Development of a neural-networks tool-wear monitoring system for a turning process

Abstract

This paper presents the results of developing a tool-wear monitoring system for hard turning in laboratory conditions. The system is based on modem artificial intelligence methods such as neural networks (NNs). One of the most dominant factors influencing the reliability of the turning process is the condition of the tool; thus, systems for monitoring tool conditions have been developed both in practice and in the laboratory. The paper describes research connected to the selection of methods and strategies for determining the tool-wear condition after turning on the basis of a set laboratory system model. The tool monitoring is performed by an indirect method on the basis of cutting force as one of best determiners of tool condition in the online working regime, combined with one of the artificial intelligence methods, i.e. neural networks. The paper also presents the topology of the neural network used for the training. © 2006 Journal of Mechanical Engineering. All rights reserved.