Open Access
Issue
MATEC Web Conf.
Volume 190, 2018
5th International Conference on New Forming Technology (ICNFT 2018)
Article Number 15008
Number of page(s) 8
Section Micro cold forming, Special session SFB 747
DOI https://doi.org/10.1051/matecconf/201819015008
Published online 18 September 2018
  1. C. von Kopylow and R. B. Bergmann, “Optical Metrology” in Micro metal forming, pp. 392–404, (Springer, Berlin Heidelberg 2013). [Google Scholar]
  2. M. Agour, R. Klattenhoff, C. Falldorf, R. B. Bergmann, Proc. SPIE 10233, 102330R (2017). [CrossRef] [Google Scholar]
  3. M. Agour, R. Klattenhoff, C. Falldorf, R. B. Bergmann, Opt. Eng. 56, 124101 (2017) [CrossRef] [Google Scholar]
  4. C Falldorf, M Agour, C Von Kopylow, RB Bergmann, J. Opt. 14, 065701(2012). [CrossRef] [Google Scholar]
  5. M. Agour, P. Huke, C. Kopylow, C. Falldorf, AIP Conference Proceedings 1236, 265-270 (2010). [CrossRef] [Google Scholar]
  6. U. Schnars and W. Jüptner, Appl. Opt. 33, 179–181(1994). [CrossRef] [PubMed] [Google Scholar]
  7. J. C. Wyant, Proc. SPIE 4737, 98 (2002). [CrossRef] [Google Scholar]
  8. D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory; C. A. Puliafito, J. G. Fujimoto, Science, 254, 1178–1181 (1991). [CrossRef] [PubMed] [Google Scholar]
  9. C. Falldorf, M. Agour, and R. B. Bergmann, Opt. Eng. 54, 024110 (2015). [CrossRef] [Google Scholar]
  10. P. Marquet, B. Rappaz, P. J. Magistretti, E. Cuche, Y. Emery, T. Colomb, and C. Depeursinge, Opt. Lett. 30, 468–470(2005). [CrossRef] [Google Scholar]
  11. M. Takeda and H. Yamamoto, Appl. Opt. 33, 7829–7837 (1994). [CrossRef] [Google Scholar]
  12. M Agour, K El-Farahaty, E Seisa, E Omar, T Sokkar, Appl. Opt. 54, E188–E195 (2015). [CrossRef] [Google Scholar]
  13. Savio, E.; De Chiffre, L.; Schmitt, R.: Metrology of freeform shaped parts. CIRP Annals-Manufacturing Technology 56 (2007), Nr. 2, S. 810-835. [CrossRef] [Google Scholar]
  14. Hernla, M.: Messunsicherheit bei Koordinatenmessungen: Abschätzung der aufgabenspezifischen Messunsicherheit mit Hilfe von Berechnungstabellen. expert verlag, 2007. - ISBN 3816926762. [Google Scholar]
  15. Hernla, M.: Abschätzung der Messunsicherheit bei Koordinatenmessungen unter den Bedingungen der industriellen Fertigung. VDI-Verlag, 1992. - ISBN 3181474029. [Google Scholar]
  16. Westkämper, E.; Stotz, M.; Effenberger, I.: Automatische Segmentierung von Messpunktwolken in regelgeometrische Elemente (Automatic Segmentation of Measurement Point Clouds to Geometric Primitives). tm-Technisches Messen 73 (2006), Nr. 1/2006, S. 60-66. [CrossRef] [Google Scholar]
  17. Goch, G.: Algorithm for the combined approximation of continuously differentiable profiles composed of straight lines and circle segments. Annals of the CIRP 40/I (1991), S. 499-502. [CrossRef] [Google Scholar]
  18. Lübke, K.; Sun, Z.; Goch, G.: Ganzheitliche Approximation eines Gerade-Kreis-Gerade-Profils mit automatischer Trennung in Einzelprofile. In: Scholl, G. (Hrsg.): XXIV. Messtechnisches Symposium des Arbeitskreises der Hochschullehrer für Messtechnik e.V. (AHMT), Hamburg. Shaker Verlag, Aachen, 2010. - ISBN 978-3-8322-9453-3, S. 77-90. [Google Scholar]
  19. Lübke, K.; Sun, Z.; Goch, G.: Three-dimensional holistic approximation of measured points combined with an automatic separation algorithm. CIRP Annals - Manufacturing Technology 61/I (2012), S. 499-502. [CrossRef] [Google Scholar]
  20. Zhang, P.; Mehrafsun, S.; Lübke, K.; Goch, G.; Vollertsen, F.: Laserchemische Feinbearbeitung und Qualitätsprüfung von Mikrokaltumformwerkzeugen. In: Kraft, O.; Haug, A.; Vollertsen, F.; Büttgenbach, S. (Hrsg.): 5. Kolloquium Mikroproduktion und Abschlusskolloquium SFB 499, Karlsruhe. 2011. - ISBN 978-3-86644-747-9 S. 169-176. [Google Scholar]
  21. Grubbs, F. E.: Procedures for detecting outlying observations in samples. Technometrics 11 (1969), Nr. 1, S. 1-21. [CrossRef] [Google Scholar]
  22. A. Mecke, I. Lee, J.R. Baker jr., M.M. Banaszak Holl, B.G. Orr, Eur. Phys. J. E 14, 7 (2004) [CrossRef] [EDP Sciences] [Google Scholar]
  23. K Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105). [Google Scholar]
  24. Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014). [Google Scholar]
  25. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778). [Google Scholar]
  26. Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3431-3440). [Google Scholar]
  27. Yu, Fisher, and Vladlen Koltun. "Multi-scale context aggregation by dilated convolutions." arXiv preprint arXiv:1511.07122 (2015). [Google Scholar]
  28. Vinyals, O., Toshev, A., Bengio, S., & Erhan, D. (2015, June). Show and tell: A neural image caption generator. In Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on (pp. 3156-3164). IEEE. [CrossRef] [Google Scholar]
  29. Weimer, D., Scholz-Reiter B., Shpitalni M.. "Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection." CIRP Annals-Manufacturing Technology 65.1 (2016): 417-420. [CrossRef] [Google Scholar]
  30. Staar, B.; Lütjen, M.; Freitag, M.: Präzise Oberflächendefekterkennung in Mikrobauteilen mit neuronalen Netzen. In: Vollertsen F.; Hopmann C.; Schulze V.; Wulfsberg J. (Hrsg.): 8. Kolloquium Mikroproduktion. BIAS Verlag, Bremen, 2017, S. 11-16 [Google Scholar]
  31. Masci, J., Meier, U., Ciresan, D., Schmidhuber, J., & Fricout, G. (2012, June). Steel defect classification with max-pooling convolutional neural networks. In Neural Networks (IJCNN), The 2012 International Joint Conference on (pp. 1-6). IEEE. [Google Scholar]
  32. Bradski, Gary, and Adrian Kaehler. "OpenCV." Dr. Dobb’s journal of software tools 3 (2000). [Google Scholar]
  33. Ronneberger, O., Fischer, P., & Brox, T. (2015, October). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Springer, Cham [Google Scholar]
  34. Chollet, François. "Xception: Deep learning with depthwise separable convolutions." arXiv preprint (2016). [Google Scholar]
  35. Ioffe, S., & Szegedy, C. (2015, June). Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning (pp. 448-456). [Google Scholar]
  36. Clevert, Djork-Arné, Thomas Unterthiner, and Sepp Hochreiter. "Fast and accurate deep network learning by exponential linear units (elus)." arXiv preprint arXiv:1511.07289 (2015). [Google Scholar]
  37. Kingma, Diederik P., and Jimmy Ba. "Adam: A method for stochastic optimization." arXiv preprint arXiv:1412.6980 (2014). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.