Surface roughness fuzzy inference system within the control simulation of end milling

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@Article{Zuperl:2016:PE,
  author =       "U. Zuperl and F. Cus",
  title =        "Surface roughness fuzzy inference system within the
                 control simulation of end milling",
  journal =      "Precision Engineering",
  volume =       "43",
  pages =        "530--543",
  year =         "2016",
  ISSN =         "0141-6359",
  DOI =          "doi:10.1016/j.precisioneng.2015.09.019",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0141635915001804",
  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.",
  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

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