ASTM C1239-2013(2018) Standard Practice for Reporting Uniaxial Strength Data and Estimating Weibull Distribution Parameters for Advanced Ceramics.pdf

1、Designation: C1239 13 (Reapproved 2018)Standard Practice forReporting Uniaxial Strength Data and Estimating WeibullDistribution Parameters for Advanced Ceramics1This standard is issued under the fixed designation C1239; the number immediately following the designation indicates the year oforiginal a

2、doption or, in the case of revision, the year of last revision. A number in parentheses indicates the year of last reapproval. Asuperscript epsilon () indicates an editorial change since the last revision or reapproval.1. Scope1.1 This practice covers the evaluation and reporting ofuniaxial strength

3、 data and the estimation of Weibull probabilitydistribution parameters for advanced ceramics that fail in abrittle fashion (see Fig. 1). The estimated Weibull distributionparameters are used for statistical comparison of the relativequality of two or more test data sets and for the prediction ofthe

4、probability of failure (or, alternatively, the fracturestrength) for a structure of interest. In addition, this practiceencourages the integration of mechanical property data andfractographic analysis.1.2 The failure strength of advanced ceramics is treated as acontinuous random variable determined

5、by the flaw population.Typically, a number of test specimens with well-definedgeometry are failed under isothermal, well-defined displace-ment and/or force-application conditions. The force at whicheach test specimen fails is recorded. The resulting failure stressdata are used to obtain Weibull para

6、meter estimates associatedwith the underlying flaw population distribution.1.3 This practice is restricted to the assumption that thedistribution underlying the failure strengths is the two-parameter Weibull distribution with size scaling. Furthermore,this practice is restricted to test specimens (t

7、ensile, flexural,pressurized ring, etc.) that are primarily subjected to uniaxialstress states. The practice also assumes that the flaw populationis stable with time and that no slow crack growth is occurring.1.4 The practice outlines methods to correct for bias errorsin the estimated Weibull parame

8、ters and to calculate confi-dence bounds on those estimates from data sets where allfailures originate from a single flaw population (that is, a singlefailure mode). In samples where failures originate from mul-tiple independent flaw populations (for example, competingfailure modes), the methods out

9、lined in Section 9 for biascorrection and confidence bounds are not applicable.1.5 This practice includes the following:SectionScope 1Referenced Documents 2Terminology 3Summary of Practice 4Significance and Use 5Interferences 6Outlying Observations 7Maximum Likelihood Parameter Estimatorsfor Competi

10、ng Flaw Distributions8Unbiasing Factors and Confidence Bounds 9Fractography 10Examples 11Keywords 12Computer Algorithm MAXL Appendix X1Test Specimens with Unidentified FractureOriginsAppendix X21.6 The values stated in SI units are to be regarded as thestandard per IEEE/ASTM SI 10.1.7 This internati

11、onal standard was developed in accor-dance with internationally recognized principles on standard-ization established in the Decision on Principles for theDevelopment of International Standards, Guides and Recom-mendations issued by the World Trade Organization TechnicalBarriers to Trade (TBT) Commi

12、ttee.2. Referenced Documents2.1 ASTM Standards:2C1145 Terminology of Advanced CeramicsC1322 Practice for Fractography and Characterization ofFracture Origins in Advanced CeramicsE6 Terminology Relating to Methods of Mechanical TestingE178 Practice for Dealing With Outlying ObservationsE456 Terminolo

13、gy Relating to Quality and StatisticsIEEE/ASTM SI 10 American National Standard for Use ofthe International System of Units (SI): The Modern MetricSystem3. Terminology3.1 Proper use of the following terms and equations willalleviate misunderstanding in the presentation of data and inthe calculation

14、of strength distribution parameters.1This practice is under the jurisdiction of ASTM Committee C28 on AdvancedCeramics and is the direct responsibility of Subcommittee C28.01 on MechanicalProperties and Performance.Current edition approved July 1, 2018. Published July 2018. Originally approvedin 199

15、3. Last previous edition approved in 2013 as C1239 13. DOI: 10.1520/C1239-13R18.2For referenced ASTM standards, visit the ASTM website, www.astm.org, orcontact ASTM Customer Service at serviceastm.org. For Annual Book of ASTMStandards volume information, refer to the standards Document Summary page

16、onthe ASTM website.Copyright ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. United StatesThis international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for theD

17、evelopment of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.13.1.1 censored strength datastrength measurements (thatis, a sample) containing suspended observations such as thoseproduced by multiple competing or

18、concurrent flaw popula-tions.3.1.1.1 Consider a sample where fractography clearly estab-lished the existence of three concurrent flaw distributions(although this discussion is applicable to a sample with anynumber of concurrent flaw distributions). The three concurrentflaw distributions are referred

19、 to here as distributions A, B, andC. Based on fractographic analyses, each test specimenstrength is assigned to a flaw distribution that initiated failure.In estimating parameters that characterize the strength distri-bution associated with flaw distribution A, all test specimens(and not just those

20、 that failed from Type A flaws) must beincorporated in the analysis to ensure efficiency and accuracyof the resulting parameter estimates. The strength of a testspecimen that failed by a Type B (or Type C) flaw is treated asa right censored observation relative to the A flaw distribution.Failure due

21、 to a Type B (or Type C) flaw restricts, or censors,the information concerning Type A flaws in a test specimen bysuspending the test before failure occurred by a Type A flaw(1).3The strength from the most severe Type A flaw in thosetest specimens that failed from Type B (or Type C) flaws ishigher th

22、an (and thus to the right of) the observed strength.However, no information is provided regarding the magnitudeof that difference. Censored data analysis techniques incorpo-rated in this practice utilize this incomplete information toprovide efficient and relatively unbiased estimates of thedistribu

23、tion parameters.3.2 Definitions:3.2.1 competing failure modesdistinguishably differenttypes of fracture initiation events that result from concurrent(competing) flaw distributions.3.2.2 compound flaw distributionsany form of multipleflaw distribution that is neither pure concurrent nor pureexclusive

24、. A simple example is where every test specimencontains the flaw distribution A, while some fraction of the testspecimens also contains a second independent flaw distributionB.3.2.3 concurrent flaw distributionstype of multiple flawdistribution in a homogeneous material where every testspecimen of t

25、hat material contains representative flaws fromeach independent flaw population. Within a given testspecimen, all flaw populations are then present concurrentlyand are competing with each other to cause failure. This termis synonymous with “competing flaw distributions.”3.2.4 effective gage sectiont

26、hat portion of the test speci-men geometry that has been included within the limits ofintegration (volume, area, or edge length) of the Weibulldistribution function. In tensile test specimens, the integrationmay be restricted to the uniformly stressed central gagesection, or it may be extended to in

27、clude transition and shankregions.3.2.5 estimatorwell-defined function that is dependent onthe observations in a sample. The resulting value for a givensample may be an estimate of a distribution parameter (a pointestimate) associated with the underlying population. The arith-metic average of a samp

28、le is, for example, an estimator of thedistribution mean.3.2.6 exclusive flaw distributionstype of multiple flawdistribution created by mixing and randomizing test specimensfrom two or more versions of a material where each versioncontains a different single flaw population. Thus, each testspecimen

29、contains flaws exclusively from a single distribution,but the total data set reflects more than one type of strength-controlling flaw. This term is synonymous with “mixtures offlaw distributions.”3.2.7 extraneous flawsstrength-controlling flaws observedin some fraction of test specimens that cannot

30、be present in thecomponent being designed. An example is machining flaws inground bend test specimens that will not be present inas-sintered components of the same material.3.2.8 fractographyanalysis and characterization of pat-terns generated on the fracture surface of a test specimen.Fractography

31、can be used to determine the nature and locationof the critical fracture origin causing catastrophic failure in anadvanced ceramic test specimen or component.3.2.9 fracture originthe source from which brittle fracturecommences (Terminology C1145).3.2.10 multiple flaw distributionsstrength-controllin

32、gflaws observed by fractography where distinguishably differentflaw types are identified as the failure initiation site withindifferent test specimens of the data set and where the flaw typesare known or expected to originate from independent causes.3.2.10.1 DiscussionAn example of multiple flaw dis

33、tribu-tions would be carbon inclusions and large voids which mayboth have been observed as strength-controlling flaws within adata set and where there is no reason to believe that thefrequency or distribution of carbon inclusions created during3The boldface numbers in parentheses refer to the list o

34、f references at the end ofthis practice.FIG. 1 Example of Weibull Plot of Strength DataC1239 13 (2018)2fabrication was in any way dependent on the frequency ordistribution of voids (or vice-versa).3.2.11 populationtotality of potential observations aboutwhich inferences are made.3.2.12 population me

35、anaverage of all potential measure-ments in a given population weighted by their relative frequen-cies in the population.3.2.13 probability density functionfunction f(x) is a prob-ability density function for the continuous random variable Xif:fx! $0 (1)and*2fx! dx 5 1 (2)The probability that the ra

36、ndom variable X assumes avalue between a and b is given by the following equation:Pra,X,b! 5 *abfx! dx (3)3.2.14 samplecollection of measurements or observationstaken from a specified population.3.2.15 skewnessterm relating to the asymmetry of a prob-ability density function. The distribution of fai

37、lure strength foradvanced ceramics is not symmetric with respect to themaximum value of the distribution function, but has one taillonger than the other.3.2.16 statistical biasinherent to most estimates, this is atype of consistent numerical offset in an estimate relative to thetrue underlying value

38、. The magnitude of the bias error typicallydecreases as the sample size increases.3.2.17 unbiased estimatorestimator that has been cor-rected for statistical bias error.3.2.18 Weibull distributioncontinuous random variable Xhas a two-parameter Weibull distribution if the probabilitydensity function

39、is given by the following equations:fx! 5SmDSxDm21expF2SxDmGx.0 (4)fx! 5 0 x #0 (5)and the cumulative distribution function is given by thefollowing equations:Fx! 5 1 2 expF2SxDmGx.0 (6)orFx! 5 0 x #0 (7)where:m = Weibull modulus (or the shape parameter) (0), and = scale parameter (0).3.2.19 The ran

40、dom variable representing uniaxial tensilestrength of an advanced ceramic will assume only positivevalues, and the distribution is asymmetrical about the mean.These characteristics rule out the use of the normal distribution(as well as others) and point to the use of the Weibull andsimilar skewed di

41、stributions. If the random variable represent-ing uniaxial tensile strength of an advanced ceramic is char-acterized by Eq 4-7, then the probability that this advancedceramic will fail under an applied uniaxial tensile stress isgiven by the cumulative distribution function as follows:Pf5 1 2 expF2SD

42、mG.0 (8)Pf5 0 #0 (9)where:Pf= probability of failure, and= Weibull characteristic strength.Note that the Weibull characteristic strength is dependent onthe uniaxial test specimen (tensile, flexural, or pressurized ring)and will change with test specimen size and geometry. Inaddition, the Weibull cha

43、racteristic strength has units of stressand should be reported using units of megapascals or gigapas-cals.3.2.20 An alternative expression for the probability offailure is given by the following equation:Pf5 1 2 expF2*vS0DmdVG.0 (10)Pf5 0 #0 (11)The integration in the exponential is performed over a

44、lltensile regions of the test specimen volume if the strength-controlling flaws are randomly distributed through the volumeof the material, or over all tensile regions of the test specimenarea if flaws are restricted to the test specimen surface. Theintegration is sometimes carried out over an effec

45、tive gagesection instead of over the total volume or area. In Eq 10, 0isthe Weibull material scale parameter. The parameter is amaterial property if the two-parameter Weibull distributionproperly describes the strength behavior of the material. Inaddition, the Weibull material scale parameter can be

46、 describedas the Weibull characteristic strength of a test specimen withunit volume or area forced in uniform uniaxial tension. TheWeibull material scale parameter has units ofstress(volume)1/mand should be reported using units ofMPa(m)3/mor GPa(m)3/mif the strength-controlling flaws aredistributed

47、through the volume of the material. If the strength-controlling flaws are restricted to the surface of the testspecimens in a sample, then the Weibull material scale param-eter should be reported using units of MPa(m)2/morGPa(m)2/m. For a given test specimen geometry, Eq 8 and Eq10 can be equated, w

48、hich yields an expression relating 0and. Further discussion related to this issue can be found in 8.6.3.3 For definitions of other statistical terms, terms related tomechanical testing, and terms related to advanced ceramicsused in this practice, refer to Terminologies E456, C1145, andE6 or to appro

49、priate textbooks on statistics (2-5).3.4 Symbols:A = test specimen area (or area of effective gage section,if used).b = gage section dimension, base of bend test specimen.d = gage section dimension, depth of bend test specimen.F(x) = cumulative distribution function.f(x) = probability density function.C1239 13 (2018)3Li= length of the inner span for a bend test specimen.Lo= length of the outer span for a bend test specimen.+ = likelihood function.m = Weibull modulus.m = estimate of the Weibull modulus.mU= unbiased estimate