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Tool-wear prediction and pattern-recognition using artificial neural network and DNA-based computing

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Abstract

Managing tool-wear is an important issue associated with all material removal processes. This paper deals with the application of two nature-inspired computing techniques, namely, artificial neural network (ANN) and (in silico) DNA-based computing (DBC) for managing the tool-wear. Experimental data (images of worn-zone of cutting tool) has been used to train the ANN and, then, to perform the DBC. It is demonstrated that the ANN can predict the degree of tool-wear from a set of tool-wear images processed under a given procedure whereas the DBC can identify the degree of similarity/dissimilar among the processed images. Further study can be carried out while solving other complex problems integrating ANN and DBC where both prediction and pattern-recognition are two important computational problems that need to be solved simultaneously.

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Authors and Affiliations

  1. Fraunhofer Joint Laboratory of Excellence for Advanced Manufacturing Engineering (Fh-J_LEAPT), Department of Chemical, Materials and Production Engineering, University of Naples Federico II, Piazzale Tecchio 80, 80125, Naples, Italy

    Doriana M. D’Addona & D. Matarazzo

  2. Department of Mechanical Engineering, Kitami Institute of Technology, 165 Koencho, Kitami, Hokkaido, 090-8507, Japan

    A. M. M. Sharif Ullah

Authors
  1. Doriana M. D’Addona
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  2. A. M. M. Sharif Ullah
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  3. D. Matarazzo
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Correspondence to Doriana M. D’Addona.

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D’Addona, D.M., Ullah, A.M.M.S. & Matarazzo, D. Tool-wear prediction and pattern-recognition using artificial neural network and DNA-based computing. J Intell Manuf 28, 1285–1301 (2017). https://doi.org/10.1007/s10845-015-1155-0

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  • DOI: https://doi.org/10.1007/s10845-015-1155-0

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