ASTM E1169-2017 Standard Practice for Conducting Ruggedness Tests.pdf

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1、Designation: E1169 14E1169 17 An American National StandardStandard Practice forConducting Ruggedness Tests1This standard is issued under the fixed designation E1169; the number immediately following the designation indicates the year oforiginal adoption or, in the case of revision, the year of last

2、 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 conducting ruggedness tests. The purpose of a ruggedness test is to identify those factors that stron

3、glyinfluence the measurements provided by a specific test method and to estimate how closely those factors need to be controlled.1.2 This practice restricts itself to designs with two levels per factor. The designs require the simultaneous change of the levelsof all of the factors, thus permitting t

4、he determination of the effects of each of the factors on the measured results.1.3 The system of units for this practice is not specified. Dimensional quantities in the practice are presented only as illustrationsof calculation methods. The examples are not binding on products or test methods treate

5、d.1.4 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibilityof the user of this standard to establish appropriate safety safety, health, and healthenvironmental practices and determine theapplicability of regulatory limitations

6、 prior to use.1.5 This international standard was developed in accordance with internationally recognized principles on standardizationestablished in the Decision on Principles for the Development of International Standards, Guides and Recommendations issuedby the World Trade Organization Technical

7、Barriers to Trade (TBT) Committee.2. Referenced Documents2.1 ASTM Standards:2E456 Terminology Relating to Quality and StatisticsE1325 Terminology Relating to Design of ExperimentsE1488 Guide for Statistical Procedures to Use in Developing and Applying Test MethodsF2082 Test Method for Determination

8、of Transformation Temperature of Nickel-Titanium Shape Memory Alloys by Bend andFree Recovery3. Terminology3.1 DefinitionsThe terminology defined in Terminology E456 applies to this practice unless modified herein.3.1.1 fractional factorial design, na factorial experiment in which only an adequately

9、 chosen fraction of the treatmentsrequired for the complete factorial experiment is selected to be run. E13253.1.2 level (of a factor), na given value, a specification of procedure or a specific setting of a factor. E13253.1.3 Plackett-Burman designs, na set of screening designs using orthogonal arr

10、ays that permit evaluation of the linear effectsof up to n=t1 factors in a study of t treatment combinations. E13253.1.4 ruggedness, ninsensitivity of a test method to departures from specified test or environmental conditions.3.1.4.1 DiscussionAn evaluation of the “ruggedness” of a test method or a

11、n empirical model derived from an experiment is useful in determiningwhether the results or decisions will be relatively invariant over some range of environmental variability under which the testmethod or the model is likely to be applied.1 This practice is under the jurisdiction of ASTM Committee

12、E11 on Quality and Statistics and is the direct responsibility of Subcommittee E11.20 on Test MethodEvaluation and Quality Control.Current edition approved May 1, 2014Oct. 1, 2017. Published May 2014January 2018. Originally approved in 1987. Last previous edition approved in 20132014 asE1169 13a.E11

13、69 14. DOI: 10.1520/E1169-14.10.1520/E1169-17.2 For referencedASTM standards, visit theASTM website, www.astm.org, or contactASTM Customer Service at serviceastm.org. For Annual Book of ASTM Standardsvolume information, refer to the standards Document Summary page on the ASTM website.This document i

14、s not an ASTM standard and is intended only to provide the user of an ASTM standard an indication of what changes have been made to the previous version. Becauseit may not be technically possible to adequately depict all changes accurately, ASTM recommends that users consult prior editions as approp

15、riate. In all cases only the current versionof the standard as published by ASTM is to be considered the official document.Copyright ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. United States13.1.5 ruggedness test, na planned experiment in which environme

16、ntal factors or test conditions are deliberately varied in orderto evaluate the effects of such variation.3.1.5.1 DiscussionSince there usually are many environmental factors that might be considered in a ruggedness test, it is customary to use a“screening” type of experiment design which concentrat

17、es on examining many first order effects and generally assumes that secondorder effects such as interactions and curvature are relatively negligible. Often in evaluating the ruggedness of a test method, ifthere is an indication that the results of a test method are highly dependent on the levels of

18、the environmental factors, there is asufficient indication that certain levels of environmental factors must be included in the specifications for the test method, or eventhat the test method itself will need further revision.3.1.6 screening design, na balanced design, requiring relatively minimal a

19、mount of experimentation, to evaluate the lowerorder effects of a relatively large number of factors in terms of contributions to variability or in terms of estimates of parametersfor a model. E13253.1.7 test result, nthe value of a characteristic obtained by carrying out a specified test method.3.2

20、 Definitions of Terms Specific to This Standard:3.2.1 factor, ntest variable that may affect either the result obtained from the use of a test method or the variability of thatresult.3.2.1.1 DiscussionFor experimental purposes, factors must be temporarily controllable.3.2.2 foldover, ntest runs, add

21、ed to a two-level fractional factorial experiment, generated by duplicating the original designby switching levels of one or more factors in all runs.3.2.2.1 DiscussionThe most useful type of foldover is with signs of all factors switched. The foldover runs are combined with the initial test results

22、.The combination allows main effects to be separated from interactions of other factors that are aliased in the original design.4. Summary of Practice4.1 Conducting a ruggedness test requires making systematic changes in the variables, called factors, and then observing thesubsequent effect of those

23、 changes upon the test result of each run. Factors are features of the test method or of the laboratoryenvironment that are known to vary across laboratories and are subject to control by the test method.4.2 The factors chosen for ruggedness testing are those believed to have the potential to affect

24、 the results. However, since nolimits may be provided in the standard for these factors, ruggedness testing is intended to evaluate this potential.4.3 This practice recommends statistically designed experiments involving two levels of multiple factors. The steps to beconducted include:4.3.1 Identifi

25、cation of relevant factors;4.3.2 Selection of appropriate levels (two for each factor) to be used in experiment runs;4.3.3 Display of treatment combinations in cyclic shifted order (see Annex A1 for templates), which assigns factors and levelsto runs;4.3.4 Execution of runs arranged in a random orde

26、r;4.3.5 Statistical analysis to determine the effect of factors on the test method results; and4.3.6 Possible revision of the test method as needed.5. Significance and Use5.1 Aruggedness test is a special application of a statistically designed experiment. It is generally carried out when it is desi

27、rableto examine a large number of possible factors to determine which of these factors might have the greatest effect on the outcomeof a test method. Statistical design enables more efficient and cost effective determination of the factor effects than would beachieved if separate experiments were ca

28、rried out for each factor. The proposed designs are easy to use in developing theinformation needed for evaluating quantitative test methods.5.2 In ruggedness testing, the two levels for each factor are chosen to use moderate separations between the high and lowsettings. In general, the size of effe

29、cts, and the likelihood of interactions between the factors, will increase with increased separationbetween the high and low settings of the factors.E1169 1725.3 Ruggedness testing is usually done within a single laboratory on uniform material, so the effects of changing only the factorsare measured

30、. The results may then be used to assist in determining the degree of control required of factors described in the testmethod.5.4 Ruggedness testing is part of the validation phase of developing a standard test method as described in Guide E1488. It ispreferred that a ruggedness test precedes an int

31、erlaboratory (round robin) study.6. Ruggedness Test Design6.1 Aseries of fractional factorial designs are recommended for use with ruggedness tests for determining the effects of the testmethod variables (see Annex A1). All designs considered here have just two levels for each factor. They are known

32、 asPlackett-Burman designs (1).36.1.1 Choose the level settings so that the measured effects will be reasonably large relative to measurement error. It is suggestedthat the high and low levels be set at the extreme limits that could be expected to exist between different qualifying laboratories.6.2

33、Table 1 shows the recommended design for up to seven factors, each factor set at two levels. The level setting is indicatedby either (-1) or (1) for low or high levels, respectively. For factors with non-ordered scales (categorical), the designation “low”or “high” is arbitrary.6.3 The design provide

34、s equal numbers of low and high level runs for every factor. In other words, the designs are balanced.Also, for any factor, while it is at its high level, all other factors will be run at equal numbers of high and low levels; similarly,while it is at its low level, all other factors will be run at e

35、qual numbers of high and low levels. In the terminology used bystatisticians, the design is orthogonal.6.4 The difference between the average response of runs at the high level and the average response of runs at the low level ofa factor is the “main effect” of that factor. When the effect of a fact

36、or is the same regardless of levels of other factors, then themain effect is the best estimate of the factors effect.6.5 If the effect of one factor depends on the level of another factor, then these two factors interact.The interaction of two factorscan be thought of as the effect of a third factor

37、 for which the column of signs is obtained by multiplying the columns of signs forthe two initial factors. For example, the eight signs for Column C of Table 1, multiplied by the corresponding eight signs in ColumnD, gives a column of signs for the interaction CD. The complication of the fractional

38、factorial designs presented here is that maineffects are confounded (aliased) with the two-factor interactions. Factors are aliased when their columns of signs are the negativesor positives of each other. For example, the column of signs for the interaction CD is identical to minus the column of sig

39、ns forColumn A.6.6 To separate factor main effects from interactions, the design shall be increased with additional runs.A“foldover,” as shownin Table 2, is recommended to separate the main effects from the aliased interactions. When the runs in Tables 1 and 2 arecombined, all main factors will no l

40、onger be aliased with two-factor interactions.6.7 Sensitivity of the experiment can be increased by the addition of a second block of runs that replicates the first (that is, runswith the same factor settings as the first block). Increasing the size of the experiment improves the precision of factor

41、 effects andfacilitates the evaluation of statistical significance of the effects. However, the preference of this practice is to use a foldover ratherthan a repeat of the original design.3 The boldface numbers in parentheses refer to the list of references at the end of this standard.TABLE 1 Recomm

42、ended Design for Up to Seven FactorsNOTE 1For four factors, use Columns A, B, C, and E; for five factors, use Columns A, B, C, D, and F; for six factors, use Columns A, B, C, D, F,and G.PB Order Run # A B C D E F G Test Result1 1 1 1 -1 1 -1 -12 -1 1 1 1 -1 1 -13 -1 -1 1 1 1 -1 14 1 -1 -1 1 1 1 -15

43、-1 1 -1 -1 1 1 16 1 -1 1 -1 -1 1 17 1 1 -1 1 -1 -1 18 -1 -1 -1 -1 -1 -1 -1Ave +Ave+Ave -Ave-EffectE1169 1736.8 The sequence of runs in Tables 1 and 2 is not intended to be the actual sequence for carrying out the experiments. The orderin which the runs of a ruggedness experiment are carried out shou

44、ld be randomized to reduce the probability of encountering anypotential effects of unknown, time-related factors. Alternatively, optimum run orders to control the number of required factorchanges and the effect of linear time trends have been derived (2). In some cases, it is not possible to change

45、all factors in acompletely random order. It is best if this limitation is understood before the start of the experiment.Astatistician may be contactedfor methods to deal with such situations.7. Ruggedness Test Calculations7.1 Estimate factor effects by calculating the difference between average resp

46、onses at the high and the low levels. When thedesign is folded over, obtain the main effect of a factor by averaging effects from the design and its foldover. Estimate thecorresponding confounded interactions by taking half the difference of the main effects.7.2 A half-normal plot is used to identif

47、y potentially statistically significant effects.7.2.1 Construct a half-normal plot by plotting the absolute values of effects on the X-axis, in order from smallest to largest,against the half-normal plotting values given in Annex A2 on the Y-axis. Effects for all columns in the design, including col

48、umnsnot used to assign levels to any real experiment factor, are plotted. The half-normal plotting values do not depend on data. Theydepend only on the half-normal distribution and the number of effects plotted.7.2.2 A reference line in the half normal plot is provided with slope 1/seffect, if an es

49、timate of precision is available. Potentiallysignificant effects are those that fall farthest to the right of the line.7.3 If an estimate of precision is available or can be derived from the experiment, statistical tests of factor effects can bedetermined using the Students t-test. The t-test statistic for a factor is the effect divided by the standard error seffect, which is thesame for all factors with a balanced and orthogonal design. If the t-value is greater than the t-value corresponding to the 0.05significance level, the factor is statistically signifi