Constant-Amplitude Fatigue Behavior of Five Carbon or Low-Alloy Cast Steels at Room Temperature and −45°C

Abstract

Five common carbon or low-alloy cast steels—SAE 0030, SAE 0050A, C-Mn, Mn-Mo and AISI 8630—were subjected to constant-amplitude fatigue tests at room temperature and at the common low climatic temperature of −45°C (−50°F). Tests included smooth specimen axial low and high cycle fatigue and compact type (CT) specimen crack growth behavior from 10−5 n/cycle (4 × 10−4 in./cycle) to threshold values at 10−10 m/cycle (4 × 10−9 in./cycle). Three of the five steels had nil ductility transition temperatures above the low test temperature. Despite this, all five cast steels showed equivalent or better fatigue resistance at the low temperature, except for some very short life low-cycle fatigue tests and for some very high fatigue crack growth rates where fracture was imminent. Scanning electron fractographic analysis indicated that ductile type fatigue crack growth mechanisms with or without striations occurred for all steels at both test temperatures except for a few interdispersed cleavage facets in 0050A steel at very high crack growth rates. At the low temperature, monotonic and cyclic stress-strain properties Su, Sy, and Sy' increased by an average of about 10%, fatigue limits increased from 2 to 25%, and ΔKth increased from 0 to 90%. No consistent correlations existed between fatigue and monotonic properties at either temperature. Mean stress effects at near ΔKth levels appeared to be influenced by crack closure.

A modification in Weibull parameters to achieve a more accurate probability distribution function in fatigue applications

“…5 show that for the higher survival probability, the expected fatigue life would decrease at every stress level. Engineering designs usually are in the 0.01 percent probability of failure range 6 . Therefore, extrapolation is required.…”

“…Lower and upper tolerance limits in a Weibull distributed model can be calculated using Eq. ( 1a ) and ( 1b ) 6 : where is a function of the sample size.…”

“…For the evaluation of the distribution of each sample data set, two-parameter and three-parameter Weibull distribution functions can be established as 6 : where is the failure fraction in the test data set , is the minimum expected fatigue life, is characteristic fatigue life (cycles when 63.2% have failed), and is the Weibull slope or shape parameter. The terms , , and are 3-parameter Weibull model, and for the 2-parameter Weibull model, the parameter of is zero, .…”

“…The coefficient of variation (standard deviation/mean) is approximately C = l/b for the two-parameter Weibull distribution. For b values between 3 and 6, (typical of fatigue), the error from this approximation is about 10 to 15% 6 . In modern life testing analysis to obtain information about fatigue life of a component, new method of experimental process is conducted, where products are tested under higher stress than normal to get their failure information.…”

“…The importance of this idea is that generally, engineering design costs account on average for 5% of total costs in the projects 5 and most of the cost is related to conducting experimental tests. Weibull distribution is often used in preference to analyze probability aspects of fatigue results 6 and to reduce required number of experimental tests for evaluation of fatigue life distribution. The novelty of this paper is making some artificial data by considering any result of the fatigue test as an equivalent Weibull distribution with the mean value of the same test fatigue life and cumulative probability of common Weibull method.…”