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Professor Emeritus of Mechanical Engineering Iowa State University Ames, Iowa 13.1 TESTING METHODS AND PRESENTATION OF RESULTS / 13.3 13.2 SN DIAGRAM FOR SINUSOIDAL AND RANDOM LOADING /

CHAPTER 13STRENGTH UNDER DYNAMIC

CONDITIONS

Charles R Mischke, Ph.D., RE.

Professor Emeritus of Mechanical Engineering

Iowa State University Ames, Iowa

13.1 TESTING METHODS AND PRESENTATION OF RESULTS / 13.3

13.2 SN DIAGRAM FOR SINUSOIDAL AND RANDOM LOADING / 13.7

13.3 FATIGUE-STRENGTH MODIFICATION FACTORS / 13.9

13.4 FLUCTUATING STRESS / 13.24

13.5 COMPLICATED STRESS-VARIATION PATTERNS / 13.29

13.6 STRENGTH AT CRITICAL LOCATIONS / 13.31

a Distance, exponent, constant

A Area, addition factor, IiNt

b Distance, width, exponent

B "Li2N1

bhn Brinell hardness, roller or pinion

BHN Brinell hardness, cam or gear

c Exponent

C Coefficient of variation

Cp Materials constant in rolling contact

d Difference in stress level, diameter

de Equivalent diameter

D Damage per cycle or block of cycles

D1 Ideal critical diameter

E Young's modulus

/ Fraction of mean ultimate tensile strength

fi Fraction of life measure

F Force

3* Significant force in contact fatigue

h Depth

H 8 Brinell hardness

7 Second area moment

kfl, k a Marin surface condition modification factor

k b Marin size modification factor

kc, k c Marin load modification factor

kd, k d Marin temperature modification factor

ke, k e Marin miscellaneous-effects modification factor

K Load-life constant

K/ Fatigue stress concentration factor

K, Geometric (theoretical) stress concentration factor

r Notch radius, slope of load line

r,- Average peak-to-valley distance

R Reliability

R a Average deviation from the mean

R rms Root-mean-squared deviation from the mean

RA Fraction reduction in area

RQ As-quenched hardness, Rockwell C scale

RT Tempered hardness, Rockwell C scale

5 Strength

St 1x Axial endurance limit

S' e Rotating-beam endurance limit

S f Fatigue strength

Sse Torsional endurance limit

S U9 S ut Ultimate tensile strength

z Variable, coordinate, variable of TV(O, z)

a Prot loading rate, psi/cycle

p Rectangular beam width

A Approach of center of roller

afl Normal stress amplitude component

G/ Fatigue strength coefficient

Gm Steady normal stress component

Gmax Largest normal stress

cmin Smallest normal stress

G0 Nominal normal stress

G0 Strain-strengthening coefficient

a Standard deviation

T Shear stress

(|> Pressure angle

<(> Fatigue ratio: <(>&, beading; ^t 0x , axial; <J>,, torsion; <|>o.3o, bending with

0.30-in-diameter rotating specimen

O(z) Cumulative distribution function of the standardized normal

13.1 TESTING METHODS AND PRESENTATION

OFRESULTS

The designer has need of knowledge concerning endurance limit (if one exists) andendurance strengths for materials specified or contemplated These can be estimatedfrom the following:

• Tabulated material properties (experience of others)

• Personal or corporate R R Moore endurance testing

• Uniaxial tension testing and various correlations

• For plain carbon steels, if heat treating is involved, Jominy test and estimation oftempering effects by the method of Crafts and Lamont

• For low-alloy steels, if heat treating is involved, prediction of the Jominy curve bythe method of Grossmann and Fields and estimation of tempering effects by themethod of Crafts and Lamont

• If less than infinite life is required, estimation from correlations

• If cold work or plastic strain is an integral part of the manufacturing process, usingthe method of Datsko

The representation of data gathered in support of fatigue-strength estimation is bestmade probabilistically, since inferences are being made from the testing of necessar-ily small samples There is a long history of presentation of these quantities as deter-ministic, which necessitated generous design factors The plotting of cycles to failure

as abscissa and corresponding stress level as ordinate is the common SN curve.When the presentation is made on logarithmic scales, some piecewise rectificationmay be present, which forms the basis of useful curve fits Some ferrous materialsexhibit a pronounced knee in the curve and then very little dependency of strengthwith life Deterministic researchers declared the existence of a zero-slope portion of

the curve and coined the name endurance limit for this apparent asymptote

Proba-bilistic methods are not that dogmatic and allow only such statements as, "A nullhypothesis of zero slope cannot be rejected at the 0.95 confidence level."

Based on many tests over the years, the general form of a steel SN curve is taken

to be approximately linear on log-log coordinates in the range 103 to 106 cycles andnearly invariant beyond 107 cycles With these useful approximations and knowledge

that cycles-to-failure distributions at constant stress level are lognormal (cannot berejected) and that stress-to-failure distributions at constant life are likewise lognor-mal, specialized methods can be used to find some needed attribute of the SN pic-ture The cost and time penalties associated with developing the complete picturemotivate the experimentor to seek only what is needed

13.1.1 Sparse Survey

On the order of a dozen specimens are run to failure in an R R Moore apparatus atstress levels giving lives of about 103 to 107 cycles The points are plotted on log-logpaper, and in the interval 103 < N < 107 cycles, a "best" straight line is drawn Thosespecimens which have not failed by 108 or 5 x 108 cycles are used as evidence of theexistence of an endurance limit All that this method produces is estimates of twomedian lines, one of the form

Sf = CN b 103<7V<106 (13.1)and the other of the form

Sf = S' e 7V>106 (13.2)This procedure "roughs in" the SN curve as a gross estimate No standard deviationinformation is generated, and so no reliability contours may be created

13.1.2 Constant-Stress-Level Testing

If high-cycle fatigue strength in the range of 103 to 106 cycles is required and ity (probability of survival) contours are required, then constant-stress-level testing isuseful A dozen or more specimens are tested at each of several stress levels Theseresults are plotted on lognormal probability paper to "confirm" by inspection the log-normal distribution, or a statistical goodness-of-fit test (Smirnov-Kolomogorov, chi-squared) is conducted to see if lognormal distribution can be rejected If not, thenreliability contours are established using lognormal statistics Nothing is learnedabout endurance limit Sixty to 100 specimens usually have been expended

reliabil-13.1.3 Probit Method

If statistical information (mean, standard deviation, distribution) concerning theendurance limit is needed, the probit method is useful Given a priori knowledgethat a "knee" exists, stress levels are selected that at the highest level produce one ortwo runouts and at the lowest level produce one or two failures This places the test-ing at the "knee" of the curve and within a couple of standard deviations on eitherside of the endurance limit The method requires exploratory testing to estimate thestress levels that will accomplish this The results of the testing are interpreted as alognormal distribution of stress either by plotting on probability paper or by using agoodness-of-fit statistical reduction to "confirm" the distribution If it is confirmed,the mean endurance limit, its variance, and reliability contours can be expressed Theexistence of an endurance limit has been assumed, not proven With specimensdeclared runouts if they survive to 107 cycles, one can be fooled by the "knee" of anonferrous material which exhibits no endurance limit

13.1.4 Coaxing

It is intuitively appealing to think that more information is given by a failed men than by a censored specimen In the preceding methods, many of the specimens

speci-were unfailed (commonly called runouts) Postulating the existence of an endurance

limit and no damage occurring for cycles endured at stress levels less than theendurance limit, a method exists that raises the stress level of unf ailed (by, say, 107cycles) specimens to the next higher stress level and tests to failure starting the cyclecount again Since every specimen fails, the specimen set is smaller The results areinterpreted as a normal stress distribution The method's assumption that a runoutspecimen is neither damaged nor strengthened complicates the results, since there isevidence that the endurance limit can be enhanced by such coaxing [13.1]

13.1.5 Prot Method 1

This method involves steadily increasing the stress level with every cycle Its tage is reduction in number of specimens; its disadvantage is the introduction of (1)coaxing, (2) an empirical equation, that is,

advan-S a = Se' + Ka" (13.3)

f See Ref [13.2].

where S a = Prot failure stress at loading rate, a psi/cycle

Sg = material endurance limit

K,n= material constants

a = loading rate, psi/cycle

and (3) an extrapolation procedure More detail is available in Collins [13.3]

13.1.6 Up-Down Method 1

The up-down method of testing is a common scheme for reducing R R Moore data

to an estimate of the endurance limit It is adaptable to seeking endurance strength

at any arbitrary number of cycles Figure 13.1 shows the data from 54 specimens

FIGURE 13.1 An up-down fatigue test conducted on 54 specimens.

(From Ransom [13.5], with permission.)

gathered for determining the endurance strength at 107 cycles The step size was 0.5kpsi.The first specimen at a stress level of 46.5 kpsi failed before reaching 107 cycles,and so the next lower stress level of 46.0 kpsi was used on the subsequent specimen

It also failed before 107 cycles The third specimen, at 45.5 kpsi, survived 107 cycles,and so the stress level was increased The data-reduction procedure eliminates spec-imens until the first runout-fail pair is encountered We eliminate the first specimenand add as an observation the next (no 55) specimen, a = 46.5 kpsi.The second step

is to identify the least-frequent event—failures or runouts Since there are 27 failures

and 27 runouts, we arbitrarily choose failures and tabulate N 1, iNt, and PN1 as shown

in Table 13.1 We define A = ZiN 1 and B = Zi 2N1 The estimate of the mean of the 107cycle strength is

-* =s ° +d (w^} < 13 - 4 >where S0 = the lowest stress level on which the less frequent event occurs, d = the stress-level increment or step, and N 1 = the number of less frequent events at stress level a/ Use + 1A if the less frequent event is runout and -1A if it is failure The estimate

of the mean 107-cycle strength is

f See Refs [13.4] and [13.5].

TABLE 13.1 Extension of Up-DownFatigue Data

Class failures

Stress level, Coded I \ kpsi level N t IN 1 /2AT,48.5 7 1 7 4948.0 6 4 24 14447.5 5 1 5 2547.0 4 3 12 4846.5 3 5 15 4546.0 2 8 16 3245.5 1 3 3 345.0 O _2 J) J)

tf A point estimate of the coefficient of variation is oYji = 2.93/46.27, or 0.063 ficients of variation larger than 0.1 have been observed in steels One must examinethe sources of tables that display a single value for an endurance strength to discoverwhether the mean or the smallest value in a sample is being reported This can alsoreveal the coefficient of variation This is still not enough information upon which adesigner can act

Coef-13.2 SNDIAGRAMFORSINUSOIDAL

AND RANDOM LOADING

The usual presentation of R R Moore testing results is on a plot of Sf (or S//SM)

ver-sus N, commonly on log-log coordinates because segments of the locus appear to be

rectified Figure 13.2 is a common example Because of the dispersion in results,sometimes a ±3tf band is placed about the median locus or (preferably) the datapoints are shown as in Fig 13.3 In any presentation of this sort, the only things thatmight be true are the observations All lines or loci are curve fits of convenience,there being no theory to suggest a rational form What will endure is the data and not

CYCLES TO FAILURE

FIGURE 13.2 Fatigue data on 2024-T3 aluminum alloy for narrow-band random loading, A,

and for constant-amplitude loading, O (Adapted with permission from Haugen [13.14], p 339.)

CYCLES

FIGURE 13.3 Statistical SN diagram for constant-amplitude and narrow-band random loading

for a low-alloy steel Note the absence of a "knee" in the random loading.

the loci Unfortunately, too much reported work is presented without data; henceearly effort is largely lost as we learn more.

The R R Moore test is a sinusoidal completely reversed flexural loading, which istypical of much rotating machinery, but not of other forms of fatigue Narrow-bandrandom loading (zero mean) exhibits a lower strength than constant-amplitude sine-wave loading Figure 13.3 is an example of a distributional presentation, and Fig 13.2shows the difference between sinusoidal and random-loading strength

13.3 FATIGUE-STRENGTH MODIFICATION

FACTORS

The results of endurance testing are often made available to the designer in a cise form by metals suppliers and company materials sections Plotting coordinatesare chosen so that it is just as easy to enter the plot with maximum-stress, minimum-stress information as steady and alternating stresses The failure contours areindexed from about 103 cycles up to about 109 cycles Figures 13.4,13.5, and 13.6 are

con-HGURE 13.4 Fatigue-strength diagram for 2024-T3,2024-T4, and 2014-T6 aluminum alloys,

axial loading Average of test data for polished specimens (unclad) from rolled and drawn

sheet and bar Static properties for 2024: S u = 72 kpsi, Sy = 52 kpsi; for 2014: S11 = 72 kpsi, Sy = 63 kpsi (Grumman Aerospace Corp.)

examples The usual testing basis is bending fatigue, zero mean, constant amplitude.Figure 13.6 represents axial fatigue The problem for the designer is how to adjustthis information to account for all the discrepancies between the R R Moore spec-imen and the contemplated machine part The Marin approach [13.6] is to introducemultiplicative modification factors to adjust the endurance limit, in the determinis-tic form

FIGURE 13.5 Fatigue-strength diagram for alloy steel, S u = 125 to 180 kpsi, axial loading

Aver-age of test data for polished specimens of AISI 4340 steel (also applicable to other alloy steels,

such as AISI 2330,4130,8630) (Grumman Aerospace Corp.)

FIGURE 13.6 Fatigue-strength diagram for 7075-T6 aluminum alloy, axial loading Average

of test data for polished specimens (unclad) from rolled and drawn sheet and bar Static

prop-erties: S u = 82 kpsi, S^ = 75 kpsi (Grumman Aerospace Corp.)

where k a = surface condition modification factor

k b = size modification factor

k c = loading modification factor

k d = temperature modification factor

k e = miscellaneous-effects modification factor

S' e = endurance limit of rotating-beam specimen

S e = endurance limit at critical location of part in the geometry and

condi-tion of use

The stochastic Marin equation is expressed as

Se = kAkck,keS; (13.7)where ka ~ LN(^i ka , (5 ka ), k b is deterministic, kc ~ LN([i kc , a^), k d ~ LN(\ji kd , a^), k e dis-

tribution depends on the effect considered, S/ ~ LTV(Ji^, ^Se) and &e ~ LN([i Se , (3 Se ) by

the central limit theorem of statistics Where endurance tests are not available, mates of R R Moore endurance limits are made by correlation of the mean ultimate

esti-tensile strength to the endurance limit through the fatigue ratio <(>:

$; = <№* (13.8)

The fatigue ratios of Gough are shown in Fig 13.7 He reports the coefficients ofvariation as for all metals, 0.23; for nonferrous metals, 0.20; for iron and carbonsteels, 0.14, for low-alloy steels, 0.13, and for special-alloy steels, 0.13 Since the mate-rials involved were of metallurgical interest, there are members in his ensemblesother than engineering materials Nevertheless, it is clear that knowledge of the

mean of <(> is not particularly useful without the coefficient of variation Coefficients

of variation of engineering steel endurance limits may range from 0.03 to 0.10 vidually, and the coefficient of variation of the fatigue ratio of the ensemble about0.15 For more detail, see Sec 13.3.7

indi-ROTARY BENDING FATIGUE RATIO, <|>b

FIGURE 13.7 The lognormal probability density function of the fatigue ratio

(j> ft of Gough.

CLASS NO

1 ALL METALS 380

2 NONFERROUS 152

3 IRON & CARBON STEELS 111

4 LOW ALLOY STEELS 78

5 SPECIAL ALLOY STEELS 39

13.3.1 Marin Surface Factor ka

The Marin surface condition modification factor for steels may be expressed in theform

The mean and standard deviation are

Hfa, = *S£ CT^ = Qi* (13.10)

Table 13.2 gives values of a, b, and C for various surface conditions See also Fig 13.8.

TABLE 13.2 Parameters of Marin Surface Condition Factor

1^ = OSi(I 9 C) a

Surface finish kpsi MPa b Coefficient of variation C

con-Source: Data from C G Noll and C Lipson, "Allowable Working Stresses," Society of

Experimental Stress Analysis, vol 3, no 2,1946, p 49, reduced from their graphed data points.

Example 1 A steel has a mean ultimate tensile strength of 520 MPa and a

machined surface Estimate k a

Solution: From Table 13.2,

k a = 4.45(520)-°-265(l, 0.058)u*fl - 4.45(520)-°-265(l) = 0.848a* - 4.45(520)-°-265(0.058) = 0.049The distribution is kfl ~ ZJV(0.848,0.049) The deterministic value of k a is simply themean, 0.848

In bending and torsion, where a stress gradient exists, Kuguel observed that the ume of material stressed to 0.95 or more of the maximum stress controls the risk ofencountering a crack nucleation, or growth of an existing flaw becoming critical

vol-The equivalent diameter d e of the R R Moore specimen with the same failurerisk is

*=vy^ <mi>

MEAN ULTIMATE TENSILE STRENGTH, Sut.kpsi

FIGURE 13.8 Marin endurance limit fatigue modification factor ka for

various surface conditions of steels See also Table 13.2.

where A 0-95 is the cross-sectional area exposed to 95 percent or more of the

maxi-mum stress For a round in rotating bending or torsion, A 0-95 = 0.075 515d 2 For a

round in nonrotating bending, A 0-95 = 0.010 462d 2 For a rectangular section b x h in

bending, A0.95 = Q.OSbh See [13.6], p 284 for channels and I-beams in bending Table

13.3 gives useful relations In bending and torsion,

f (de/0.30)-°-107 = 0.879d;°-107 d e in inches (13.12)

kb = \

[(4/7.62)-°107 = 1.24de-°107 d e in mm (13.13)

For axial loading, k b = 1 Table 13.4 gives various expressions for k b The Marin size

factor is scalar (deterministic) At less than standard specimen diameter (0.30 in),

many engineers set k b = l.

The Marin loading factor kc can be expressed as

fcc = oS£(l,C) (13.14)

TABLE 13.3 Equivalent Diameters for Size Factor

Section Equivalent diameter d e Round, rotary bending, torsion d

Round, nonrotating bending 0.31 d

Rectangle, nonrotating bending Q.SOSbh

TABLE 13.4 Quantitative Expression for Size Factor

Expression Range Proposer

load-shown in Fig 13.9 k c is involved in finite life fatigue also

For torsion, Table 13.7 summarizes experimental experience In metals described

by distortion-energy failure theory, the average value of k c would be 0.577 The tribution of kc is lognormal

dis-13.3.4 Marin Temperature Factor k d

Steels exhibit an increase in endurance limit when temperatures depart from ent For temperatures up to about 60O0F, such an increase is often seen, but above60O0F, rapid deterioration occurs See Fig 13.8 If specific material endurance

ambi-TABLE 13.5 Parameters of Marin Loading Factor

1^ = O(SiL(I1C)a

Mode of loading kpsi MPa (3 Coefficient of variation C

Axial 1.23 1.43 -0.078 0.126

Torsion 0.328 0.258 0.125 0.125

limit-temperature testing is not available, then an ensemble of 21 carbon and alloy

steels gives, for t fm 0F,

13.3.5 Stress Concentration and Notch Sensitivity

The modified Neuber equation (after Heywood) is

K, £0±£fi» (13.16)

1^5Sr*1

LOG NUMBER OF STRESS CYCLES N

FIGURE 13.9 An S-N diagram plotted from the results of completely

reversed axial fatigue tests on a normalized 4130 steel, SM =116 kpsi (data from

NACA Tech Note 3866, Dec 1966).

TABLE 13.6 Marin Loading Factor

for Axial Loading

NUMBER OF CYCLES TO FAILURE, MILLIONS

FIGURE 13.10 Effect of temperature on the fatigue limits of three steels: (a) 1035; (b) 1060; (c)

4340; (d) 4340, completely reversed loading K = 3 to 3.5; (e) 4340, completely reversed loading, unnotched; (/) 4340, repeatedly applied tension (American Society for Metals.)

and is lognormally distributed Table 13.8 gives values of V0 and C K The

stress-concentration factor K/ may be applied to the nominal stress CTO as K/a0 as an

aug-mentation of stress (preferred) or as a strength reduction factor k e = 1/K/ (sometimes convenient) Both K/and k e are lognormal with the same coefficient ofvariation For stochastic methods, Eq (13.16) is preferred, as it has a much larger sta-tistical base than notch sensitivity

TABLE 13.8 Heywood's Parameters, Eq (13.16), Stress Concentration

Va cKf

JJsing JJsing _Using JLJsing Using Using Feature 5u/,kpsi 5e',kpsi 5M,,MPa S'e,MPa ~S ut S' e Transverse hole 5/Sut 2.5/5; 174/5«, 87/5; 0.10 0.11 Shoulder 4/Sut 2IS' e 139/5M, 69.5/5; 0.11 0.08

Groove 3/5Mr 1.5/5; 104/5«, 52/5; 0.15 0.13

The finite life stress-concentration factor for steel for N cycles is obtained from

the notch sensitivities (#)103 and (q)<uf For 103 cycles,

(A»103-lW)IO3 = —~—i—A ^ - I

13.3.6 Miscellaneous-Effects Modification Factor k e

There are other effects in addition to surface texture, size loading, and temperaturethat influence fatigue strength These other effects are grouped together becausetheir influences are not always present, and are not weUunderstood quantitatively in

any comprehensive way They are largely detrimental (k e < 1), and consequently not be ignored For each effect present, the designer must make an estimate of the

can-magnitude and probable uncertainty of k e Such effects include

• Introduction of complete stress fields due to press or shrink fits, hydraulic sure, and the like

pres-• Unintentional residual stresses that result from grinding or heat treatment andintentional residual stresses that result from shot peening or rolling of fillets

• Unintended coatings, usually corrosion products, and intentional coatings, such asplating, paint, and chemical sheaths

• Case hardening for wear resistance by processes such as carborization, nitriding,tuftriding, and flame and induction hardening

• Decarborizing of surface material during processing

NOTCH RADIUS r, IN

(b)

FIGURE 13.11 Scatterbands of notch sensitivity q as a function of notch radius and heat

treat-ment for undifferentiated steels, (a) Quenched and tempered; (b) normalized or annealed (Adapted from Sines and Waisman [13.16], with permission of McGraw-Hill,, Inc.)

NOTCH RADIUS r, IN

FIGURE 13.12 Notch-sensitivity chart for steels and UNS A92024T wrought aluminum alloys

sub-jected to reversed bending or reversed axial loads For larger notch radii, use values of q ing to r - 0.16 in (4 mm) (From Sines and Waisman [13.16], with permission of McGraw-Hill, Inc.)

correspond-When these effects are present, tests are needed in order to assess the extent of suchinfluences and to form a rational basis for assignment of a fatigue modification

factor k e.

13.3.7 Correlation Method

The scalar fatigue ratio ty is defined as the mean endurance limit divided^by the mean

ultimate tensile strength The stochastic fatigue ratio <|> is defined as $' eISut Engineers

can estimate endurance limit by multiplying a random variable c|> by the mean

ulti-mate tensile strength Since designers have an interest in bending, axial (push-pull),and torsional fatigue, the appropriate endurance limit is correlated to the mean ulti-mate tensile strength as follows:

S; - <$> bSut (13.20)

Si=4>A, (13.22)

Rotating Bending Data for this mode of loading exist for various specimen

diam-eters, and so the size effect is mixed in With data in the form of S uh S'e, and specimen diameter d from 133 full-scale R R Moore tests, plotting In 0 versus In d leads to the

where £(0,0.137 393 3) is a normal variate Deviations from the regression line arenormal on a log-log plot Exponentiating,

<$>b = 0.445 031d-°106951 X(1,0.138 050)where X(1,0.138 050) is lognormal For the standard specimen size of 0.30 in,

4>a30 = 0.445 031(0.30)-°106951 X(1,0.138 050)-0.506X(1,0.138) (13.23)where cj>0.3o is lognormal

Axial Loading The complications of nonconcentric loading, particularly in

compression, have been mentioned Data for steels show a knee_ above S^ =

106.7(1, 0.089) when 5* is greater than 220 kpsi In the range 60 < S* < 213 kpsi

(Landgraf data),

S^ = 0.465(1,0.19)5«, (13.24)The corresponding kc is

where kc is lognormally distributed This value of kc is reported in Table 13.5 because

of the larger data base

Torsional Loading From distortion energy theory, a Marin torsional loading

modification factor is to be expected From steel data from Grover et al.,

<|>, - 0.1665^125X(1,0.263) (13.27)

f Units: strengths, kpsi; diameter or radius, in; Va, in 1 ' 2

* Deterministic values are simply the means, obtained by substituting unity for the lognormal ateA

vari-Cast iron in torsion, when behavior is described by the maximum principal stress

theory, has k c = 1, and when behavior is described by the maximum principal straintheory, exhibits

TABLE 13.9 Summary of Fatigue Equation S e = k a k b k c k d k e 4>Q 3 oS ut ,

Customary Engineering Units1

Eq (13.12)

Table 13.5 Table 13.5 Table 13.5

Eq (13.15)

Eq (13.16)

Table 13.8 Table 13.8 Table 13.8

Eq (13.13)

Table 13.5Table 13.5Table 13.5

Eq (13.16)

Table 13.8Table 13.8Table 13.8

TABLE 13.10 Summary of Fatigue Equation S6 = kAkckdke<|>o.3o5Mf, SI Units1

1 Units: strengths,MPa;diameter or radius,mm; va,mm1/2

* Deterministic values are simply means, obtained by substituting unity for the lognormal variate X.

Many tests show the behavior of cast iron falling between these two theoreticalmodels Also,

v = 0.225 - 0.003 04SM/

(13.30)

S ut = 5.76 + 0.179H B ± 5 kpsi (for gray cast iron)

Example 2 Cycles to failure of 70 000 in rotary bending at 55O0F is intended for a

round machined steel part, 1 in in diameter, in the presence of a notch (K t = 2.1) with

a 0.1-in radius The tensile strength is S ut = 100 kpsi The nominal stress is <TO ~ ZJV(12,1.2) kpsi Estimate the reliability in attaining the cycles-to-failure goal

Solution Estimate fatigue strength S/.

From Eqs (13.20) and (13.23),

S; = 4>o.3(&, = 0.506(1,0.138)1005; = 0.506(1)100-50.6 kpsi

C5-0.138