Prof. Dr.-Ing. Marion Merklein



Organisation


Institute of Manufacturing Technology


Awards / Honours


2015 : Mitgliedschaft Berlin-Brandenburgische Akademie der Wissenschaften
2015 : Mitgliedschaft Nationale Akademie der Wissenschaften Leopoldina
2014 : Mitgliedschaft acatech - Deutsche Akademie der Technikwissenschaften
2013 : Gottfried Wilhelm Leibniz Prize
2004 : Heinz Maier-Leibnitz Prize



Project lead

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Development of a self-learning transformation and dilatometer model for the virtual process design of hot stamping processes
Prof. Dr.-Ing. Marion Merklein
(01/07/2019 - 30/06/2021)

FE-based springback prediction of sheet metal forming processes from lightweight materials considering the anisotropic hardening
Prof. Dr.-Ing. Marion Merklein
(01/02/2019 - 31/01/2021)

Improvement of the mapping accuracy in the material modeling by considering the yield locus under plane strain
Prof. Dr.-Ing. Marion Merklein
(01/01/2019 - 31/12/2020)

Beam-based strategies for the production of property optimized Tailor Heat Treated Blanks
Prof. Dr.-Ing. Marion Merklein
(01/01/2019 - 31/12/2020)

Manufacturing of high-strength aluminium parts by forming under quenching conditions
Prof. Dr.-Ing. Marion Merklein
(01/01/2016 - 31/03/2018)


Project member


SSI: Sustainable Smart Industry
Prof. Dr. Kai-Ingo Voigt
(01/01/2017 - 31/12/2019)


Publications (Download BibTeX)

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Heling, B., Oberleiter, T., Rohrmoser, A., Kiener, C., Schleich, B., Hagenah, H.,... Wartzack, S. (2019). Berücksichtigung von produktionsspezifischen Abweichungen im interdisziplinären Toleranzmanagement. In S. Wartzack (Eds.), Industriekolloquium der Forschungsgruppe FOR 2271 (pp. 36-45). Druck+Verlag Ernst Vögel GmbH.
Heling, B., Oberleiter, T., Rohrmoser, A., Kiener, C., Schleich, B., Hagenah, H.,... Wartzack, S. (2019). Berücksichtigung von produktionsspezifischen Abweichungen im interdisziplinären Toleranzmanagement. In Wartzack (Hrg.), Industriekolloquium der Forschergruppe FOR 2271 (pp. 36-45). Erlangen: Stamsried: Druck+Verlag Ernst Vögel GmbH.
Rigas, N., Degner, J., Berendt, E., Junker, F., & Merklein, M. (2019). Characterisation of new lubrication systems for hot forming of high strength aluminum alloys. In OP Conference Series, Materials Science and Engineering.
Papke, T., Huber, F., Geyer, G., Schmidt, M., & Merklein, M. (2019). Characterisation of the Tensile Bonding Strength of Ti-6Al-4V Hybrid Parts Made by Sheet Metal Forming and Laser Beam Melting. In Proceedings of the 8th Congress of the German Academic Association for Production Technology (WGP) (pp. 361-370). Aachen: Heidelberg: Springer.
Papke, T., Huber, F., Heimes, M., Schmidt, M., & Merklein, M. (2019). Charakterisierung der Umformbarkeit und mechanischen Eigenschaften von additiv gefertigtem Ti6Al4V. In Lehrstuhl für Umformtechnik, Montanuniversität Leoben, Martin Stockinger (Eds.), XXXVIII. Verformungskundliches Kolloquium (pp. 101-106).
Pilz, F., & Merklein, M. (2019). Comparison of extrusion processes in sheet-bulk metal forming for production of filigree functional elements. CIRP Journal of Manufacturing Science and Technology. https://dx.doi.org/10.1016/j.cirpj.2019.05.004
Löffler, M., Schulte, R.K., Freiburg, D., Biermann, D., Stangier, D., Tillmann, W., & Merklein, M. (2019). Control of the material flow in sheet-bulk metal forming using modifications of the tool surface. International Journal of Material Forming, 12(1), 17-26. https://dx.doi.org/10.1007/s12289-018-1399-2
Suttner, S., Schmid, H., & Merklein, M. (2019). Cross-profile deep drawing of magnesium alloy AZ31 sheet metal for springback analysis under various temperatures. In 18th International Conference on Sheet Metal, SHEMET 2019.
Huber, F., Papke, T., Kerkien, M., Tost, F., Geyer, G., Merklein, M., & Schmidt, M. (2019). Customized exposure strategies for manufacturing hybrid parts by combining laser beam melting and sheet metal forming. Journal of Laser Applications. https://dx.doi.org/10.2351/1.5096115
Jaremenko, C., Ravikumar, N., Affronti, E., Merklein, M., & Maier, A. (2019). Determination of Forming Limits in Sheet Metal Forming Using Deep Learning. Materials, 12(7). https://dx.doi.org/10.3390/ma12071051

Last updated on 2016-10-11 at 15:48