A NEURAL-NETWORK SYSTEM FOR PREDICTING MACHINING BEHAVIOR

This paper describes a case study the aim of which was to apply neural -network technology to the selection of machining parameters and to th e prediction of machining performance in metal cutting. The project in volved the development of a back-propagation neural network using Neur alwork Professional II, a multi-paradigm, prototyping and development system. The network was trained using data from the Machining Data Han dbook, after training the network being able to select appropriate mac hining conditions and to predict cutting forces and surface finish for a given work material. The results so obtained are analysed and discu ssed. A novel feature of this work is the development of an on-line im plementation of the trained network using the C programming language. Current limitations of neural-network technology with respect to engin eering application are discussed also.