PT  - JOURNAL ARTICLE
AU  - Vrabeľ, Marek
AU  - Maňková, Ildikó
AU  - Paľo, Miroslav
AU  - Lattner, Radek
TI  - The Effect of Artificial Neural Network Architecture on Surface Roughness Parameter Prediction Capability when Turning Inconel 718
DP  - 2016 Aug 1
TA  - Manufacturing Technology Journal
PG  - 834--839
VI  - 16
IP  - 4
AID - 10.21062/ujep/x.2016/a/1213-2489/MT/16/4/834
IS  - 12132489
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.