In the last two postings, a mathematical model for predicting fatigue performance from hardness was introduced and discussed. To review, the following equation, developed by Roeselle and Fatemi, describes the prediction of strain-life curves from Brinell hardness values(1):
In the previous postings, strain life curves predicted from this equation correlated well with experimental strain life data for normalized carbon steels at hardness values of 163 to 223 BHN. In addition, good correlations were obtained for quenched and tempered low alloy steels with hardness values of 353 to 390 BHN.
Heat treatment, either normalizing or quench and tempering, offers the advantage of developing uniform microstructures, hardness values, and consistent fatigue properties. It is of interest to examine the fatigue performance of a high-hardness as-rolled steel, and to determine if the fatigue properties can be predicted from the equation described above.
C-70 is a high carbon (0.7%) steel which is produced in the as-rolled and controlled cooled condition. Figure 1 shows the strain-life curve for this steel at a hardness value of 241 BHN (Iteration No. 43). Also shown for comparison are the strain life curves of normalized SAE 1038 (163 BHN-Iteration No. 18) and quenched and tempered SAE 4140 (381 BHN Iteration-No. 67). As would be expected from the hardness values, the long life fatigue performance of the C-70 is between that of the other two grades.
Figure 2 shows experimental strain-life data for C-70 along with a strain life curve predicted from the above equation. As can be seen good correlation can between measured and predicted results was obtained. This suggests that the fatigue properties of as-rolled steels can be estimated from hardness values if fatigue data are not immediately available.
To repeat what was noted in the earlier postings on normalized steels and quenched and tempered steels, the prediction methodology cannot account for the effects of variations in microstructure, non-metallic inclusions, prior austenite grain size, surface condition or residual stress. Thus, care must be exercised in using the prediction model for design purposes.
(1) M. Roessle and A. Fatemi, International Journal of Fatigue, Vol. 22, 2000