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Neural Predictor for Surface Roughness of Turned Parts

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Abstract

Lathes and other turning machines are widely used in the metalworking and manufacturing sector which are powered by induction motors and subject to disturbances in power quality. The dimensions and surface roughness of turned parts produced by these machines are a function of the machining parameters, material properties of the parts and tools, and tool geometry. The surface roughness of machined parts varies with changes in the electromagnetic torque on the motor shaft, and it is considered one of the main indices of finished product quality. This paper seeks to present artificial neural networks as a surface roughness predictor for turned parts, based on the values of the effective current feeding a three-phase induction motor in a machining process. Simulation and experimental results are presented to validate the performance of the proposed method under different unbalanced voltage and machining conditions.

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Acknowledgements

The authors wish to thank Araucaria Foundation (Process No. 06/56093-3), and CNPq (Process No. 474290/2008-5, No. 473576/2011-2 and No. 552269/2011-5) for their support.

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Authors and Affiliations

  1. Departments of Electrical, Mechanical and Computer Engineering, Federal Technological University of Paraná, Av. Alberto Carazzai, 1640, Centro, Cornélio Procópio, PR, 86300-000, Brazil

    Demerval Mizuyama, Carlos Elias da Silva Jr., Alessandro Goedtel, Clayton L. Graciola & Rodrigo H. C. Palácios

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  1. Demerval Mizuyama
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  2. Carlos Elias da Silva Jr.
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  3. Alessandro Goedtel
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  4. Clayton L. Graciola
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  5. Rodrigo H. C. Palácios
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Correspondence to Alessandro Goedtel.

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Mizuyama, D., da Silva, C.E., Goedtel, A. et al. Neural Predictor for Surface Roughness of Turned Parts. J Control Autom Electr Syst 29, 360–370 (2018). https://doi.org/10.1007/s40313-018-0376-9

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  • DOI: https://doi.org/10.1007/s40313-018-0376-9

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