Incremental supervised learning of cutting conditions using the fuzzy ARTMAP neural network

Abstract

As a part of an effort to systematize operation planning for cutting processes, the fuzzy ARTMAP neural network has been applied to model the process of selecting cutting conditions and subsequently to learn cutting conditions for training the model. The fuzzy ARTMAP neural network is capable of incremental supervised learning, which enables the model to be reinforced continually and efficiently. In addition, a new algorithm called the replacement algorithm is proposed. When new cutting conditions that are more effective for a certain circumstance are obtained, the proposed algorithm deletes the old information learned, and then makes the network learn the better ones. Examples of decisions of cutting conditions using the fuzzy ARTMAP neural network and the replacement algorithm are provided and discussed.