Abstract
The aim of the present work is to develop a statistical approach for the correlation between the quality of metallic materials with respect to the size and arrangement of inclusions and fatigue life in the VHCF regime by using the example of an austenitic stainless steel AISI 304. For this purpose, the size and location of about 60000 inclusions on cross sections of AISI 304 sheet in both longitudinal and transversal directions were measured and subsequently modeled using conventional statistical functions. In this way a statistical model of inclusion population in AISI 304 was created. The model forms a database for the subsequent statistical prediction of inclusion distribution in fatigue specimens and the corresponding fatigue lives. By applying the extreme value theory the biggest measured inclusions were used in order to predict the maximum inclusion size in the highest stressed volume of fatigue specimens and the results were compared with the failure-relevant inclusions. The location of the crack initiating inclusions was defined based on the modeled inclusion population and the stress distribution in the fatigue specimen, using the probabilistic Monte Carlo framework. Reasonable agreement was obtained between modeling and experimental results.
Highlights
The VHCF behaviour of metallic materials containing microstructural defects such as non-metallic inclusions is determined by the size and distribution of the damage dominating defects
The location of the crack initiating inclusions was defined based on the modeled inclusion population and the stress distribution in the fatigue specimen, using the probabilistic Monte Carlo framework
The aim of the present work is to develop a statistical approach for the correlation between the quality of metallic materials with respect to the size and arrangement of inclusions and fatigue life in the VHCF regime by using the example of an austenitic stainless steel AISI 304 [2]
Summary
The VHCF behaviour of metallic materials containing microstructural defects such as non-metallic inclusions is determined by the size and distribution of the damage dominating defects. The size and location of about 60.000 inclusions measured on the longitudinal and transversal cross sections of AISI 304 sheet form a database for the probabilistic determination of failure-relevant inclusion distribution in fatigue specimens and their corresponding fatigue lifes. By applying the method of Murakami et al the biggest measured inclusions were used in order to predict the size of failure-relevant inclusions in the fatigue specimens.
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