Neural Network Based Machinability Evaluation

  • Chris Nikolopoulos
  • Iqbal Shareef
  • Donald Kalmes
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1821)

Abstract

This paper reports on the progress of an ongoing research project to investigate the feasibility of using artificial neural networks to shorten the time required for machinability testing. A neural network model is used to predict the cutting tool life for a given material. A short term test has been developed whose responses provide the input to the neural net. The results of the longterm tests, ISO 3685, are used together with the short-term test data for supervised training of the neural networks developed in this research.

Keywords

Tool Wear Tool Life Hide Node Flank Wear Acoustic Emission Signal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Chris Nikolopoulos
    • 1
  • Iqbal Shareef
    • 2
  • Donald Kalmes
    • 3
  1. 1.Department of Computer ScienceBradley UniversityBradley
  2. 2.Department of Manufacturing and Industrial EngineeringBradley UniversityBradley
  3. 3.Caterpillar Inc.PeoriaUSA

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