Introduction to stress_life

Package stress_life contains methods for damage calculations via stress life fatigue concept. Everything needed is accessible from class SN_Curve of the module SN_curve.

SN curve definition

  • It is possible to set two slopes of SN curve (low cycle fatique and high cycle fatigue).
  • Two types for meanstress sensitivity handling are implemented (see chapter below).
  • You can choose the behaviour of the SN curve after reaching the endurance limit.
  • There are various methods for modifiing the SN curve (shifting, shifting on endurance limit, changing the endurance limit number of cycles, transformation to different R-ratio ...)

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Meanstress influence

1) FMK meanstress influence

  • FKM-Guideline specifies various mean stress sensitivity factors depending on the R-ratio of the cycle.
  • Four different regimes are distinguished.

2) Goodman meanstress influence

  • distinguishes between only two sectios.
  • For R > 1 (complete compressive cycle) there is no meanstress influence (same as FKM section I).
  • Otherwise constant meanstress factor M is considered.

Simplest usage

  • Always start with SN curve definition. You can call some modification methods on that SN curve.
  • Define a load as numpy array.
  • Call some SN_Curve method "compute_D_*(load, ...)" to get the result as pandas DataFrame
import numpy as np
import pandas as pd
from stress_life import SN_curve

# create SN curve
sn = SN_curve.SN_Curve(SD=250, S1=500, k1=9, k=5, ND=2e6, M=0.22, R=0.4, meanCorrType="goodman", miner="haibach")

# create load (e.g. min and max load, number of cycles, shift factor)
load = np.array([[-800, 400, 1, 1.7],
                 [-800, 200, 1, 1.0],
                 [-800, 1000, 1, 0.7]])

# get the Damage in DataFrame object
res = sn.compute_D_l1_l2(load)

# returns
#  lower_load  upper_load   mean    amp    R  amp_R=-1  amp_R=R_SN  req_cycles  shift_factor             D  
#0     -800.0       400.0 -200.0  600.0 -2.0     556.0  367.400881         1.0           1.7  1.348129e-07   
#1     -800.0       200.0 -300.0  500.0 -4.0     434.0  286.784141         1.0           1.0  9.932204e-07  
#2     -800.0      1000.0  100.0  900.0 -0.8     922.0  609.251101         1.0           0.7  2.347872e-03