Looking for Spring steel (55Si7) library material

khokon_sazzal

Hi, I am simulating fatigue loading on a spring steel (55si7 or AISI 55S7) specimen in Ansys. But due to proper material properties, specially S-N curve, I am unable to do it.

gnagapp

@khokon_sazzal 

If you have access to the Granta Selector product, you can search the database for the material properties. I do see several different materials show up on a quick search

If you do not have access to Granta, you may have to check with your supplier for the material properties

danielshaw

Obtaining S-N curves can be difficult. Ansys provides some in Mechanical and some in Granta. Otherwise, the user must obtain them. You may need to contact a material supplier to to obtain them.

ramgopisetti

You can construct an therotical SN curve if you have the Ultimate tensile strength of your material. the ordinates of that curve will be as follows

For an four point curve( mostly helpful)

at 0 Cycle = SUT

at 1E3 Cycle = n x SUT where n =0.8 and changes based on Manufacturing ( refer to shigley textbook)

at 1E6 Cycle = 0.5 x SUT for SUT <= 1400MPa or 700 MPa if less than 1400MPa

and at infinte cucle it just continues .

use the followign python script to visulise the plot

import matplotlib.pyplot as plt
import numpy as np
from scipy.interpolate import interp1d
SUT=650
SYT=510
SE=118.4
def sn(SUT,SYT,R,a,m):
    plt.subplot(2, 1, 1)
    X=np.array([0,1E3, 1E6,1E7] )
    Y=np.array([SUT, 0.8*SUT, 0.5*SUT, 0.5*SUT])
    plt.xlabel("Cycles")
    plt.ylabel("Strength (MPa)")
    plt.grid()
    plt.plot(X,Y)
    plt.subplot(2, 1, 2)
    X1 = np.array([SYT, 0])
    X2 = np.array([SUT, 0])
    Y1 = np.array([0, SYT])
    Y2 = np.array([0, 0.5*SUT])
    plt.plot(X1, Y1, label='Yield Line')
    plt.plot(X2, Y2, label='Modified GoodMan')
    yu = R * X2
    plt.plot(X2, yu, label='Ratio=%f' % (R))
    plt.axis([0, SUT, 0, SYT])
    plt.hlines(y=a,xmin=0,xmax=m)
    plt.vlines(x=m,ymin=0,ymax=a)
    plt.xlabel("Mean stress (MPa)")
    plt.ylabel("Alternating Stress (MPa)")
    plt.legend(loc='upper right')

    plt.show()

This is theroy,you can extend this idea as you want and fit your results.