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Neural network based selection of optimal tool - path in free form surface machining

Korosec M., Kopaz J.

Journal of Automation Mobile Robotics and Intelligent Systems

2007

Vol. 1, No. 4

41-50

The purpose of the presented paper is to show how with the help of artificial Neural Network (NN) the prediction of milling tool-path strategies could be performed in order to determine which milling tool - path strategies or their sequences will yield the best results (i.e. the most appropriate ones) of free form surface machining, in accordance with a selected technological aim. Usually, the machining task could be completed successfully using different tool-path strategies or their sequences. They can all perform the machining task according to the demands but always only one of the all possible applied strategies is optimal in terms of the desired technological goal (surface quality in most cases). In the presented paper, the best possible surface quality of a machined surface was taken as the primary technological aim. Configuration of the applied Neural Network is presented and the whole procedure of determining the optimal tool-path sequence is shown through an example of a light switch mould. Verification of the machined surface quality, in relation to the average mean roughness Ra is also being performed and compared with the NN predicted results.