Image data processing via neural networks for tool wear prediction

In the manufacturing systems, one of the most important issues is to estimate the rest of cutting tool life under a given cutting conditions as accurately as possible. In fact, machining efficiency is easily influenced by the kind of tool selected at each cutting process. One of the most complex problems for tool selection is that of estimating the tool life under a given cutting conditions as accurately as possible. As the quality of the cutting tool is directed related to the quality of product, the level of tool wear should be kept under control during machining operations. In order to monitor the tool wear development during machining processes, the interface chosen between the working procedure and the computer was a digital image of the cutting tool detected by an optical sensor. Images, however, are not homogeneous. Images with standard size and pixel density were produced elaborating tool images files obtained during machining tests. This paper is focused on a procedure for the processing of cutting tool images detected during tests. A methodology to design and optimized artificial neural networks for automatic tool wear recognition using standard images of cutting tool is proposed.