Cutting Tool Image Data Processing for Automatic Tool Wear Recognition Through Artificial Neural Networks

In a manufacturing system, 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 life of the tool under a given cutting condition as accurately as possible As the quality of the cutting tool is directly related to the quality of the product, the level of tool wear should be kept under control during machining operations. Automatic cutting tool wear monitoring is an important aspect in the improvement of production systems. In this paper, a procedure for the processing of cutting tool images detected during turning tests is presented. Images with standard size and pixel density were produced by elaborating tool image files obtained during machining tests. A methodology to design artificial neural networks for automatic tool wear recognition using standard images of cutting tool is proposed.