On-line learning artificial neural networks for stability classification of milling processes
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2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), Institute of Electrical and Electronics Engineers (IEEE), (Juli 2016)

For increasing the productivity in milling processes an estimation of the milling stability in dependency of critical parameters, such as spindle speed, cutting-depth and cutting-width is needed. In this paper an approach for online estimation of the milling stability classification via Artificial Neural Networks is provided. The network grows with the needs of the network and is trained with an Extended Kalman Filter. Some statistical training parameters are adapted during the training process and a first-order trust criterion is introduced to estimate the prediction quality of the network. The proposed network is capable for continuous online training with sorted input data. With the adaptive enhancements the training works well on analytical benchmark functions and with a 2-DOF milling stability simulation.
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