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

Doriana M. D’Addona (), A. M. M. Sharif Ullah and D. Matarazzo
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Doriana M. D’Addona: University of Naples Federico II
A. M. M. Sharif Ullah: Kitami Institute of Technology
D. Matarazzo: University of Naples Federico II

Journal of Intelligent Manufacturing, 2017, vol. 28, issue 6, No 4, 1285-1301

Abstract: 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.

Keywords: Tool-wear; Nature-inspired computing; Pattern-recognition; Prediction; Artificial neural network; DNA-based computing (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (9)

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DOI: 10.1007/s10845-015-1155-0

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