TOOL WEAR MONITORING WITH THE APPLICATION OF NEURAL NETWORKS

The paper presents the results of developing a tool wear monitoring system for hard turning in laboratory conditions, using modern artificial intelligence methods as neural networks (NN). One of the most dominant factors influencing the reliability of turning process is tool condition; thus, systems for monitoring tool conditions have been developed both in practice and in laboratory conditions. The paper shows researches connected to the selection of methods and strategies for determining tool wear condition after turning on the basis of set laboratory system model. Tool monitoring is performed by indirect method on the basis of cutting force as one of best determiners of tool condition in the on-line working regime, combined with one of artificial intelligence method, i.e. neural networks. The paper also presents the topology of the neural network used for training.