Abstract
The fine-scaled striation structure as a relevant quality feature in laser fusion cutting of sheet metals cannot be predicted from online process signals, today. High-speed recordings are used to extract a fast melt-wave signal as temporally resolved input signal and a surrogate surface profile as output. The two signals are aligned with a sliding-window algorithm and prepared for a one-step ahead prediction with neural networks. As network architecture a convolutional neural network approach is chosen and qualitatively checked for its suitability to predict the general striation structure. Test and inference of the trained model reproduce the peak count of the surface signal and prove the general applicability of the proposed method. Future research should focus on enhancements of the neural network design and on transfer of this methodology to other signal sources, that are easier accessible during laser cutting of sheet metals.
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References
Tercan, H., Al Khawli, T., Eppelt, U., Büscher, C., Meisen, T., Jeschke, S.: Improving the laser cutting process design by machine learning techniques. Prod. Eng. Res. Devel. 11(2), 195–203 (2017)
Santolini, G., Rota, P., Gandolfi, D., Bosetti, P.: Cut quality estimation in industrial laser cutting machines: a machine learning approach. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2019
Belforte, D.A.: 2017 was a great year - for industrial lasers. Ind. Laser Solutions 33(1), 11–15 (2018)
Kheloufi, K., Hachemi Amara, E., Benzaoui, A.: Numerical simulation of transient three-dimensional temperature and kerf formation in laser fusion cutting. J. Heat Transfer 137(11), 112101/1–112101/9 (2015)
Zaitsev, A.V., Ermolaev, G.V., Polyanskiy, T.A., Gurin, A.M.: Numerical simulation of the shape of laser cut for fiber and co2 lasers. In: AIP Conference Proceedings, vol. 1893, no. 1, p. 030046 (2017)
Hirano, K., Fabbro, R.: Experimental investigation of hydrodynamics of melt layer during laser cutting of steel. J. Phys. D Appl. Phys. 44(10), 105502 (2011)
Arntz, D., Petring, D., Jansen, U., Poprawe, R.: Advanced trim-cut technique to visualize melt flow dynamics inside laser cutting kerfs. J. Laser Appl. 29(2), 022213 (2017)
Horelu, A., Leordeanu, C., Apostol, E., Huru, D., Mocanu, M., Cristea, V.: Forecasting techniques for time series from sensor data. In: 2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC, pp. 261–264, September 2015
Binkowski, M., Marti, G., Donnat, P.: Autoregressive convolutional neural networks for asynchronous time series. CoRR, abs/1703.04122 (2017)
Koprinska, I., Wu, D., Wang, Z.: Convolutional neural networks for energy time series forecasting. In 2018 International Joint Conference on Neural Networks, IJCNN, pp. 1–8, July 2018
Kim, T.-Y., Cho, S.-B.: Predicting residential energy consumption using cnn-lstm neural networks. Energy 182, 72–81 (2019)
Kim, T.-Y., Cho, S.-B.: Web traffic anomaly detection using c-lstm neural networks. Expert Syst. Appl. 106, 66–76 (2018)
Wen, P., Zhang, Y., Chen, W.: Quality detection and control during laser cutting progress with coaxial visual monitoring. J. Laser Appl. 24(3), 032006 (2012)
Chollet, F., et al.: Keras (2015). https://keras.io
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv e-prints, arXiv:1412.6980 (2014)
Wang, Z., Yan, W., Oates, T.: Time series classification from scratch with deep neural networks: a strong baseline. In: 2017 International Joint Conference on Neural Networks, IJCNN, pp. 1578–1585, May 2017
Acknowledgments
All presented investigations are conducted in the context of the Collaborative Research Centre SFB1120 “Precision Melt Engineering" at RWTH Aachen University and funded by the German Research Foundation (DFG). For the sponsorship and support we wish to express our sincere gratitude.
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Halm, U., Arntz-Schroeder, D., Gillner, A., Schulz, W. (2021). Towards Online-Prediction of Quality Features in Laser Fusion Cutting Using Neural Networks. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, vol 1250. Springer, Cham. https://doi.org/10.1007/978-3-030-55180-3_26
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DOI: https://doi.org/10.1007/978-3-030-55180-3_26
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