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Surface roughness prediction in machining of cast polyamide using neural network

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Abstract

This paper is about predicting the surface roughness by means of neural network approach method on machining of an engineering plastic material. The work material was an extruded PA6G cast polyamide for the machining tests. The network has 2 inputs called spindle speed and feed rate for this study. Output of the network is surface roughness (Ra). Gradient Descent Method was applied to optimize the weight parameters of neuron connections. The minimum Ra is obtained for 400 rpm and 251 cm/min as 0.8371 μm.

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Acknowledgments

The authors thank to Dr. Önder Ekinci and Dr. Melih İnal for their valuable contributions.

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Authors and Affiliations

  1. Electronics and Telecommunications Engineering Department, University of Kocaeli, 41040, Kocaeli, Turkey

    Serhat Yilmaz

  2. Mechanical Engineering Department, University of Kocaeli, 41040, Kocaeli, Turkey

    A. Armagan Arici & Erol Feyzullahoglu

Authors
  1. Serhat Yilmaz
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  2. A. Armagan Arici
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  3. Erol Feyzullahoglu
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Corresponding author

Correspondence to Serhat Yilmaz.

Appendix

Appendix

See Figs. 5, 6, 7.

Fig. 5
figure 5

Algorithm of the training program

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Fig. 6
figure 6

Algorithm of the performance-testing program

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Fig. 7
figure 7

Algorithm of the surface-graphing program

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Yilmaz, S., Arici, A.A. & Feyzullahoglu, E. Surface roughness prediction in machining of cast polyamide using neural network. Neural Comput & Applic 20, 1249–1254 (2011). https://doi.org/10.1007/s00521-011-0557-y

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  • DOI: https://doi.org/10.1007/s00521-011-0557-y

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