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2599 results:
  • 81. Viktor Lyamkin
    Date: 14.07.2025
    2024 - today      Scientific Assistant, University of Applied Sciences Kaiserslautern 2016 -2023        Doctoral thesis "Temporally and Spatially Resolved Characterisation of Martensitic Transformation Induced in AISI 347 Steel by Thermo-Mechanical Fatigue Loads", Saarland University 2013 – 2016  &n
  • 82. Viktor Lyamkin
    Date: 06.08.2024
    Scientific Assistant 2024 - today      Scientific Assistant, University of Applied Sciences Kaiserslautern 2016 -2023        Doctoral thesis "Temporally and Spatially Resolved Characterisation of Martensitic Transformation Induced in AISI 347 Steel by Thermo-Mechanical Fatigue Loads", Saarland Univers
  • B. Eng. Vanessa Hayna phone:       +49 631 37242448 e-mail:        vanessa.hayna@hs-kl.de building:     H room:         2.013
  • 84. Vanessa Hayna
    Date: 14.07.2025
    Research Assistant Function at the University of Applied Sciences
  • 85. Vanessa Hayna
    Date: 21.11.2024
    2024 - heute    Research Assistant Materials Science Laboratory, University of Applied Sciences Kaiserslautern 2023 - today    Master's Degree, Study of Mechanical Engineering/Mechatronics, University of Applied Sciences Kaiserslautern 2022 - today    Student Research Assistant Materials Science Lab
  • 86. Vanessa Hayna
    Date: 18.04.2023
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  • 87. Vanessa Hayna
    Date: 26.10.2023
    B. Eng. Vanessa Hayna phone:       +49 631 37242448 e-mail:        vanessa.hayna@hs-kl.de building:     H room:         2.013 Research Assistant 2024 - heute    Research Assistant Materials Science Laboratory, University of Applied Sciences Kais
  • Utilisation of statistical approaches within the scope of lifetime prediction methods for notched specimens using the example of unalloyed steels The construction of dynamically loaded metallic materials and components is usually based on the relation between the stress amplitude and the number of cycles to failure, which is displayed in S-N curve
  • The S-N curve generated using accelerated lifetime prediction methods are based on only a few real data points due to the significantly reduced experimental effort. However, the database can be significantly expanded by generating virtual data points, which makes it possible to integrate statistical methods (Point 1). In order to describe the s
  • Initial results on the integration of statistical methods into accelerated lifetime prediction using the example of the material C45E were presented at the conference “Steel Innovation” in April 2025. The procedure for generating an S-N curve with density function, to describe the scatter band, is shown and described below.
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