End Milling: A Neural Approach for Defining Cutting Conditions

摘要

The purpose of this paper is to present a new adaptive solution based on a feed forward neural network (FNN) in order to improve the task of selecting cutting conditions for milling operations. From a set of inputs parameters, such as work material, its mechanical properties, and the type of cutting tool, the system suggests feed rate and cutting speed values. The four main issues related to the neural network-based techniques, namely, the selection of a proper topology of the neural network, the input representation, the training method and the output format are discussed. The proposed network was trained using a set of inputs parameters provided by cutting operations manuals and tool manufacturers catalogues. Some tests and results show that adaptative solution proposed yields performance improvements. Finally, future work and potential applications are outlined.