Monitoring microdrilling operations with wavelets and neural networks Conference

Tansel, I, Mekdeci, C, Rodriguez, O et al. (1992). Monitoring microdrilling operations with wavelets and neural networks . 2 681-686.



cited authors

  • Tansel, I; Mekdeci, C; Rodriguez, O; Uragun, B

fiu authors

abstract

  • The encoding of thrust force signals of microdrilling operations with wavelet transformations and the classification of the estimated coefficients with Adaptive Resonance Theory ART2-type neural networks are proposed for detection of severe tool damage just before complete tip breakage occurs. More than 90% accuracy was observed in the sixty-one cases studied. The proposed combination was found to be a powerful signal classification tool because of its fast algorithms, self-learning capability, and extremely high computational speed on the parallel processing hardware.

publication date

  • December 1, 1992

start page

  • 681

end page

  • 686

volume

  • 2