Prediction method of metal material multiaxial high cycle fatigue failure including mean stress effect

Abstract

The invention discloses a prediction method of metal material multiaxial high cycle fatigue failure including mean stress effect; the prediction method of metal material multiaxial high cycle fatigue failure including mean stress effect comprises the following steps: using the uniaxial fatigue and the pure torsional fatigue as the boundary conditions for calculating the biggest damage surface born by the material during the fatigue loading process and taking the biggest damage surface as the boundary surface, selecting the positive stress and the shearing stress on the boundary surface as the damage parameter, using the mean stress effect parameter obtained by the uniaxial fatigue for correnting the stress on the boundary surface, and establishing the metal material multiaxial high cycle fatigue failure prediction model including axial direction mean stress and shearing stress influence; the prediction method of metal material multiaxial high cycle fatigue failure including mean stress effect is also applied to the situation without axial mean stress and shearing mean stress. The fatigue service life, the fatigue crack initiation and the initial extension direction of the material under multiaxial high cycle fatigue loading situation can be precisely forecasted while the axial mean stress and shearing mean stress are present.

Description

technical field [0001] The invention in this paper relates to the prediction of fatigue life and crack direction when metal materials are subjected to multiaxial fatigue loads under axial average stress and shear average stress. Using uniaxial and pure torsional fatigue performance parameters to predict multiaxial high cycle fatigue life is suitable for A variety of metallic material structures widely used in aerospace vehicles. Background technique [0002] Practical engineering structures are often subjected to complex loads. In order to better meet the structural strength and life requirements, the international fatigue community has paid more attention to multiaxial fatigue research that is more in line with actual loading conditions in recent years. In practical engineering applications, many structures and equipment, such as aircraft fuselage wings, automobile crankshafts, steam turbine rotor blades, pressure vessels, etc., bear multi-axial loads, or bear complex uniax...

AI Technical Summary

Problems solved by technology

The equivalent stress criterion is obtained based on the test data on the basis of the static strength theory. It has a simple form and is widely used in engineering, but it lacks a reasonable physical background and is generally only suitable for proportional loading; the stress invariant criterion is mostly based on hydrostatic pressure, stress deviation The second invariant of the quantity is the damage parameter, which is easy to calculate, but its effectiveness in explaining the multiaxial fatigue failure mechanism has yet to be verified, and it is difficult to determine the parameters, and it needs to be corrected when the non-proportional loading is performed; the critical surface criterion is based on the crack initiation and The mechanism of expansion has certain physical meaning. The general steps are to first select the critical surface, then evaluate the stress (strain) on the critical surface, and finally calculate the fatigue damage, but the determination of the critical surface is more complicated than calibration.

At present, the influence of axial mean stress and shear mean stress is rarely considered in multiaxial high cycle fatigue failure models. Some models in the literature use the maximum normal stress or maximum hydrostatic pressure on the critical surface to consider the influence of mean stress , some models only consider the influence of the axial mean stress but not the shear mean stress. In some models, the influence coefficient of the mean stress is difficult to determine. None of the stress-influenced multiaxial high cycle fatigue failure prediction methods is widely accepted

Images