Control of tolerances in turning by predictive control with neural networks

Stochastic and non-deterministic influences have an effect on cutting processes and lead to an unsteady and dynamic process behaviour. Concepts for the improvement of process reliability and for the control of tolerances have to be developed in order to fulfil the increasing requirements on product quality. A concept for the improvement of manufacturing accuracy through artificial neural networks (ANN) will be presented as an example for the turning process. This ANN model makes it possible to predict the dimensional deviation caused by tool wear. Feeding this back in an open loop within the machine controller the deviation can be compensated by using an adaptive control of the depth of cut.