NAT'L INST OF STAND a rcr-u linn », A11107 PUBLICATIONS NIST Special Publication 260-171

Charpy Machine Verification:

Limits and Uncertainty

J. D. Splett C. N. McCowan

C.-M. Wang

Nisr National Institute of Standards and Technology

U.S. Department <y Commerce
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NIST Special Publication 260-171

Charpy Machine Verification:

Limits and Uncertainty

J. D. Splett C.-M. Wang

Statistical Engineering Division Information Technology! Laboratory

C. N. McCowan

Materials Reliability Division Materials Science and Engineering Laboratory National Institute ofStandards and Technology

Boulder, Colorado 80305

September 2008

U.S. Department of Commerce

Carlos M. Gutierrez, Secretary

National Institute of Standards and Technology

Patrick D. Gallagher, Deputy Director
Certain commercial entities, equipment, or materials may be identified in this document in order to describe an experimental procedure or concept adequately. Such

identification is not intended to imply recommendation or endorsement by the National Institute of Standards and Technology, nor is it intended to imply that the

entities, materials, or equipment are necessarily the best available for the purpose.

National Institute of Standards and Technology Special Publication 960-171

Natl. Inst. Stand. Technol. Spec. Publ. 960-171, 10 pages (September 2008) CODEN: NSPUE2

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Contents

Page

1. Introduction 1

2. Background: Distributions and Uncertainty 2

3. Discussion 4

3.1 Explanation and Use of Uncertainty 4

3.2 ASTM Verification Limits 5

4. Summary 7

5. References 7

iii

Charpy Machine Verification: Limits and Uncertainty

J. D. Splett,

f C. N. McCowan,* and C.-M. Wangf

National Institute of Standards and Technology

325 Broadway

Boulder, CO 80305

We clarify some issues pertaining to uncertainty statements and the ASTM E 23 limits used in the Charpy machine verification program. We explain some of the distributional subtleties associated with uncertainty and ultimately relate these to the ASTM limits. Key words: absorbed energy; Charpy V-notch; impact test; pendulum impact test; uncertainty;

verification testing

1. Introduction

To indirectly verify a Charpy machine, a set of five Charpy verification specimens are tested on

the machine of interest and the broken specimens are returned to NIST along with the test results. Next, the customer receives a verification letter from NIST indicating whether or not the machine passed the indirect verification test. In addition, the verification letter reports the average absorbed energy for the verification specimens (the certified reference value), and the uncertainty of the certified reference value. In this document, we clarify some issues pertaining

to the uncertainty reported in the verification letter and the ASTM verification limits for E 23

[1]. Specifically, we address the following questions from a customer's perspective:

1. What is the meaning of the uncertainty provided with Charpy indirect verification specimens (standard reference materials (SRMs) 2092, 2096, and 2098)?

2. How should the uncertainty be used by a customer?

3. How is the stated uncertainty related to the outcome of a verification test?

' Statistical Engineering Division, Information Technology Laboratory

; Materials Reliability Division, Materials Science and Engineering Laboratory

1
2. Background: Distributions and Uncertainty

There are various distributions used in the indirect verification of Charpy impact test machines,

including the distributions associated with the machines used to certify the SRMs and the

distributions associated with the customer's verification test. It is important to understand the

role played by each of these distributions in the development of statistical uncertainty statements and verification limits in the Charpy machine verification program.

All individual specimens from a batch of SRMs are assumed to follow the same distribution of absorbed energies. Thus, data from verification specimens tested on machines being verified by

customers should follow the same basic distribution as data from the same batch of verification specimens tested on the three reference machines at NIST. This is a fundamental assumption that

is supported by the data shown in Figure 1. Here, the distribution of individual data points from

three NIST reference machines tested for the certification of a batch of SRMs is compared to the

distribution of individual data points from customer machines testing the SRMs in verification

tests. The distributions in Figure 1 demonstrate that the customer and NIST distributions are quite similar; although the customer distribution has slightly larger spread than the NIST

distribution. The larger spread in the customer data is probably due to the fact that there are more

machines included in the histogram of customer data. However, the comparison indicates that specimens sent to customers for verification tests are representative of the batch, and that customer measurements are similar to NIST measurements. We also need to consider the differences between the distributions of individual values and the

distributions of average values. This detail is often overlooked and can cause confusion, resulting

in a comparison between "apples and oranges." To simplify the discussion, the histogram in Figure 2(a) displays the distribution for 1000 simulated normal data points that have the same mean {ju = 93.76 J ) and standard deviation (cr = 2.33 J ) as the NIST data in Figure 1. For

further simplification, suppose that all the tests were all done on the same machine, and the

quantities ju and a represent "true" values rather than sample values.

With similar assumptions we can simulate several relevant distributions of averages, as shown in Figure 2(b) and 2(c), for comparison with Figure 2(a). For example, a laboratory typically tests five verification specimens for a verification test. In our simulated normal example, the standard

deviation of a distribution based on the average of five specimens would be = = 1-04 J, and the distribution of 1000 averages based on five observations would be

similar to the distribution shown in Figure 2(b). As one would expect, the distribution of

averages has a smaller range of values than the individual test data. In Figure 2(c), the effect of

increasing the number of samples used for the average is shown. For this example, we generated 1000 averages of samples of size of 75 based on the normal distribution of Figure 2(a).

2
NIST: Mean = 93.76 J. Std Dev = 2.33 J

CUSTOMER: Mean = 94.22 J. Std Dev = 2.88 J

Energy, J Figure 1. The upper distribution is based on 97 individual measurements from three NIST

machines used to certify a high energy value for an SRM. The lower distribution represents customer data (135 individual measurements) for the same SRM batch. Only results for customer machines that pass the ASTM indirect verification requirements are included in the histogram. The number of customer machines that tested verification specimens from this batch

is 27.

Notice that the range of values for the distribution of averages of 75 specimens is much smaller than the range of the original normal distribution of single observations, and it is significantly smaller than the range of the distribution based on averages of five specimens. In general, the standard deviation of the distribution of averages is -y=, where a denotes the actual standard

deviation of single observations and n is the number of observations in each average. In our example, the standard deviation of the distribution of the average of 75 specimens is

V75 V75

3
2520

15 -| 10

5

0

(a) Mean = 93.90 J. StdDev = 2.27J

(b) Mean = 93.74 J. StdDev=1.01J

(c) Mean = 93.75 J. Std Dev = 0.27 J

25 h 20

15

10

50

Energy, J Figure 2. (a) Distribution of 1 000 simulated normal observations, (b) Distribution of 1 000

averages from samples of size 5. (c) Distribution of 1000 averages from samples of size 75.

The point is that the distributions are not the same, and since uncertainty is directly related to the standard deviation associated with these distributions, it follows that the uncertainties would also

differ for these cases.

3. Discussion

3.1 Explanation and Use of Uncertainty

The verification letter associated with five test results for verification specimens includes the NIST certified reference value ( R ) and its associated uncertainty (u(R)). The value of u(R)

quantifies the uncertainty we have about the closeness ofthe reference value to the true average

4
absorbed energy ofthe batch. It includes many sources of variation, such as between-machine

variation, within-machine variation, material homogeneity, and operator error, to name a few. (To complicate matters, we cannot even estimate each individual component of uncertainty

separately because Charpy tests are destructive.) Thus, the value of u(R) not only represents the

variation in a batch of specimens, it contains many more sources of error as well. The verification set mean ( V ) and its associated standard deviation (Sv /45 , where Sv

is the standard deviation of the five measurements) are calculated once verification testing is complete. The values of Sv /y/~5 and u(R) are different because the distributions of V and R are different, as demonstrated in Section 2. In addition to the differences between the number of samples

associated with the respective averages, there are differences between machines and operators, and the standard deviation of the verification set mean does not include between-machine

variation (like the NIST certified reference value) because all samples were broken on the same

machine. We do not define the uncertainty of the verification set mean as Sv /4s because, according to the "Guide to the Expression of Uncertainty in Measurement" [2]. the word "uncertainty"

implies that all sources of error have been quantified and accounted for in the uncertainty

estimate. Only the customer can conduct a thorough uncertainty analysis and determine the appropriate uncertainty for their particular machine. Once a customer's machine has been verified, the indirect verification test results, along with the reference value and its uncertainty, can be used by the customer in the estimation of the uncertainty of the mean for a new material that the customer wants to evaluate [3]. Basically, the

verification test results define the bias (and its uncertainty) between the customer's Charpy

machine and the NIST reference value. The uncertainty of the bias is then combined with other

uncertainties to obtain the final combined standard uncertainty of a test result for a new material.

3.2 ASTM Verification Limits

Customers testing NIST SRMs to meet the verification limits set in ASTM E23 want to know

whether the uncertainty associated with the reference value influences their chance of passing the

verification test. It turns out that this is a difficult question to answer directly, because the ASTM limits were not established based on modern uncertainty analyses. The ASTM limits were originally developed in the 1950's using the distribution of the average of five measurements for "good" machines, and were designed to include a large proportion of the good

machines tested [4].

Intuitively we understand that as the uncertainty associated with the certified absorbed energy for a particular SRM increases, the mean of the customer's verification set also has increased

uncertainty, but exactly how this relates to the ASTM verification limits is not clear. However, we know from experience that the ASTM limits are reasonable for current variation levels associated with the verification specimens, and the verification results shown in Figure 3 support

this conclusion.

5
Figure 3 displays distributions of customer averages (box-and-whisker plots) along with the

pilot-lot averages for the three NIST reference machines (X's) and the calculated ASTM limits (solid dots) for various batches of material. The vertical lines extending from the top and bottom

of each box terminate at the distribution's most extreme points within 1 .5 times the interquartile range. (The interquartile range is the difference between the 75

th and 25th percentiles of the sample.) Values larger than 1 .5 times the interquartile range are displayed as open circles and are sometimes considered extreme values. The box itself contains the middle 50 % of the data and

the horizontal line in the middle of the box represents the median. Both passing and failing machines were included in the calculation of percentiles shown in the figure. Machines falling outside the solid dots would not pass the verification test. To more directly understand the relationship between the uncertainties associated with

verification specimen mean and limits that might reasonably bracket a "good" machine, it is easiest to derive a new form of limits that considers uncertainty contributions directly. One such approach is currently being developed at NIST. In short, new limits are being developed for both

the mean and standard deviation of the verification test result based on actual data for the

particular SRM tested.

Joules

120

110

100

90

80

0

Jk

is i M U 1

Batch Figure 3. Distribution of all available customer means (box-and-whisker plot), reference machine averages (X's), and ASTM limits (solid dots) for various high-energy lots. Each box

contains the middle 50 % of the data. The lower and upper horizontal lines represent the minimum and maximum values within 1.5 times the interquartile range. Values beyond 1.5 times the interquartile range are represented by open circles.

6
Initial work has shown that limits based on uncertainty are similar to ASTM E23 limits, indicating that the ASTM limits are reasonable for the level of variability currently associated with impact verification specimens. The "uncertainty" limits will not be proposed as replacements to the existing "fixed" ASTM limits, but they can serve as a useful tool for predicting the influence of variability in verification specimens on test results. This approach will also be used to predict how proposed changes in verification limits (ASTM and ISO) might

affect the users of NIST verification specimens, and be used as the basis of arguments supporting

"good" rules for international impact standards.

4. Summary

We have discussed issues pertaining to uncertainty and verification limits for the Charpy

machine verification program from a customer's perspective. We have shown how distributions

are related to uncertainty and have explained how to interpret the various uncertainties in the

verification program.

The authors thank Dr. Dominic Vecchia and Dr. Hari Iyer for their very helpful comments and

suggestions during the preparation of this document.

5. References

[1] ASTM E 23-02a, "Standard Test Methods for Notched Bar Impact Testing of Metallic

Materials," ASTM International, West Conshohocken, PA (2002).

[2] International Organization for Standardization, Guide to the Expression of Uncertainty in Measurement, International Organization for Standardization, Geneva Switzerland; 1993

(corrected and reprinted 1995).

[3] J. D. Splett, C. N. McCowan, H. K. Iyer, and C.-M. Wang, "NIST Recommended Practice Guide: Computing Uncertainty for Charpy Impact Machine Test Results," Nat. Inst. Stand. Technol. Spec. Pub. 1067(2007).

[4] D. E. Driscoll, "Reproducibility of Charpy Impact Test," Impact Testing, ASTM STP 176

(1955).

7

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