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 toolwear. Experimental data (images of worn-zone of cutting tool) has been used to train theANNand, 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.

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

D'ADDONA, DORIANA MARILENA
;
2017

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 toolwear. Experimental data (images of worn-zone of cutting tool) has been used to train theANNand, 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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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11588/612642
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