Manufacturing Technology 2016, 16(4):834-839 | DOI: 10.21062/ujep/x.2016/a/1213-2489/MT/16/4/834

The Effect of Artificial Neural Network Architecture on Surface Roughness Parameter Prediction Capability when Turning Inconel 718

Marek Vrabeľ1, Ildikó Maňková1, Miroslav Paľo1, Radek Lattner2
1 Faculty of Mechanical Engineering, Technical University of Kosice, Masiarska 74, 040 01 Kosice, Slovakia
2 Faculty of Production Technology and Management, J. E. Purkyne University in Usti nad Labem. Pasteurova 3334/7, 400 01 Usti nad Labem. Czech Republic

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.

Keywords: Artificial Neural Network, Optimization, Turning, Surface Roughness, Nickel Based Alloy

Published: August 1, 2016  Show citation

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Vrabeľ M, Maňková I, Paľo M, Lattner R. The Effect of Artificial Neural Network Architecture on Surface Roughness Parameter Prediction Capability when Turning Inconel 718. Manufacturing Technology. 2016;16(4):834-839. doi: 10.21062/ujep/x.2016/a/1213-2489/MT/16/4/834.
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