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Design of artificial neural networks for tool wear monitoring

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Abstract

An on-line scheme for tool wear monitoring using artificial neural networks (ANNs) has been proposed. Cutting velocity, feed, cutting force and machining time are given as inputs to the ANN, and the flank wear is estimated using the ANN. Different ANN structures are designed and investigated to estimate the tool wear accurately. An existing analytical model is used to obtain the data for various cutting conditions in order to eliminate the huge cost and time associated with generation of training and evaluation data. Motivated by the fact that the tool wear at a given instance of time depends on the tool wear value at a previous instance of time, memory is included in the ANN. ANNs without memory, with one-phase memory, and with two-phase memory are investigated in this study. The effect of various training parameters, such as learning coefficient, momentum, temperature, and number of hidden neurons, on these architectures is studied. The findings and experience obtained should facilitate the design and implementation of reliable and economical real-time systems for tool wear monitoring and identification in intelligent manufacturing.

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Authors and Affiliations

  1. 2 Little street, Newark, NJ, 07107, USA

    KURAPATI VENKATESH

  2. Department of Electrical and Computer Engineering, USA

    MENGCHU ZHOU

  3. Department of Mechanical Engineering, Center for Manufacturing Systems, New Jersey Institute of Technology, Newark, NJ, 07102-1982, USA

    REGGIE J. CAUDILL

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  1. KURAPATI VENKATESH
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  2. MENGCHU ZHOU
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  3. REGGIE J. CAUDILL
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VENKATESH , K., ZHOU , M. & CAUDILL , R.J. Design of artificial neural networks for tool wear monitoring. Journal of Intelligent Manufacturing 8, 215–226 (1997). https://doi.org/10.1023/A:1018573224739

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