Einfluss des bearbeitungsbedingten Werkstoffzustands auf das belastungsinduzierte Abbauverhalten von Eigenspannungen.
Zusammenfassung der Projektergebnisse
The objectives of this project were the understanding of the relation between process load and the resulting surface and subsurface material properties in deep rolling and the determination of the influence of these material properties on the lifespan of the material. To fulfill these objectives, investigations on the influence of the internal mechanical load on the residual stress state have been performed. These investigations result in a model which is able to transfer residual stress profiles between different workpiece diameters and predict the residual stress state depending on the mechanical loads and the overlap. To investigate the temperatures, which could have an influence on the formation of white etching layers, innovative methods to measure the workpiece temperatures have been developed and only a slight temperature increment could be detected. To detect the subsurface deformation during deep rolling, microcinematographic observations during an analogy experiment have been performed. While it was possible to visualize the contact point, the subsurface deformation could not be observed due to low resolution. This method could later be successfully used when improved measurement methods are available. To identify process parameters which are able to induce white etching layers, experiments using different heat treatments have been performed. Here, martensitic steels showed a tendency for white layer buildup. Parameterwise, a higher deep rolling pressure pw which induces higher grain refinement and lattice distortion and an increased feed, which leads to higher work hardening, increased this tendency. Subsequently, fatigue experiments using parameter combinations obtained in WP1 have been performed. Here, it was possible to use samples with similar surface and different subsurface residual stresses. During static four point bending test, a residual stress relaxation could be detected using the profile with a lower penetration depth. In later performed rotating bending tests, a higher lifetime and less residual stress relaxation could be detected using the profile with a higher penetration depth. The obtained results prove that the subsurface residual stresses can be systematically influenced by adjusting the mechanical process load and play an important role for the fatigue behavior of components.
Projektbezogene Publikationen (Auswahl)
- (2020) Analytic roughness prediction by deep rolling. Prod. Eng. Res. Devel. (Production Engineering) 14 (3) 345–354
Denkena, B.; Abrão, A.; Krödel, A.; Meyer, K.
(Siehe online unter https://doi.org/10.1007/s11740-020-00961-0) - (2020) Formation of White Etching Layers by Deep Rolling of AISI 4140 Steel. J. of Materi Eng and Perform (Journal of Materials Engineering and Performance) 29 (7) 4351–4359
Souza, Poliana S.; Cangussu, Vinicius M.; Câmara, Marcelo A.; Abrão, Alexandre M.; Denkena, Berend; Breidenstein, Bernd; Meyer, Kolja
(Siehe online unter https://doi.org/10.1007/s11665-020-04988-3) - Analytical Modeling of Surface Roughness, Hardness and Residual Stress Induced by Deep Rolling. Journal of Manufacturing Processes, 26, 876 - 884, 2017
Magalhães, F., Abrão, A., Denkena, B., Breidenstein, B., Mörke, T.
(Siehe online unter https://doi.org/10.1007/s11665-016-2486-5) - Correlation between process load and deep rolling induced residual stress profiles, Procedia CIRP, 78, 161-165, 2018
Denkena, B., Grove, T., Breidenstein, B., Abrão, A., Meyer, K.
(Siehe online unter https://doi.org/10.1016/j.procir.2018.09.063) - The effect of deep rolling on the surface finish and fatigue life of AISI 4140 steel. Acta Mechanìca et Mobilitatem, 3, 9 - 13, 2018
Grabe, T., Abrão, A., Leal, C., Denkena, B., Breidenstein, B., Meyer, K.
- Prediction of surface residual stress and hardness induced by ball burnishing through neural networks. International Journal of Materials Research, 14, 295 - 310, 2019
Magalhães, F., Ventura, C., Abrão, A., Denkena, B., Breidenstein, B., Meyer, K.
(Siehe online unter https://doi.org/10.1504/IJMR.2019.100994)
