Incorporation of Mean/Maximum Stress Effects in the Multiaxial Racetrack Filter

Abstract

This work extends the Multiaxial Racetrack Filter (MRF) to incorporate mean or maximum stress effects, adopting a filter amplitude that depends on the current stress level along the stress or strain path. In this way, a small stress or strain amplitude event can be filtered out if associated with a non-damaging low mean or peak stress level, while another event with the very same amplitude can be preserved if happening under a more damaging high mean or peak stress level. The variable value of the filter amplitude must be calculated in real time, thus it cannot depend on the peak or mean stresses along a load event, because it would require cycle identification and as so information about future events. Instead, mean/maximum stress effects are modeled in the filter as a function of the current (instantaneous) hydrostatic or normal stress along the multiaxial load path, respectively for invariantbased and critical-plane models. The MRF efficiency is evaluated from tension-torsion experiments in 316L stainless steel tubular specimens under non-proportional (NP) load paths, showing it can robustly filter out nondamaging events even under multiaxial NP variable amplitude loading histories.

A two-step multiaxial racetrack filter algorithm for non-proportional load histories

“…Several of these points could be filtered out without compromising the subsequent multiaxial fatigue life calculations. This amplitude-filtering process is a most desirable step in practical applications, to eliminate unavoidable measurement noise and redundant over-sampled data, as well as small amplitudes that do not cause fatigue damage [12]. But it is important to avoid filtering out important counting points from multiaxial rainflow algorithms, or significant history paths that can affect the calculation of a path-equivalent stress or strain, since all stress or strain components contribute altogether for the reversals that can be eliminated.…”

“…In this example, r  80MPa was chosen as the amplitude, which in the  x   xy 3 space has a clear physical meaning: r is the Mises distance between two stress states, due to the adopted 3 scaling factor used in the shear component. The MRF algorithm, thoroughly described in [10][11][12][13], was able to reduce the 238 oversampled measurements to only 8, guaranteeing that no filtered-out data lies beyond r  80MPa of the resulting polygonal path 1-2-3-4-5-6-7-8-1. This filtering process results in a dramatic decrease of the computational time needed for further multiaxial fatigue life calculations, especially considering that the original 238 points were from a single elliptical path.…”

“…Moreover, the reversal points obtained from a multiaxial rainflow algorithm might not occur at the reversal of one of the stress or strain components [9]. In this work, a multiaxial version of the racetrack filter proposed by the authors in [10][11][12][13] is reviewed and optimized. While only requiring a single user-defined scalar filter amplitude, it is able to synchronously filter complex loading histories while preserving all key features of the loading path.…”

A fast algorithm to racetrack filter multiaxial histories preserving load shape

“…However, the fatigue limit is not only a function of the load amplitude, but also of the mean components. Thus, to incorporate mean or maximum stress effects in the MRF, a variable filter amplitude was adopted in [11], which depends on the current stress level along the stress or strain path. This way, a small amplitude event can be filtered out if associated with a non-damaging low mean or peak stress level, while another event with the very same stress or strain amplitude can be preserved if happening under a more damaging high mean or peak stress level.…”

“…Despite their associated reductions in the computational cost for fatigue damage assessments, all variations of the MRF algorithm [8,11,12] still require the definition and translation of multi-dimensional filter surfaces in computer calculations. To further reduce the computational cost, in this work the MRF algorithm is modified to eliminate the need of such multi-dimensional surfaces and translations.…”