior to machining according to machining handbooks
and user’s experience. However, this method tends to be conservative
and sub-optimal ... [+]
In traditional machining operations, cutting parameters are
usually selected prior to machining according to machining handbooks
and user’s experience. However, this method tends to be conservative
and sub-optimal since part accuracy and non machining failures prevail
over machining process efficiency. In this paper, a comparison between
traditional cutting parameter optimisation by an expert machinist and
an experimental optimisation procedure based on Soft Computing
methods is conducted. The proposed methodology increases the machining
performance in 6.1% and improves the understanding of the machining
operation through the use of Adaptive Neuro-fuzzy Inference Systems [-]
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