TSK-type FLC using a combined LR and GA: Surface roughness prediction in ultraprecision turning

@article{Nandi2006TSKtypeFU,
  title={TSK-type FLC using a combined LR and GA: Surface roughness prediction in ultraprecision turning},
  author={A. Nandi},
  journal={Journal of Materials Processing Technology},
  year={2006},
  volume={178},
  pages={200-210}
}
  • A. Nandi
  • Published 2006
  • Mathematics
  • Journal of Materials Processing Technology
    • Abstract Due to non-linearity of the cutting parameters, tool–work combination and rigidity of machine tool, it has been proved that a fuzzy logic concept gives a better way to model a complex manufacturing process, such as grinding, ultraprecision turning, etc. In this paper, an Takagi–Sugeno–Kang (TSK)-type fuzzy logic controller (FLC) is designed to model the input–output relationship of ultraprecision turning. The performance of an FLC primarily depends on its knowledge base, which consists… CONTINUE READING
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    17 Citations
    Background Citations
    1
    Methods Citations
    9
    17 Citations
    Citation Type

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    • A. Nandi
    • Materials Science, Computer Science
    • J. Intell. Fuzzy Syst.
    • 2014

    References

    SHOWING 1-10 OF 23 REFERENCES
    An expert system based on FBFN using a GA to predict surface finish in ultra-precision turning
    • 27
    Design of a genetic-fuzzy system to predict surface finish and power requirement in grinding
    • 45
    Automatic design of fuzzy logic controller using a genetic algorithm—to predict power requirement and surface finish in grinding
    • 34
    Prediction of surface roughness and dimensional deviation by measuring cutting forces and vibrations in turning process
    • 229
    Surface roughness prediction in the turning of high-strength steel by factorial design of experiments
    • 205
    Hybrid approach to surface roughness evaluation in multistage machining processes
    • 58
    Surface roughness prediction based on cutting parameters and tool vibrations in turning operations
    • 289
    Modeling the surface roughness and cutting force for turning
    • 193
    Design optimization of cutting parameters for turning operations based on the Taguchi method
    • 1,115
    Accurate modeling and prediction of surface roughness by computer vision in turning operations using an adaptive neuro-fuzzy inference system
    • 136