On the Statistical Size Effect of Cast Aluminium.

Roman Aigner,Michael Stoschka,Sebastian Pomberger,Martin Leitner

doi:10.3390/ma12101578

Roman Aigner, Michael Stoschka + Show 2 more

Open Access

https://doi.org/10.3390/ma12101578

Copy DOI

Journal: Materials	Publication Date: May 14, 2019
Citations: 15	License type: CC BY 4.0

Affiliation: Christian Doppler Laboratory for Thermoelectricity

Abstract

Manufacturing process based imperfections can reduce the theoretical fatigue strength since they can be considered as pre-existent microcracks. The statistical distribution of fatigue fracture initiating defect sizes also varies with the highly-stressed volume, since the probability of a larger highly-stressed volume to inherit a potentially critical defect is elevated. This fact is widely known by the scientific community as the statistical size effect. The assessment of this effect within this paper is based on the statistical distribution of defect sizes in a reference volume compared to an arbitrary enlarged volume . By implementation of the crack resistance curve in the Kitagawa–Takahashi diagram, a fatigue assessment model, based on the volume-dependent probability of occurrence of inhomogeneities, is set up, leading to a multidimensional fatigue assessment map. It is shown that state-of-the-art methodologies for the evaluation of the statistical size effect can lead to noticeable over-sizing in fatigue design of approximately . On the other hand, the presented approach, which links the statistically based distribution of defect sizes in an arbitrary highly-stressed volume to a crack-resistant dependent Kitagawa–Takahashi diagram leads to a more accurate fatigue design with a maximal conservative deviation of to the experimental validation data. Therefore, the introduced fatigue assessment map improves fatigue design considering the statistical size effect of lightweight aluminium cast alloys.

Highlights

In order to ensure conservative fatigue design of heterogeneous material, size effects have to be assessed properly, since they significantly influence the fatigue strength of engineering components.A study in [1] separates the size effects into statistical [2,3,4], geometrical [1,5,6], technological [7,8] and surface technology [9] contributions
The statistical size effect, whose improved assessment is the aim of this work, leads to a decrease of fatigue strength with elevated size of the structure or specimen due to increased probabilities of critical defect sizes
V95,A considered as reference volume V0, where the increased volume of position B is in the following referred to as highly-stressed volume V1

Summary

Introduction

In order to ensure conservative fatigue design of heterogeneous material, size effects have to be assessed properly, since they significantly influence the fatigue strength of engineering components. The Basquin equation [18] is modified invoking the fractal approach Another methodology to assess the statistical size effect is given in [19]. By evaluating the most extremal defect in a given reference volume V0 , the obtained defect sizes are to be Gumbel distributed, with the cumulative distribution function P( area) of Equation (7), characterized by its location- μ and scale parameter δ: area − μ This methodology, referred to as extreme value inclusion rating EVIR, enables the prediction of the size of a potentially critical inclusion with a spatial extent area( T ) in an enlarged control. The presented fatigue assessment map facilitates fatigue design of parts inheriting manufacturing process based imperfections with improved accuracy in terms of probabilistic assessment

Investigated Material

Fatigue Strength

Fractography

Fatigue Assessment

Conclusions