# ISO 20501:2003

(Main)## Fine ceramics (advanced ceramics, advanced technical ceramics) — Weibull statistics for strength data

## Fine ceramics (advanced ceramics, advanced technical ceramics) — Weibull statistics for strength data

ISO 20501:2003 covers the reporting of uniaxial strength data and the estimation of probability distribution parameters for advanced ceramics which fail in a brittle fashion. The failure strength of advanced ceramics is treated as a continuous random variable. Typically, a number of test specimens with well-defined geometry are brought to failure under well-defined isothermal loading conditions. The load at which each specimen fails is recorded. The resulting failure stresses are used to obtain parameter estimates associated with the underlying population distribution. ISO 20501:2003 is restricted to the assumption that the distribution underlying the failure strengths is the two-parameter Weibull distribution with size scaling. Furthermore, this International Standard is restricted to test specimens (tensile, flexural, pressurized ring, etc.) that are primarily subjected to uniaxial stress states. Measurements of the strength at failure are taken for one of two reasons: either for a comparison of the relative quality of two materials, or the prediction of the probability of failure (or alternatively the fracture strength) for a structure of interest. ISO 20501:2003 permits estimates of the distribution parameters which are needed for either. In addition, this International Standard encourages the integration of mechanical property data and fractographic analysis.

## Céramiques techniques — Statistiques Weibull des données de résistance

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INTERNATIONAL ISO

STANDARD 20501

First edition

2003-12-01

Fine ceramics (advanced ceramics,

advanced technical ceramics) — Weibull

statistics for strength data

Céramiques techniques — Statistiques Weibull des données de

résistance

Reference number

ISO 20501:2003(E)

ISO 2003

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ISO 20501:2003(E)

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ii © ISO 2003 — All rights reserved

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ISO 20501:2003(E)

Contents Page

Foreword............................................................................................................................................................ iv

1 Scope...................................................................................................................................................... 1

2 Terms and definitions........................................................................................................................... 1

2.1 Defect populations................................................................................................................................ 1

2.2 Mechanical testing ................................................................................................................................ 2

2.3 Statistical terms .................................................................................................................................... 3

2.4 Weibull distributions............................................................................................................................. 4

3 Symbols ................................................................................................................................................. 5

4 Significance and use ............................................................................................................................ 6

5 Method A: maximum likelihood parameter estimators for single flaw populations ...................... 7

5.1 General................................................................................................................................................... 7

5.2 Censored data ....................................................................................................................................... 7

5.3 Likelihood functions ............................................................................................................................. 7

5.4 Bias correction ...................................................................................................................................... 8

5.5 Confidence intervals............................................................................................................................. 9

6 Method B: maximum likelihood parameter estimators for competing flaw populations ............ 11

6.1 General................................................................................................................................................. 11

6.2 Censored data ..................................................................................................................................... 12

6.3 Likelihood functions ........................................................................................................................... 12

7 Procedure............................................................................................................................................. 13

7.1 Outlying observations ........................................................................................................................ 13

7.2 Fractography ....................................................................................................................................... 13

7.3 Graphical representation.................................................................................................................... 13

8 Test report............................................................................................................................................ 16

Annex A (informative) Converting to material-specific strength distribution parameters........................ 17

Annex B (informative) Illustrative examples .................................................................................................. 19

Annex C (informative) Test specimens with unidentified fracture origin ................................................... 26

Annex D (informative) Fortran program ......................................................................................................... 29

Bibliography ..................................................................................................................................................... 33

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ISO 20501:2003(E)

Foreword

ISO (the International Organization for Standardization) is a worldwide federation of national standards bodies

(ISO member bodies). The work of preparing International Standards is normally carried out through ISO

technical committees. Each member body interested in a subject for which a technical committee has been

established has the right to be represented on that committee. International organizations, governmental and

non-governmental, in liaison with ISO, also take part in the work. ISO collaborates closely with the

International Electrotechnical Commission (IEC) on all matters of electrotechnical standardization.

International Standards are drafted in accordance with the rules given in the ISO/IEC Directives, Part 2.

The main task of technical committees is to prepare International Standards. Draft International Standards

adopted by the technical committees are circulated to the member bodies for voting. Publication as an

International Standard requires approval by at least 75 % of the member bodies casting a vote.

Attention is drawn to the possibility that some of the elements of this document may be the subject of patent

rights. ISO shall not be held responsible for identifying any or all such patent rights.

ISO 20501 was prepared by Technical Committee ISO/TC 206, Fine ceramics.iv © ISO 2003 — All rights reserved

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INTERNATIONAL STANDARD ISO 20501:2003(E)

Fine ceramics (advanced ceramics, advanced technical

ceramics) — Weibull statistics for strength data

1 Scope

This International Standard covers the reporting of uniaxial strength data and the estimation of probability

distribution parameters for advanced ceramics which fail in a brittle fashion. The failure strength of advanced

ceramics is treated as a continuous random variable. Typically, a number of test specimens with well-defined

geometry are brought to failure under well-defined isothermal loading conditions. The load at which each

specimen fails is recorded. The resulting failure stresses are used to obtain parameter estimates associated

with the underlying population distribution.This International Standard is restricted to the assumption that the distribution underlying the failure strengths

is the two-parameter Weibull distribution with size scaling. Furthermore, this International Standard is

restricted to test specimens (tensile, flexural, pressurized ring, etc.) that are primarily subjected to uniaxial

stress states. Subclauses 5.4 and 5.5 outline methods of correcting for bias errors in the estimated Weibull

parameters, and to calculate confidence bounds on those estimates from data sets where all failures originate

from a single flaw population (i.e., a single failure mode). In samples where failures originate from multiple

independent flaw populations (e.g., competing failure modes), the methods outlined in 5.4 and 5.5 for bias

correction and confidence bounds are not applicable.Measurements of the strength at failure are taken for one of two reasons: either for a comparison of the

relative quality of two materials, or the prediction of the probability of failure (or alternatively the fracture

strength) for a structure of interest. This International Standard permits estimates of the distribution

parameters which are needed for either. In addition, this International Standard encourages the integration of

mechanical property data and fractographic analysis.2 Terms and definitions

For the purposes of this document, the following terms and definitions apply.

2.1 Defect populations

2.1.1

censored strength data

strength measurements (i.e., a sample) containing suspended observations such as that produced by multiple

competing or concurrent flaw populationsNOTE Consider a sample where fractography clearly established the existence of three concurrent flaw distributions

(although this discussion is applicable to a sample with any number of concurrent flaw distributions). The three concurrent

flaw distributions are referred to here as distributions A, B, and C. Based on fractographic analyses, each specimen

strength is assigned to a flaw distribution that initiated failure. In estimating parameters that characterize the strength

distribution associated with flaw distribution A, all specimens (and not just those that failed from type-A flaws) must be

incorporated in the analysis to assure efficiency and accuracy of the resulting parameter estimates. The strength of a

specimen that failed by a type-B (or type-C) flaw is treated as a right censored observation relative to the A flaw

distribution. Failure due to a type-B (or type-C) flaw restricts, or censors, the information concerning type-A flaws in a

specimen by suspending the test before failure occurs by a type-A flaw [2]. The strength from the most severe type-A flaw

in those specimens that failed from type-B (or type-C) flaws is higher than (and thus to the right of) the observed strength.

However, no information is provided regarding the magnitude of that difference. Censored data analysis techniques

incorporated in this International Standard utilize this incomplete information to provide efficient and relatively unbiased

estimates of the distribution parameters.© ISO 2003 — All rights reserved 1

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ISO 20501:2003(E)

2.1.2

competing failure modes

distinguishably different types of fracture initiation events that result from concurrent (competing) flaw

distributions2.1.3

compound flaw distributions

any form of multiple flaw distribution that is neither pure concurrent, nor pure exclusive

NOTE A simple example is where every specimen contains the flaw distribution A, while some fraction of the

specimens also contains a second independent flaw distribution B.2.1.4

concurrent flaw distributions

a type of multiple flaw distribution in a homogeneous material where every specimen of that material contains

representative flaws from each independent flaw populationNOTE Within a given specimen, all flaw populations are then present concurrently and are competing with each other

to cause failure. This term is synonymous with “competing flaw distributions”.2.1.5

exclusive flaw distributions

a type of multiple flaw distribution created by mixing and randomizing specimens from two or more versions of

a material where each version contains a different single flaw populationNOTE Thus, each specimen 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 “mixture flaw distributions”.

2.1.6extraneous flaws

strength-controlling flaws observed in some fraction of test specimens that cannot be present in the

component being designedNOTE An example is machining flaws in ground bend specimens that will not be present in as-sintered components

of the same material.2.2 Mechanical testing

2.2.1

effective gauge section

that portion of the test specimen geometry included within the limits of integration (volume, area or edge

length) of the Weibull distribution functionNOTE In tensile specimens, the integration may be restricted to the uniformly stressed central gauge section, or it

may be extended to include transition and shank regions.2.2.2

fractography

the analysis and characterization of patterns generated on the fracture surface of a test specimen

NOTE Fractography can be used to determine the nature and location of the critical fracture origin causing

catastrophic failure in an advanced ceramic test specimen or component.2.2.3

proof testing

applying a predetermined load to every test specimen (or component) in a batch or a lot over a short period of

time to ascertain if the specimen fails due to a serious strength limiting defect

NOTE This procedure, when applied to all specimens in the sample, removes potentially weak specimens and

modifies the statistical characteristics of the surviving samples.2 © ISO 2003 — All rights reserved

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ISO 20501:2003(E)

2.3 Statistical terms

2.3.1

confidence interval

interval within which one would expect to find the true population parameter

NOTE Confidence intervals are functionally dependent on the type of estimator utilized and the sample size. The

level of expectation is associated with a given confidence level. When confidence bounds are compared to the parameter

estimate one can quantify the uncertainty associated with a point estimate of a population parameter.

2.3.2confidence level

probability that the true population parameter falls within a specified confidence interval

2.3.3estimator

well-defined function that is dependent on the observations in a sample

NOTE The resulting value for a given sample may be an estimate of a distribution parameter (a point estimate)

associated with the underlying population. The arithmetic average of a sample is, e.g., an estimator of the distribution

mean.2.3.4

population

totality of potential observations about which inferences are made

2.3.5

population mean

the average of all potential measurements in a given population weighted by their relative frequencies in the

population2.3.6

probability density function

function f (x) is a probability density function for the continuous random variable X if

f (x) W 0 (1)and

fxdx= 1 (2)

NOTE The probability that the random variable X assumes a value between a and b is given by

Pr a< () ()2.3.7

ranking estimator

function that estimates the probability of failure to a particular strength measurement within a ranked sample

2.3.8sample

collection of measurements or observations taken from a specified population

2.3.9

skewness

term relating to the asymmetry of a probability density function

NOTE The distribution of failure strength for advanced ceramics is not symmetric with respect to the maximum value

of the distribution function but has one tail longer than the other.© ISO 2003 — All rights reserved 3

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ISO 20501:2003(E)

2.3.10

statistical bias

inherent to most estimates, this is a type of consistent numerical offset in an estimate relative to the true

underlying valueNOTE The magnitude of the bias error typically decreases as the sample size increases.

2.3.11unbiased estimator

estimator that has been corrected for statistical bias error

2.4 Weibull distributions

2.4.1

Weibull distribution

continuous random variable X has a two-parameter Weibull distribution if the probability density function is

given bymm−1

mx x

fx()=−exp whenx> 0 (4)

ββ β

f (x) = 0 when x u 0 (5)

and the cumulative distribution function is given by

x

Fx=−1exp− whenx>0 (6)

β

F(x) = 0 when x u 0 (7)

where

m is the Weibull modulus (or the shape parameter) (> 0);

β is the Weibull scale parameter (> 0)

NOTE 1 The random variable representing uniaxial tensile strength of an advanced ceramic will assume only positive

values, 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 and similar skewed distributions. If the random variable

representing uniaxial tensile strength of an advanced ceramic is characterized by Equations 4 to 7, then the probability

that this advanced ceramic will fail under an applied uniaxial tensile stress σ is given by the cumulative distribution function

P=−1exp− whenσ>0 (8)θ

P = 0 when σ u 0 (9)

where

P is the probability of failure;

σ is the Weibull characteristic strength.

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ISO 20501:2003(E)

NOTE 2 The Weibull characteristic strength is dependent on the uniaxial test specimen (tensile, flexural, or pressurized

ring) and will change with specimen geometry. In addition, the Weibull characteristic strength has units of stress, and

should be reported using units of MPa or GPa.NOTE 3 An alternative expression for the probability of failure is given by

Pd=−1exp−V whenσ>0 (10)

∫

V

P = 0 when σ u 0 (11)

The integration in the exponential is performed over all tensile regions of the specimen volume if the strength-controlling

flaws are randomly distributed through the volume of the material, or over all tensile regions of the specimen area if flaws

are restricted to the specimen surface. The integration is sometimes carried out over an effective gauge section instead of

over the total volume or area. In Equation 10, σ is the Weibull material scale parameter and can be described as the

Weibull characteristic strength of a specimen with unit volume or area loaded in uniform uniaxial tension. The Weibull

1/m 3/m 3/mmaterial scale parameter has units of stress⋅(volume) , and should be reported using units of MPa⋅m or GPa⋅m if

the strength-controlling flaws are distributed through the volume of the material. If the strength-controlling flaws are

restricted to the surface of the specimens in a sample, then the Weibull material scale parameter should be reported using

2/m 2/munits of MPa⋅m or GPa⋅m . For a given specimen geometry, Equations 8 and 10 can be combined, to yield an

expression relating σ and σ . Further discussion related to this issue can be found in Annex A.

0 θ3 Symbols

A specimen area

b gauge section dimension, base of bend test specimen

d gauge section dimension, depth of bend test specimen

f (x) probability density function

F(x) cumulative distribution function

L likelihood function

L length of the inner load span for a bend test specimen

L length of the outer load span for a bend test specimen

m Weibull modulus

mˆ estimate of the WeibuII modulus

m unbiased estimate of the WeibuII modulus

N number of specimens in a sample

P probability of failure

r number of specimens that failed from the flaw population for which the WeibuII estimators are

being calculatedt intermediate quantity defined by Equation 15, used in calculation of confidence bounds

V specimen volume© ISO 2003 — All rights reserved 5

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ISO 20501:2003(E)

x realization of a random variable X

X random variable

β Weibull scale parameter

σ uniaxial tensile stress

σ estimate of mean strength

σ maximum stress in the j th test specimen at failure

σ Weibull material scale parameter (strength relative to unit size) defined in Equation 10

σˆ � estimate of the WeibuII material scale parameterσ Weibull characteristic strength (associated with a test specimen) defined in Equation 8

σˆ estimate of the Weibull characteristic strength4 Significance and use

4.1 This International Standard enables the experimentalist to estimate Weibull distribution parameters from

failure data. These parameters permit a description of the statistical nature of fracture of fine ceramic

materials for a variety of purposes, particularly as a measure of reliability as it relates to strength data utilized

for mechanical design purposes. The observed strength values are dependent on specimen size and

geometry. Parameter estimates can be computed for a given specimen geometry (,mˆˆσ ), but it is suggested

that the parameter estimates be transformed and reported as material-specific parameters (,mσ ). In addition,

different flaw distributions (e.g., failures due to inclusions or machining damage) may be observed, and each

will have its own strength distribution parameters. The procedure for transforming parameter estimates for

typical specimen geometries and flaw distributions is outlined in Annex A.4.2 This International Standard provides two approaches, Method A and Method B, which are appropriate

for different purposes.Method A provides a simple analysis for circumstances in which the nature of strength-defining flaws is either

known or assumed to be from a single population. Fractography to identify and group test items with given

flaw types is thus not required. This method is suitable for use for simple material screening.

Method B provides an analysis for the general case in which competing flaw populations exist. This method is

appropriate for final component design and analysis. The method requires that fractography be undertaken to

identify the nature of strength-limiting flaws and assign failure data to given flaw population types.

4.3 In method A, a strength data set can be analysed and values of the Weibull modulus and characteristic

strength (,mˆˆσ ) are produced, together with confidence bounds on these parameters. If necessary the

estimate of the mean strength can be computed. Finally, a graphical representation of the failure data along

with a test report can be prepared. It should be noted that the confidence bounds are frequently widely spaced,

which indicates that the results of the analysis should not be used to extrapolate far beyond the existing

bounds of probability of failure.6 © ISO 2003 — All rights reserved

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4.4 In method B, begin by performing a fractographic examination of each failed specimen in order to

characterize fracture origins. Screen the data associated with each flaw distribution for outliers. If all failures

originate from a single flaw distribution compute an unbiased estimate of the Weibull modulus, and compute

confidence bounds for both the estimated Weibull modulus and the estimated Weibull characteristic strength.

If the failures originate from more than one flaw type, separate the data sets associated with each flaw type,

and subject these individually to the censored analysis. Finally, prepare a graphical representation of the

failure data along with a test report. When using the results of the analysis for design purposes it should be

noted that there is an implicit assumption that the flaw populations in the strength test pieces and the

components are of the same types.5 Method A: maximum likelihood parameter estimators for single flaw populations

5.1 General

This International Standard outlines the application of parameter estimation methods based on the maximum

likelihood technique. This technique has certain advantages. The parameter estimates obtained using the

maximum likelihood technique are unique (for a two-parameter Weibull distribution), and as the size of the

sample increases, the estimates statistically approach the true values of the population more efficiently than

other parameter estimation techniques.5.2 Censored data

The application of the techniques presented in this International Standard can be complicated by the presence

of test specimens that fail from extraneous flaws, fractures that originate outside the effective gauge section,

and unidentified fracture origins. If these complications arise, the strength data from these specimens should

generally not be discarded. Strength data from specimens with fracture origins outside the effective gauge

section [3] and from specimens with fractures that originate from extraneous flaws should be censored, and

the maximum likelihood methods presented later in Method B (Clause 6) of this International Standard are

applicable. It is imperative that the number of unidentified fracture origins, and how they were classified, be

stated in the test report. A discussion of the appropriateness of each option can be found in 6.2.2.

5.3 Likelihood functionsThe likelihood function for the two-parameter Weibull distribution of a sample with a single flaw population [4]

is defined by the expression:mmˆˆ−1

N

mˆσσ

L=−exp (12)

σσˆˆ σˆ

θθ θ

i=1

NOTE σ is the maximum stress in the i th test specimen at failure and N is the number of test specimens in the

sample being analysed. The parameter estimates (the Weibull modulus, mˆ, and the characteristic strength, σˆ ) are

ˆ ˆdetermined by taking the partial derivatives of the logarithm of the likelihood function with respect to m and σ and

equating the resulting expressions to zero.The system of equations obtained by differentiating the log likelihood function for a sample with a single flaw

population [5] is given byσσln

() ()

i=1

−−lnσ= 0 (13)

∑ i

Nmˆ

i=1

∑ i

i=1

© ISO 2003 — All rights reserved 7

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ISO 20501:2003(E)

and

ˆ 1

σσˆ = (14)

θ ∑ i

i=1

ˆ ˆ

Equation 13 is solved first for m . Subsequently σ is computed from Equation 14. Obtaining a closed form

solution of Equation 13 for mˆ is not possible. This expression must be solved numerically.

Since the characteristic strength also reflects specimen geometry and stress gradients, this International

Standard suggests reporting the estimated Weibull material scale parameter, σˆ . Expressions that relate σˆ

0 θto the Weibull material scale parameter σ for typical specimen geometries are given in Annex A.

5.4 Bias correction5.4.1 The procedures described herein, to correct for statistical bias errors and to compute confidence

bounds, are appropriate only for data sets where all failures originate from a single population (i.e., an

uncensored sample). Procedures for bias correction and confidence bounds in the presence of multiple active

flaw populations are not currently well developed. The statistical bias associated with the estimator σˆ is

minimal (< 0,3 % for 20 test specimens, as opposed to ≈ 7 % bias for m with the same number of specimens).

Therefore, this International Standard allows the assumption that σˆ is an unbiased estimator of the true

population parameter. The parameter estimate of the Weibull modulus, m, generally exhibits statistical bias.

The amount of statistical bias depends on the number of specimens in the sample. An unbiased estimate of

ˆ ˆm shall be obtained by multiplying m by unbiasing factors [6]. This procedure is discussed in 5.4.2. Statistical

bias associated with the maximum likelihood estimators presented in this International Standard can be

reduced by increasing the sample size.5.4.2 An unbiased estimator produces nearly zero statistical bias between the value of the true parameter

and the point estimate. The amount of deviation can be quantified either as a percent difference or with

unbiasing factors. In keeping with the accepted practice in the open literature, this International Standard

quantifies statistical bias through the use of unbiasing factors, denoted here as UF. Depending on the number

of specimens in a given sample, the point estimate of the Weibull modulus, mˆ, may exhibit significant

statistical bias. An unbiased estimate of the Weibull modulus (denoted as m ) is obtained by multiplying the

biased estimate with an appropriate unbiasing factor. Unbiasing factors for mˆ are listed in Table 1. An

example in Annex B demonstrates the use of Table 1 in correcting a biased estimate of the Weibull modulus.

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ISO 20501:2003(E)

Table 1 — Unbiasing factor for the maximum likelihood estima

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