Surface roughness fuzzy inference system within the control simulation of end milling
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- @Article{Zuperl:2016:PE,
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author = "U. Zuperl and F. Cus",
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title = "Surface roughness fuzzy inference system within the
control simulation of end milling",
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journal = "Precision Engineering",
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volume = "43",
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pages = "530--543",
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year = "2016",
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ISSN = "0141-6359",
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DOI = "doi:10.1016/j.precisioneng.2015.09.019",
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URL = "http://www.sciencedirect.com/science/article/pii/S0141635915001804",
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abstract = "This paper presents a surface roughness control of end
milling with associated simulation block diagram. The
objective of the proposed surface roughness control is
to assure the desired surface roughness by adjusting
the cutting parameters and maintaining the cutting
force constant. For simulation purposes an
experimentally validated surface roughness control
simulator is employed. Its structure combines genetic
programming (GP), neural network (NN) and adaptive
neuro fuzzy inference system (ANFIS) based models.
Surface roughness control simulator simulates the
surface roughness of the part by enabling the
regulation of cutting force. The focus of this research
is to develop a reliable method to predict surface
roughness average during end milling process. An ANFIS
is applied to predict the effect of cutting parameters
(spindle speed, feed rate and axial/radial depth of
cut) and cutting force signals on surface roughness.
Machining experiments conducted using the proposed
method indicate that using an appropriate cutting force
signals, the surface roughness can be predicted within
3percent of the actual surface roughness for various
end-milling conditions. Simulation results are
presented to confirm the efficiency of a control
model.",
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keywords = "genetic algorithms, genetic programming, End milling,
Surface roughness, Prediction, Control simulation,
ANFIS, Neural network, GP modelling",
- }
Genetic Programming entries for
Uros Zuperl
Franci Cus
Citations