%0 Conference Paper %1 8600856 %A Kaiser, Benjamin. %A Csiszar, Akos. %A Verl, Alexander. %B 2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP) %D 2018 %K Adversarial CNC GAN Generative Network %P 1-6 %R 10.1109/M2VIP.2018.8600856 %T Generative models for direct generation of CNC toolpaths %U https://ieeexplore.ieee.org/document/8600856/ %X Today, numerical controls (CNC) are the standard for the control of machine tools and industrial robots in production and enable highly flexible and efficient production, especially for frequently changing production tasks. A numerical control has discrete inputs and outputs. Within the NC channel, however, it is necessary to analytically describe curves for the calculation of the position setpoints and the jerk limitation. The resulting change between discrete and continuous description forms and the considerable restrictions in the parallelisation of the interpolation of continuous curves within the NC channel lead to a performance overhead that limits the performance of the NC channel with regard to the calculation of new position setpoints. This can lead to a drop in production speed and thus to longer production times. To solve this problem, we propose a new approach in this paper. This is based on the use of deep generative models and allows the direct generation of interpolated toolpaths without calculation of continuous curves and subsequent discretization. The generative models are being trained to create curves of certain types such as linear and parabolic curves or splines directly as discrete point sequences. This approach is very well feasible with regard to its parallelization and reduces the computing effort within the NC channel. First results with straight lines and parabolic curves show the feasibility of this new approach for the generation of CNC toolpaths.

@inproceedings{8600856, abstract = {Today, numerical controls (CNC) are the standard for the control of machine tools and industrial robots in production and enable highly flexible and efficient production, especially for frequently changing production tasks. A numerical control has discrete inputs and outputs. Within the NC channel, however, it is necessary to analytically describe curves for the calculation of the position setpoints and the jerk limitation. The resulting change between discrete and continuous description forms and the considerable restrictions in the parallelisation of the interpolation of continuous curves within the NC channel lead to a performance overhead that limits the performance of the NC channel with regard to the calculation of new position setpoints. This can lead to a drop in production speed and thus to longer production times. To solve this problem, we propose a new approach in this paper. This is based on the use of deep generative models and allows the direct generation of interpolated toolpaths without calculation of continuous curves and subsequent discretization. The generative models are being trained to create curves of certain types such as linear and parabolic curves or splines directly as discrete point sequences. This approach is very well feasible with regard to its parallelization and reduces the computing effort within the NC channel. First results with straight lines and parabolic curves show the feasibility of this new approach for the generation of CNC toolpaths.}, added-at = {2019-10-21T16:40:24.000+0200}, author = {Kaiser, Benjamin. and Csiszar, Akos. and Verl, Alexander.}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/24b50d3f6037f8c70ca033358fcc8e5f8/benjaminkaiser}, booktitle = {2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)}, doi = {10.1109/M2VIP.2018.8600856}, interhash = {d1da54906e0f7027db093be6feaa886f}, intrahash = {4b50d3f6037f8c70ca033358fcc8e5f8}, keywords = {Adversarial CNC GAN Generative Network}, month = nov, pages = {1-6}, timestamp = {2019-10-21T14:40:24.000+0200}, title = {Generative models for direct generation of CNC toolpaths}, url = {https://ieeexplore.ieee.org/document/8600856/}, year = 2018 }