Open Access
Issue
MATEC Web Conf.
Volume 190, 2018
5th International Conference on New Forming Technology (ICNFT 2018)
Article Number 15005
Number of page(s) 9
Section Micro cold forming, Special session SFB 747
DOI https://doi.org/10.1051/matecconf/201819015005
Published online 18 September 2018
  1. J. P. Wulfsberg, T. Redlich, and P. Kohrs, “Square Foot Manufacturing. a new production concept for micro manufacturing,” Production Engineering - Research and Development 4,1 (2010), 75–83. [CrossRef] [Google Scholar]
  2. H. N. Hansen, K. Carneiro, H. Haitjema, and L. de Chiffre, “Dimensional Micro and Nano Technology,” Annals of the CIRP 55 2 (2006), 721–734. [CrossRef] [Google Scholar]
  3. E. Mounier and A. Bonnabel, Press Release Emerging MEMS, 29. August 2013, 2013. [Google Scholar]
  4. M. W. Fu and W. L. Chan, “A review on the state-of-the-art microforming technologies,” The International Journal of Advanced Manufacturing Technology 67 9 (2012), 2411–2437. [Google Scholar]
  5. E. P. DeGarmo, J. T. Black, and R. A. Kohser, Materials and Processes in Manufacturing, 9th (Wiley, 2003). [Google Scholar]
  6. H. Flosky and F. Vollertsen, “Wear behaviour in a combined micro blanking and deep drawing process,” CIRP Annals - Manufacturing Technology 63,1 (2014), 281–284. [Google Scholar]
  7. M. Geiger, M. Kleine, R. Eckstein, N. Tieslerl, and U. Engel, “Microforming,” CIRP Annals - Manufacturing Technology 50 2 (2001), 445–462. [Google Scholar]
  8. F. Vollertsen, “Categories of size effects,” Production Engineering 2 4 (2008), 377–383. [Google Scholar]
  9. S. M. Afazov, A. A. Becker, and T. H. Hyde, “Development of a Finite Element Data Exchange System for chain simulation of manufacturing processes,” Advances in Engineering Software 47 (2012), 104–113. [CrossRef] [Google Scholar]
  10. D. Rippel, M. Lütjen, and B. Scholz-Reiter, “A Framework for the Quality-Oriented Design of Micro Manufacturing Process Chains,” Journal of Manufacturing Technology Management 25 7 (2014), 1028–1049. [CrossRef] [Google Scholar]
  11. D. Rippel, M. Lütjen, and M. Freitag, “Local characterisation of variances for the planning and configuration of process chains in micro manufacturing,” Journal of Manufacturing Systems 43, Part 1 (2017), 79–87. [CrossRef] [Google Scholar]
  12. S. M. Afazov, “Modelling and simulation of manufacturing process chains,” CIRP Journal of Manufacturing Science and Technology 6,1 (2013), 70–77. [CrossRef] [Google Scholar]
  13. M. Pietrzyk, L. Madej, and S. Weglarczyk, “Tool for optimal design of manufacturing chain based on metal forming,” CIRP Annals - Manufacturing Technology 57,1 (2008), 309–312. [CrossRef] [Google Scholar]
  14. I. Sabotin, J. Valentincic, M. Junkar, and A. Sluga, “Process planning system for micro-products,” in Proceedings of the 10th International Conference on Management of Innovative Technologies (2009). [Google Scholar]
  15. B. Denkena, H. Rudzio, and A. Brandes, “Methodology for Dimensioning Technological Interfaces of Manufacturing Process Chains,” CIRP Annals - Manufacturing Technology 55,1 (2006), 497–500. [CrossRef] [Google Scholar]
  16. B. Denkena and H. K. Tönshoff, “Prozessauslegung und-integration in die Prozesskette,” in Spanen - Grundlagen, B. Denkena and H. K. Tönshoff, eds. (Springer Verlag, 2011), pp. 339–362. [Google Scholar]
  17. B. Denkena, J. Schmidt, and M. Krüger, “Data Mining Approach for Knowledge-based Process Planning,” Procedia Technology 15 (2014), 406–415. [CrossRef] [Google Scholar]
  18. Kim, Y.-H.: Moraglio, A., A. Kattan, and Y. Yoon, “Geometric Generalisation of Surrogate Model-Based Optimisation to Combinatorial and Program Spaces,” Mathematical Problems in Engineering 2014,1, 10. [Google Scholar]
  19. F. Duddeck and E. Wehrle, “Recent Advances on Surrogate Modelling for Robustness Assessment of Structures with respect to Crashworthiness Requirements,” in Proceedings of the 10th European LS-DYNA Conference 2015 (DYNAmore GmbH, 2015), p. 11. [Google Scholar]
  20. B. M. Colosimo, L. Pagani, and M. Strano, “Reduction of calibration effort in FEM-based optimization via numerical and experimental data fusion,” Structural and Multidisciplinary Optimization 51 2 (2015), 463–478. [CrossRef] [Google Scholar]
  21. E. S. Hung and S. D. Senturia, “Generating efficient dynamical models for microelectromechanical systems from a few finite-element simulation runs,” Journal of Microelectromechanical Systems 8 3 (1999), 280–289. [CrossRef] [Google Scholar]
  22. T. W. Simpson, J. D. Poplinski, N. P. Koch, and J. K. Allen, “Metamodels for Computer-based Engineering Design. Survey and recommendations,” Engineering with Computers 17 2 (2001), 129–150. [Google Scholar]
  23. R. Jin, W. Chen, and T. W. Simpson, “Comparative studies of metamodelling techniques under multiple modelling criteria,” Structural and Multidisciplinary Optimization 23,1 (2001), 1–13. [CrossRef] [Google Scholar]
  24. S. Shan and G. G. Wang, “Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions,” Structural and Multidisciplinary Optimization 41 2 (2010), 219–241. [CrossRef] [Google Scholar]
  25. T. Goel, R. T. Haftka, W. Shyy, and N. V. Queipo, “Ensemble of surrogates,” Structural and Multidisciplinary Optimization 33 3 (2007), 199–216. [CrossRef] [Google Scholar]
  26. S. Giurgea, D. Fodorean, G. Cirrincione, A. Miraoui, and M. Cirrincione, “Multimodel Optimization Based on the Response Surface of the Reduced FEM Simulation Model With Application to a PMSM,” IEEE Transactions on Magnetics 44 9 (2008), 2153–2157. [CrossRef] [Google Scholar]
  27. C. Huang, B. Radi, and A. E. Hami, “Uncertainty analysis of deep drawing using surrogate model based probabilistic method,” The International Journal of Advanced Manufacturing Technology 86 9 (2016), 3229–3240. [CrossRef] [Google Scholar]
  28. A. Messner, Kaltmassivumformung metallischer Kleinstteile: Werkstoffverhalten,Wirkflächenreibung, Prozeßauslegung. Fertigungstechnik (Meisenbach Verlag, 1998). [Google Scholar]
  29. H. Brüning and F. Vollertsen, “Energy efficiency in laser rod end melting,” in Proceedings of the LiM - Lasers in Manufacturing 2015, Vol. 138 (2015). [Google Scholar]
  30. A. Stephen and F. Vollertsen, “Influence of the Rod Diameter on the Upset Ratio in Laser-based Free Form Heading,” in Proceedings of the 10th Int. Conference on Technology of Plasticity 2011, G. Hirt and E. A. Tekkaya, eds. (Wiley, 2011), pp. 220–223. [Google Scholar]
  31. C. M. Elliot, “On the finite element approximation of an elliptic variational inequality arising from an implicit time discretization of the Stefan problem,” IMA Journal of Numerical analysis 1981,1, 115–125. [CrossRef] [MathSciNet] [Google Scholar]
  32. E. Bänsch, “Finite element discretization of the Navier-Stokes equations with a free capillary surface,” Numerische Mathematik 88 2 (2001), 203–235. [CrossRef] [MathSciNet] [Google Scholar]
  33. H. Brüning and F. Vollertsen, “Form filling behavior of preforms generated by laser rod end melting,” CIRP Annals - Manufacturing Technology 64 (2015), 293–296. [CrossRef] [Google Scholar]
  34. H. Brüning, M. Jahn, F. Vollertsen, and A. Schmidt, “Influence of laser beam absorption mechanism on eccentricity of preforms in laser rod end melting,” in Proceedings of the 11th Int. Conference on Micro Manufacturing (2016), p. 77. [Google Scholar]
  35. D. Rippel, E. Moumi, M. Lütjen, B. Scholz-Reiter, and B. Kuhfuss, “Application of Stochastic Regression for the Configuration of a Micro Rotary Swaging Processes,” Mathematical Problems in Engineering (2014), 12. [Google Scholar]
  36. M. Jahn and A. Schmidt, Finite element simulation of a material accumulation process including phase transitions and a capillary surface - Berichte aus der Technomathematik 12-03 (Universität Bremen, 2012). [Google Scholar]

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