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

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.