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Mira Adolfsson

Improved fatigue lifetime prediction of large and heavy machinery components

Doctoral Student

Fatigue fracture is the leading cause of component failure in many fields. The components are subjected to cyclic loads during their lifetime which can lead to fatigue of the material(s) that the component is made of. Most fatigue fractures initiate from the material defects originated for example from the manufacturing process. To successfully predict the lifetime of a component the analysis should consider the microstructure and defects of the used material and the effects of surface finish, residual stresses and surface treatments. Traditionally the fatigue lifetime is predicted using data from small laboratory samples and reduction factors that try to take into consideration all the parameters that reduce the performance of the material. However, the small samples don’t accurately represent the microstructure of a large component thus leading the prediction of a fatigue lifetimes to often be very conservative.

This research aims to answer to the need of more accurate fatigue lifetime prediction of components by testing the fatigue properties of cast steels in a much larger scale. The data from large-scale tests is used to unify the different effects of the parameters described above to a single experimental, theoretical or numerical framework. This information helps to design the components more efficiently to the loading conditions that they are operating in.

Academic supervisor
Mikko Hokka
Matti Isakov
Industry partner
Juuso Terva

Metso

Fatigue fracture is the leading cause of component failure in many fields. The components are subjected to cyclic loads during their lifetime which can lead to fatigue of the material(s) that the component is made of. Most fatigue fractures initiate from the material defects originated for example from the manufacturing process. To successfully predict the lifetime of a component the analysis should consider the microstructure and defects of the used material and the effects of surface finish, residual stresses and surface treatments. Traditionally the fatigue lifetime is predicted using data from small laboratory samples and reduction factors that try to take into consideration all the parameters that reduce the performance of the material. However, the small samples don’t accurately represent the microstructure of a large component thus leading the prediction of a fatigue lifetimes to often be very conservative.

This research aims to answer to the need of more accurate fatigue lifetime prediction of components by testing the fatigue properties of cast steels in a much larger scale. The data from large-scale tests is used to unify the different effects of the parameters described above to a single experimental, theoretical or numerical framework. This information helps to design the components more efficiently to the loading conditions that they are operating in.

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