抄録
In order to improve machining efficiency, it is necessary at each cutting process to select the optimum tool. Therefore, the importance of tool management which considers the features of each cutting tool has been recognized. In the 1st report, a structural model based on a neural network was proposed to estimate the rest of tool life time, and it is one of the most important information on the management. In this paper, the tool management system is proposed to select the most appropriate from a collection of cutting tools. This system consists of a data base block, estimation block and tool selection block. The data base block is in charge of the information (e.g. tool geometry, rest of tool life time, measurement value such as cutting force) on tool selection. The estimation block is composed of the three neural networks for estimating the information at the early turning, latter turning or life time under the given machining conditions. The tool selection block decides the order of selection with the information integration method. The following became clear after the investigation : (1) The estimated information by the networks agreed approximately with the experimental results. (2) The appropriate on the order of tool selection with this method was recognized by comparing the measure of information led from this system with the experiments.
