PT Journal
AU Vrabel, M
   Mankova, I
   Palo, M
   Lattner, R
TI The Effect of Artificial Neural Network Architecture on Surface Roughness Parameter Prediction Capability when Turning Inconel 718
SO Manufacturing Technology Journal
PY 2016
BP 834
EP 839
VL 16
IS 4
DI 10.21062/ujep/x.2016/a/1213-2489/MT/16/4/834
DE Artificial Neural Network; Optimization; Turning; Surface Roughness; Nickel Based Alloy
AB This paper investigates the influence of Artificial Neural Network (ANN) architectures on its prediction capability when machining nickel based super alloy. The ANN was employed to determine surface roughness parameter Ra through cutting conditions, tool wear and process monitoring indices such a cutting force components. The ANN structure was optimized by methods like a reduction of input vector parameters, dimensions of input data pattern, combined reduction and modification of hidden layers. Calculated and experimentally measured values were compared for each optimized ANN model. The work concludes that optimization of ANN has significant influence on prediction capability and accuracy for the task proposed.
ER