ISO/FDIS 16337

FINAL
INTERNATIONAL ISO/FDIS
DRAFT
STANDARD 16337
ISO/TC 69/SC 8
Application of statistical and related
Secretariat: JISC
methods to new technology and
Voting begins on:
2021­02­03 product development process —
Robust tolerance design (RTD)
Voting terminates on:
2021­03-31
Application des méthodes statistiques et des méthodes liées aux
nouvelles technologies et de développement de produit — Plans
d'expériences robustes
RECIPIENTS OF THIS DRAFT ARE INVITED TO
SUBMIT, WITH THEIR COMMENTS, NOTIFICATION
OF ANY RELEVANT PATENT RIGHTS OF WHICH
THEY ARE AWARE AND TO PROVIDE SUPPOR TING
DOCUMENTATION.
IN ADDITION TO THEIR EVALUATION AS
Reference number
BEING ACCEPTABLE FOR INDUSTRIAL, TECHNO­
ISO/FDIS 16337:2021(E)
LOGICAL, COMMERCIAL AND USER PURPOSES,
DRAFT INTERNATIONAL STANDARDS MAY ON
OCCASION HAVE TO BE CONSIDERED IN THE
LIGHT OF THEIR POTENTIAL TO BECOME STAN­
DARDS TO WHICH REFERENCE MAY BE MADE IN
NATIONAL REGULATIONS. ISO 2021
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ISO/FDIS 16337:2021(E)
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© ISO 2021

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

Introduction ..................................................................................................................................................................................................................................v

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

2 Normative references ...................................................................................................................................................................................... 1

3 Terms and definitions ..................................................................................................................................................................................... 1

4 Robust tolerance design ............................................................................................................................................................................... 2

4.1 General ........................................................................................................................................................................................................... 2

4.2 RTD experimentation ........................................................................................................................................................................ 4

4.2.1 Data generation ...................................................................... ........................................................................................... 4

4.2.2 Experimental design for data collection ...................................................................................................... 4

4.2.3 Analysis of variance ....................................................................................................................................................... 7

4.3 Tolerance determination .............................................................................................................................................................10

4.3.1 Estimating total variance if tolerance is changed .............................................................................10

4.3.2 Deciding tolerance .......................................................................................................................................................11

5 RTD case study (1) — Stabilizing a circuit by using theoretical formula ...............................................12

5.1 Experimentation .................................................................................................................................................................................12

5.1.1 Objective ...................................................................... .........................................................................................................12

5.1.2 Experimental design for data collection and analysis of variance .....................................13

5.2 Tolerance determination .............................................................................................................................................................16

6 RTD case study (2) — Stabilizing the piston by using a simulation experiment ............................18

6.1 Experimentation .................................................................................................................................................................................18

6.1.1 Objective ...................................................................... .........................................................................................................18

6.1.2 Experimental design for data collection and analysis of variance .....................................18

6.2 Tolerance determination .............................................................................................................................................................21

Bibliography .............................................................................................................................................................................................................................26

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This document was prepared by Technical Committee ISO/TC 69, Applications of statistical methods,

Subcommittee SC 8, Application of statistical and related methodology for new technology and product

Any feedback or questions on this document should be directed to the user’s national standards body. A

The designer of a product typically decides the specifications of the product and passes them on to the

manufacturing section for use in manufacturing the product. The specifications include the designed

nominal values and tolerances for the parts and/or elements of the product. The optimum nominal

values of the design parameters are determined by robust parameter design (RPD), and the optimum

RPD, as described in ISO 16336, is applied to the product prior to RTD. In RPD, the major noise factors are

used to evaluate robustness as measured by the signal-to-noise ratio, which represents the variability

of product output. It is a measure for comparing robustness between levels of control factors. RPD

identifies the combination of the values of the design parameters as an optimum RPD condition for

RTD, as described in this document, is a method for selecting the degree of errors of the parts or

elements of the product from the viewpoint of variability under the optimum RPD condition, that is,

the combination of optimum nominal values of the design parameters. If a manufactured product has

errors from the designed nominal values, the product output will deviate from the designed value. The

error in a design parameter should be smaller than the designed error limit to keep the product output

within the designed variability. This is why the design parameters need a tolerance.

The design of a product can be finalized by setting the optimum error limits of the design parameters

by using RTD. The expected variance in output of a product manufactured with errored parts or

elements can be estimated using RTD. After RPD is used to identify a set of optimum values for the

design parameters, RTD is used to check whether the estimated variance is smaller than the target

RPD can be used to set the optimum nominal values of the design parameters without increasing

manufacturing cost while RTD is closely related to the manufacturing cost. Smaller tolerances, meaning

higher­grade parts or elements, result in higher costs, while larger tolerances, meaning lower­grade

parts or elements, result in lower costs. To finalize the product design, the cost of manufacturing the

product is considered. The loss function in the Taguchi methods is used to transform the benefits of an

The cost of the improvement and the benefits of the improvement in quality should be balanced in

deciding the tolerances. RPD and RTD together provide a cost-effective way of optimizing product design.

If RPD cannot achieve the product variability smaller than the target variability, the tolerances of the

design parameters are reduced to improve the variability, but smaller tolerances result in higher costs.

On the other hand, if RPD can achieve the product variability much smaller than the target variability,

the tolerances of the design parameters are increased to reduce manufacturing cost, so larger tolerances

Products manufactured with optimum nominal values and tolerances of design parameters are robust

to noise situations under usage conditions after shipment. Robust products minimize users’ quality

This document specifies guidelines for applying the robust tolerance design (RTD) provided by the

NOTE 1 RTD is applied to the target product to set the optimum tolerances of the design parameters around

the nominal values. RTD identifies the effects of errors in the controllable design parameters on product output

and estimates the total variance of the product output if the tolerances are changed. Hence, RTD achieves the

target variance of the output from the viewpoints of robustness, performance, and cost.

NOTE 2 The tolerance expresses a maximum allowable error in the value of a design parameter in the

manufacturing process. In a perfect world, the parts or elements of every product have the designed nominal

values of the design parameters. However, actual manufacturing does not reproduce the exact designed nominal

values of the design parameters for all products. The actual products have errors in the values of their parts or

The following documents are referred to in the text in such a way that some or all of their content

constitutes requirements of this document. For dated references, only the edition cited applies. For

undated references, the latest edition of the referenced document (including any amendments) applies.

ISO 16336, Applications of statistical and related methods to new technology and product development

For the purposes of this document, the terms and definitions given in ISO 16336 apply.

ISO and IEC maintain terminological databases for use in standardization at the following addresses:

method of setting optimum tolerances from the viewpoints of robustness, performance, and cost

A company’s product design section normally gives the specifications of a product, that is, the nominal

values and tolerances of the design parameters, to the manufacturing section. The manufacturing

section uses the designed specifications in manufacturing the product. When specifications specify the

limits of a design parameter as m±Δ , the parameter value x in the manufacturing process should

where m and Δ denote a nominal value and its permissible difference, respectively. Only the symmetric

(±Δ ) case is discussed in this document. In the symmetric case, the tolerance is 2Δ, and the permissible

If the absolute error of a design parameter is larger than the specified permissible difference Δ, the

variability in the product output cannot meet the designed performance and specifications.

RTD is used by the design section to set the optimum tolerance for each design parameter to achieve

the designed performance, which is evaluated based on the total variance of the product output. The

permissible difference of a design parameter is the maximum allowable error around the nominal value

in the manufacturing process, and it is closely related to the cost of manufacturing.

The optimum nominal values of the design parameters can be identified by robust parameter design

(RPD) through robustness measure, signal­to­noise ratio . The selection of a robust product by setting

the nominal values as the optimum values using RPD prior to RTD is highly recommended. RPD can

optimize the target product by choosing the optimum combination of design parameter nominal values

from the viewpoint of the variability of the product output without increasing the cost .

If RPD cannot achieve a target variability, RTD is used to identify possible tolerances for achieving the

target variability even at a higher cost. Smaller tolerances result in smaller variability, but this requires

upgrading the parts or elements of the product, which leads to higher manufacturing cost. RTD is used

Even if RPD achieves the target variance, RTD is used, in some cases, to identify larger tolerances than

those considered in RPD. Larger tolerances mean larger variability, but if the increased variability

satisfies the target variability, the larger tolerances are applicable as they lead to reduced cost of

The purpose of RTD is to achieve the target variability by setting optimum tolerances from the

viewpoints of robustness, performance, and cost. For this purpose, RTD estimates the total variance of

the output of the designed product if the tolerance of a design parameter is changed. The total variance

Assume that a value x of design parameter F has a linear effect on output y of the product, as shown in

Figure 1 a). If the present permissible difference of x in F is Δ = Δ, the error distribution of F affects

output y with a magnitude of βΔ. If the permissible difference Δ of F is reduced to new permissible

difference Δ = λΔ [λ<1 in Figure 1 a)], the effect of changing Δ in F on the output is reduced to λβΔ,

and the variance in y due to changing Δ in F is reduced from the present variance V to new variance

V = λ V . As a result, the total output variance is reduced from V to V [Figure 1 b)].

If the tolerance of a design parameter is reduced, that is, λ<1, the magnitude of error of the design

parameter becomes smaller, and the total output variance is reduced. A smaller tolerance means that

an upgraded part or element is used, so the cost of producing the new design can be higher than that of

If the tolerance of a design parameter is enlarged, that is, λ>1, the magnitude of error of the design

parameter becomes larger, and the total output variance is enlarged. A larger tolerance means that a

down­graded part or element is be used, so the cost of producing the new design can be smaller than

1) RTD experimentation: Collect data on the designed product, and analyse the data to determine the

2) Tolerance determination: Estimate the total variance if a tolerance is changed, and compare the

effects in quality with the cost of the change to identify the optimum tolerance.

RTD experiments collect the output data of the designed product in which there are errors in the product

design parameters, and estimate the total variance and its dependence on the design parameters. The

experimental design plan is used to collect the data under the combination of design parameter errors.

The ANOVA results show the effects of errors in the design parameters on the product output. The

product output has a target variance from the viewpoints of robustness and performance.

In RTD experiments, the design parameters are taken as noise factors. A noise factor is an experimental

factor which is taken into experiment for the purpose of estimating its variability. The variance in the

In RPD, on the other hand, the design parameters are taken as control factors. A control factor is an

experimental factor which is taken into experiment for the purpose of selecting the optimum level of the

factor. Designers can fix the nominal values of design parameters to the optimum RPD values. However,

in actual manufacturing, the parts or elements of the product invariably have errors, so the designer

cannot specify the error of a design parameter. The designer can set only the permissible difference Δ

Design parameter errors cause variability in product output. If the error of a design parameter has a

linear effect on the product output, the output variance can be changed by resetting the tolerance of the

design parameter. RTD experimentation is used to determine the contributions of the effects of errors

In the tolerance determination step of RTD, the change in the output variance due to resetting a

tolerance is estimated, and the designer selects optimum tolerance for achieving the target output

variance. The optimum tolerance can be determined by balancing the effect in quality due to a tolerance

RTD experimentation is used to determine the design parameters’ linear effects for the designed

product. The relationship between the output by the product and the errors in the design parameters is

When the theoretical relationship between the product output and the design parameters is known,

the output data can be directly calculated for various combination of the design parameter values.

RTD offers multi-factor design as an experimental design for generating the output data in various

combinations of the level of experimental factors, as shown in case study (1) in Clause 5. ANOVA is used

Mathematical analysis can be applied in this case. Mathematical analysis consists of using variance

estimates for a system by, for example, propagating an input variance through the system via Taylor

If an actual product can be constructed, it can be used for experimentation, and the data output can be

collected using the actual experiment. However, in many cases, it is difficult to set the intended levels

of the errors of design parameters in an actual product because the noise levels cannot be controlled

within the error distribution of the design parameters. Simulation experimentation can be used in such

cases. This is why simulation experiments are often used in RTD. A simulation program can provide the

RTD experimentation is used for collecting output data for the designed product under the combinations

of design parameter errors. There are many design parameters, and a multi-factor experimental design

is used to generate various such combinations. The purpose of RTD experimentation is to determine

the main effects of experimental factors. An orthogonal array plan is recommended as a multi-factor

experimental plan for collecting the data as it is an efficient way to collect data for an RTD experiment.

An orthogonal array plan can reduce the number of experimental runs compared with a full-factorial

plan for the same number of factors and can assign the maximum number of factors in a plan for the

same number of experimental runs. The main effects of factors can be estimated under the condition of

a balanced combination of the other factors’ levels. The choice of the orthogonal array depends on the

An example orthogonal array (L ) is shown in Table 1. Seven experimental factors with three levels

(B-H) and one factor with two levels (A) can be assigned to the columns in the array. Rows represent

the experimental run. The number in each cell represents the level of the factor assigned to the column.

The experimental run of low No. 1 should be performed under the combination of factor’s levels

For RTD, the design parameters are assigned to the columns as noise factors. For the purpose of

estimating the linear and non­linear effects of a factor, each factor has at least three levels. However,

if the proportional property is obvious for a factor, a two-level setting is sufficient. Two-level factor

is assigned to the first column. The last column in Table 1 shows the output data y calculated for the

Table 2 shows an example of level setting of factors for RTD. The upper and lower permissible differences

are assumed to be the same for simplicity. The levels of the factors are set around nominal value m with

level width d. Nominal value m is set to an optimum value by RPD from the viewpoint of robustness.

Level width d is set from the actual standard deviation of the design parameter if it is known.

When the actual standard deviation σ of the error in the design parameter is not exactly known, the

When the actual standard deviation σ of the error in the design parameter is known, the level width d

Setting the level of the factors in this way makes the estimated variance σ of output y caused by the

linear effect of the error in the factor βσ , where β represents the linear coefficient of the relationship

If yi==11,,nj,, ,r represents the output from j­th run in r repeated runs on i­th level x in n

level factor, the linear coefficient β and the sum of squares of linear effect S are calculated as

For a two­level factor A with levels xx=−d and xx=+d , the sum of squares of linear effect S is

calculated as Sr=⋅2d β . If the linear effect of factor A is significant, S approximately represents

2rσ , where 2r denotes the number of data items and σ denotes the variance of each. If level width

d is set to σ , Sr==22drβσ βσ≅2r . Then variance σ in output y caused by the linear effect

For a three­level factor B with levels xx=−dx, =x , and xx=+d , the sum of squares of linear effect

S is calculated as Sr=⋅2d β . If the linear effect of the factor is significant, S approximately

represents 3rσ , where 3r denotes the number of data items. If the level width d is set to σ ,

Sr==22drβσ⋅⋅βσ=≅33rrβσ . Then variance σ in output y caused by the linear effect of

ANOVA is used to identify the linear effects of the factors and the ratios of their contributions to the

The total sum of squares is decomposed into sum of squares S of the linear effect of each factor and