%0 Journal Article %1 Friedrich2015 %A Friedrich, Jens %A Hinze, Christoph %A Renner, Anton %A Verl, Alexander %A Lechler, Armin %D 2015 %J Robotics and Computer-Integrated Manufacturing %K Learning Milling Neural Process Support algorithms machines myown network stability vector xfh xlr xvl %P - %R http://dx.doi.org/10.1016/j.rcim.2015.10.003 %T Estimation of stability lobe diagrams in milling with continuous learning algorithms %U http://www.sciencedirect.com/science/article/pii/S0736584515001155 %X Abstract The productivity of milling processes is limited by the occurrence of chatter vibrations. The correlation of the maximum stable cutting depth and the spindle speed can be shown in a stability lobe diagram (SLD). The stability is different for different width of cut and can change with the axis positions. Today it is already a great effort to estimate the \SLD\ only for one position. Many experiments are necessary to measure the \SLD\ or derive a detailed mathematical model to calculate the SLD. Moreover not only the cutting depth, but also the cutting width should be represented in the SLD. This paper presents a new approach to assess the process stability based on measured acceleration signals. The multidimensional stability lobe diagram (MSLD) are derived during the production using two new continuously learning algorithms. In this paper the application of a continuous learning support vector machine and a continuous neural network is shown. The support vector machine and the neural network are extended to make them capable for continuous learning and time-variant systems. A new trust criterion is introduced, which gives information about the prediction quality of the output for the selected input region. The learned \MSLDs\ are evaluated against analytically calculated \MSLDs\ and the learning algorithms can reproduce the analytical results very well.

@article{Friedrich2015, abstract = {Abstract The productivity of milling processes is limited by the occurrence of chatter vibrations. The correlation of the maximum stable cutting depth and the spindle speed can be shown in a stability lobe diagram (SLD). The stability is different for different width of cut and can change with the axis positions. Today it is already a great effort to estimate the \{SLD\} only for one position. Many experiments are necessary to measure the \{SLD\} or derive a detailed mathematical model to calculate the SLD. Moreover not only the cutting depth, but also the cutting width should be represented in the SLD. This paper presents a new approach to assess the process stability based on measured acceleration signals. The multidimensional stability lobe diagram (MSLD) are derived during the production using two new continuously learning algorithms. In this paper the application of a continuous learning support vector machine and a continuous neural network is shown. The support vector machine and the neural network are extended to make them capable for continuous learning and time-variant systems. A new trust criterion is introduced, which gives information about the prediction quality of the output for the selected input region. The learned \{MSLDs\} are evaluated against analytically calculated \{MSLDs\} and the learning algorithms can reproduce the analytical results very well. }, added-at = {2016-03-21T15:25:16.000+0100}, author = {Friedrich, Jens and Hinze, Christoph and Renner, Anton and Verl, Alexander and Lechler, Armin}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/28c494972705666f29df6c2d8f9c80392/isw-bibliothek}, doi = {http://dx.doi.org/10.1016/j.rcim.2015.10.003}, interhash = {8f7522f978b23c86bce7a9d182e5dae6}, intrahash = {8c494972705666f29df6c2d8f9c80392}, issn = {0736-5845}, journal = {Robotics and Computer-Integrated Manufacturing }, keywords = {Learning Milling Neural Process Support algorithms machines myown network stability vector xfh xlr xvl}, pages = { - }, timestamp = {2016-03-29T15:33:36.000+0200}, title = {Estimation of stability lobe diagrams in milling with continuous learning algorithms }, url = {http://www.sciencedirect.com/science/article/pii/S0736584515001155}, year = 2015 }