Issue 
Int. J. Metrol. Qual. Eng.
Volume 9, 2018



Article Number  1  
Number of page(s)  8  
DOI  https://doi.org/10.1051/ijmqe/2017023  
Published online  05 February 2018 
Research Article
Conformity and statistical tolerancing
^{1}
Groupe PSA, Centre Technique de Vélizy A,
78943
VélizyVillacoublay, France
^{2}
Université Savoie Mont Blanc, Laboratoire SYMME & IUT Annecy, Département QLIO,
Chambéry, France
^{*} Corresponding author: maurice.pillet@univsmb.fr
Received:
3
July
2017
Received in final form:
23
October
2017
Accepted:
24
October
2017
Statistical tolerancing was first proposed by Shewhart (Economic Control of Quality of Manufactured Product, (1931) reprinted 1980 by ASQC), in spite of this long history, its use remains moderate. One of the probable reasons for this low utilization is undoubtedly the difficulty for designers to anticipate the risks of this approach. The arithmetic tolerance (worst case) allows a simple interpretation: conformity is defined by the presence of the characteristic in an interval. Statistical tolerancing is more complex in its definition. An interval is not sufficient to define the conformance. To justify the statistical tolerancing formula used by designers, a tolerance interval should be interpreted as the interval where most of the parts produced should probably be located. This tolerance is justified by considering a conformity criterion of the parts guaranteeing low offsets on the latter characteristics. Unlike traditional arithmetic tolerancing, statistical tolerancing requires a sustained exchange of information between design and manufacture to be used safely. This paper proposes a formal definition of the conformity, which we apply successively to the quadratic and arithmetic tolerancing. We introduce a concept of concavity, which helps us to demonstrate the link between tolerancing approach and conformity. We use this concept to demonstrate the various acceptable propositions of statistical tolerancing (in the space decentring, dispersion).
Key words: conformity principle / statistical tolerancing / robust engineering
© L. Leblond and M. Pillet, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
1 Introduction
Designing and manufacturing an industrial product are the result of an approach which has the following objective: ensure the functional product requirements for customer satisfaction at the lowest possible cost, as soon as possible and at the expected quality levels. Nevertheless, the parts manufacturing constituting a product can never be produced to target, irrespective of the machine precision used.
Tolerancing limits this variability. It is carried out based on construction hypotheses, which will have to be verified by the production. It represents the verification of the conformity. This conformity is often interpreted as individual tolerancing conformity for each characteristic.
In this paper, we propose a formal definition of statistical conformity that does not apply individually to a part but to a part population. We apply this definition to different acceptance definition: triangular, rectangular and inertial. Each of these definitions satisfies a concavity criterion that we define. We show in appendix the interest of this particular criterion in the case of a statistical tolerancing.
2 Issues, ratings and assumptions
The variability of parts is taken into account by specifying the limits of the specifications of each elementary characteristic for quantitative and measurable quantitative quantities [1]. The tolerance interval (TI) is the difference between the maximum and minimum permissible values, and therefore shows the permissible variation of a quantity.
The TIs specification for interrelated characteristics is established according to two possibly complementary strategies: the analysis and the synthesis of tolerances [2]. The analysis consists in verifying that the TIs, defined by the functional characteristics required on the product, are respected by the tolerances on the elementary characteristics. The synthesis of the tolerances consists in allocating the tolerances to the basic characteristics from those on the functional requirements.
Formally, a characteristic is a quadruplet (Y, v, c, R) where Y, which denotes a quantity, is a real random variable with a probability density,^{1} v ∈ ℝ is called nominal, c ∈ ℝ is center and is the radius of a socalled TI. If the context permits, the quantity Y and the characteristic (Y, v, c, R) will be associated and described using the following quantities (see Fig. 1):

A TI [L, U] and its length TI:
U = c + R, L = c − R and TI = U − L = 2R.

An upper tolerance (respectively lower): T_{U} = U − v (respectively T_{L} = L − v,).
A characteristic is coherent if v ∈ [L, U] and in that case T_{U} > 0 and T_{L} < 0. It is called the nominal centered if v = c. In these conditions, T_{U} = − T_{L} and is at the center of [L, U].^{2}
For a characteristic (Y, v, c, R), is the expectation of Y and its standard deviation. The decentering of this characteristic is δ_{Y} = μ_{Y} − c. It is centered if μ_{Y} = c (i.e.δ_{Y} = 0). When the characteristic is indexed and does not cause confusion, only the index will appear in the denomination of expectation, standard deviation or decentering.
In general, a functional requirement is a function of elementary characteristics. The tolerancing consists in modelling the behaviour in the target vicinity of each of the elementary characteristics. Consequently, the Taylor expansion near the target makes it possible to obtain a linear relationship that relates the functional requirement to the elementary characteristics.
In these conditions, a functional requirement, denoted Y_{Clearance}, is expressed as a linear function of n independent characteristics. There are then reals ^{3} such as: (1) (2)
We will also make the following assumptions^{4}: (3)
The tolerancing question is asked like this: Which relationship between R_{Clearance} and the nuplet makes it possible to verify the following principle of conformity: “If all the characteristics are conformed, then Y_{Clearance} is conformed, too”?
The most natural notion of conformity in the worst case is expressed as follows: if for all i = 1 … n, and all the Y_{i} characteristics are within the TI [L_{i}, U_{i}] then the Y_{Clearance} fulfilments have to be within the TI [L_{Clearance}, U_{Clearance}]. This formulation of conformity justifies the following formulae:
• Arithmetic tolerance: (4) In other words, the designer who uses this tolerance to verify that R_{Clearance} guarantees the assembly functionality from knowledge of the R_{i} does not wish to take any risk: if the production is conform to the tolerances of the basic characteristics, it is certain that the functional requirement will be within the tolerance.
This tolerancing is often too restrictive because it is based on the condition that all the elementary characteristics are at the limits of specification. In order to be more flexible, a second formulae is available to the designer:
• Statistical tolerance (or augmented quadratic)^{5}: (5) For the tolerance radii R_{i} of the given characteristic, the tolerance radius R_{Clearance} obtained by this formula is smaller than that obtained by using the arithmetic tolerancing formula, which can greatly facilitate the life of the designer who has a functional constraint on R_{Clearance}.
This formula is traditionally justified in the following way: if all the characteristics Y_{i} follow a normal average law μ_{i} = c_{i} and standard deviationσ_{i}, then Y_{Clearance} follows a normal mean law μ_{Clearance} = c_{Clearance} and standard deviation σ_{Clearance} according to (2). The statistical tolerancing formulae are deduced by assuming that tolerance rays are proportional to the standard deviations: and R_{Clearance} = 3σ_{Clearance}. In these conditions, ℙ(Y_{Clearance} ∉ [L_{Clearance}, U_{Clearance}]) ≤ 0, 135% and the risk of having a noncompliant clearance seems to be under control.
However, this justification is not satisfactory since it uses manufacturing parameters which are unknown to the designer under the restrictive and unrealistic nullity hypothesis of the decentring of the characteristics.
The aim of this article is to show that a correct justification of the statistical tolerancing formulae requires defining specific conformity criteria. In order to verify the conformity, the manufacturer must take the tolerancing method of the characteristics produced into account. Sometimes “to be within tolerances” is not sufficient. Designers should not ignore this fact when they use statistical tolerancing. Otherwise they will justify a design with high risks. The management of information between design and manufacturing is thus affected.
Fig. 1 Illustration of quantities associated with a characteristic. 
3 Proposition of a definition of conformity for statistical tolerancing
The acceptance of the conformity by the sole respect of the TI is too restrictive. Indeed, it is not the measurements of characteristics carried out on a limited set of production that are to be considered, but rather the conformity of what the process can potentially produce, namely, the population of parts. In other words, it is not so much an effective part that one has to judge^{6} but the capacity to produce parts in a sufficiently homogeneous way to guarantee their assembly: one must therefore judge the conformity of the process that produces them.
Definition 1–Conformity.
A conformity is the data of a context and a couple called conformity objective where r : [0, 1] → [0, 1], is decreasing and strictly positive on [0,1[, r(1) = 0, and
The application r is a type of objective and a goal is maximal if the preceding inequality is equality.
A characteristic at the centered nominal (Y, ν, R) is called Cconformed^{7} if: (6)
Affirming that a characteristic conformed according to this definition, is to say that, up to a certain limit, the more decentering, the more the variability must be small in proportion to the tolerance radius. The way in which the reduction of this variability is managed through the type of objective (r application) qualifies the nature of the conformity considered. We shall see that the choice of a concave as a type of objective^{8} conditions the expected properties of a quadratic tolerancing.
We considered the following three types of objectives with (c_{P}, c_{I}) ∈] 0, 1 [^{2} and :
Objective of the tolerance or triangular:
Performance objective or rectangular:
Extended or elliptical inertial objective:
For a conformity , these denominations come from the surface form represented in the plane %decentering × %standard deviation (Fig. 2) where the percentage is relative to a radius of tolerance [4]:
A characteristic Y is conformed according to a conformity defined by a particular surface if the couple is a point inside this surface.
We demonstrate in the appendix that the surface defining compliance must:

on the one hand is included in the triangle so the base is equal to the arithmetic tolerancing and the height is equal to the dispersion of the quadratic tolerancing;

on the other hand be a concave surface. Rectangle, circle and ellipse are concave surface.
In the example (Fig. 2) below, the characteristic is extended inertial and tolerancecompliant but is not performancecompliant. Rectangle and Ellipse are inside the triangle but does not cover the available area.
This definition of conformity covers the most frequent definitions, in particular those using capability indicators [5]. Indeed, each of the preceding conformity criteria can be expressed using the traditional capability indicators [6,7]^{9} associated with a characteristic (Y, ν, R): (7) For some real objectives Pp^{obj} ≥ Ppk^{obj} > 0 and ,^{10} the writing of the characteristic conformity (Y, ν, R), for each of the preceding conformations with maximum objectives, takes the following form (Fig. 3). (8)
Performanceconformed at (Pp^{obj}, Ppk^{obj}) if (9) (10)
The surface associated with an inertial objective is a semicircle [8–10].
For usual objectives: .
The usual conformity defined by inequalities Pp_{Y} ≥ Pp^{obj} and Ppk_{Y} ≥ Ppk^{obj} and corresponding to the trapezoidal surface delimited by the triangle and the top of the rectangle at Figure 3 suffers from the same defect as the toleranceconformity, by leaving too much latitude to the allowed decentring [11].
In the example (Fig. 3), the characteristic is extended inertial, tolerancecompliant and performancecompliant. Rectangle and Ellipse are inside the triangle and cover the available area.
The study of the advantages and disadvantages of conformities in terms of characteristic acceptability is not the subject of this paper [12]. However, by way of example, In the annexes (property 3), we describe a specific property of the conformity in inertial tolerancing (Inertialconformed) that performanceconformed does not verify.
Fig. 2 Illustration of surfaces defined by conformities for k = 3. 
Fig. 3 Illustration of surfaces of maximum conformity. 
4 Statistical tolerancing and conformity principles
The overall problem is defined like this: for any nuplets of charactertics with the centered nominals independently, Y_{Jeu} to the centered nominal verifying (1) and ν_{Clearance} verifying (3), what are the optimal R_{Jeu} which ensure the principle of conformity?
When the same type of objective is used for Y_{Clearance} and Y_{i}, the principle of conformity is said to be “under the assumption of judgment identity”, which we will assume later.^{11}
The conformity principle in the worst case results in: As , the preceding condition is verified when the arithmetic tolerancing formula is used.
On the other hand, there is no smaller value for R_{Clearance} enabling this condition to be satisfied.^{12} Therefore, without taking any risks, if an assembly of worstcase parts conformed to the worst case, only the arithmetic tolerance is optimal. Another advantage of arithmetic tolerancing is that it is adapted to a set of conformities including those presented above, without necessarily being optimal.
Property 1–Conformity principle and arithmetic tolerancing.
For C a conformity with concave objective type, if the characteristics are independent and Cconformed, then Y_{Clearance} is Cconformed for arithmetic tolerancing .^{13}
This result shows that the arithmetic tolerancing makes a separation between the design specification and the verification of these manufacturing specifications possible. In practice, the considered conformities always have concave types of objective. This concavity aims to guarantee the second inequality of (6) on the functional requirement from this same inequality on each of the elementary characteristics. The demonstrations of this property are given in annexe 3 for better readability.
Arithmetic tolerancing suffers, however, from a functional incoherence that reflects its severity [13]: centered and fair characteristics that satisfy the conformity, or not, can induce conformal clearances for objectives well above what is required.
Is there a less stringent criterion of conformity than the worst case for statistical tolerance? The positive answer to this question, and which is subject to the following property (shown in the appendix), is a remarkable result. However, its application is difficult because the choice of a tolerancing formula and the choice of the criterion and the objectives of conformity to be used can no longer be decoupled.
Property 2–Conformity principle and statistical tolerance.
Are n ∈ ℕ^{*}, and, for any i = 1 … n and conformities such as concave.
Is checking the following constraints: If the characteristics are independent and C_{i}conformed, then Y_{Clear.} is C_{Clear.}conformed for: (11)This property means that, under a strong constraint on objectives, for any linear combination of n independent characteristics, statistical tolerancing (5)^{14} makes it possible to verify the principle of conformity for selected conformities. The assumption of concavity of is verified for rectangular or elliptic but not triangular conformity. It is because the rectangular or elliptical conformities impose a control of the decentring that the situation is very different from that in which the decentring are only constrained by the limits of the TIs, which is the case for the toleranceconformed, for example.
Let us illustrate this in the usual case where Pp^{obj} ≥ Ppk^{obj} > 0 are the conformity objectives for the Y_{i} et those for Y_{Clear.}. If Y_{Clear.} verifies (1) and ν_{Clear.} verifies (3), is the minimum of ensuring the following equivalence ^{15}:
PerformanceConformity principle (under the assumption of judgment identity): (12)
Constraint of realization: (13) By allowing intelligently controlled risks on all Y_{i}, statistical tolerancing can be used without impairing the quality of the functional requirement. However, the difficulty of exploiting this result is related to the dependence of the constraint of production on the number of characteristics n. It therefore requires setting objectives according to the least favourable situation for characteristics belonging to several linear relationships.
5 Conclusions
The problem in using statistical tolerancing lies in the control of the decentring of the characteristics at the time of their production. This is where using a statistical tolerance reaches its limits and is the only reason that obliges the designer to always preferentially examine the acceptability of a clearance with arithmetic tolerancing. For the latter, there is no taking of risk because if each characteristic is tolerantcompliant (lowest condition) then the clearance will necessarily be tolerantcompliant. This is no longer assured as soon as the tolerance of the functional requirement is calculated by a quadratic method.
However, statistical tolerancing could reduce the functional inconsistency of arithmetic tolerancing if, for example, performancecompliance was rigorously used with targets that were severe enough to maintain low decentring. This does not, however, prevent the use of a statistical tolerance without a mindset: it allows the designer to widen the tolerances of the links for an acceptable clearance, which satisfies the manufacturer in terms of feasibility. Everyone seems satisfied, except that the question should be asked: who pays for the uncontrolled risks? Firstly, the company that manages the crises does so at great expense, but also the customer through chronic dissatisfaction that costs them very dearly. In other words, everyone! Suffice to say that the prospects offered by the regular reflections on industrial efficiency open the doors to an information management that remains to be agile.
We have shown in this paper that statistical tolerancing must be used in conjunction with a specific definition of conformity that we have defined. In addition, we have shown the importance of the concave objective types to satisfy the principle of conformity using statistical tolerancing.
Appendices
Mathematical development
Property 2 – Conformity principle and statistical tolerancing
Are n ∈ ℕ^{*}, and, for any i = 1 … n and conformities such as concave.
Is checking the following constraints: If the characteristics are independent and C_{i}conformed, then Y_{Clear.} is C_{Clear.}conformed for:
A − If for any i = 1 … n, then . This is a consequence of the first constraint and the inequality. Indeed:
B − If for any i = 1 … n, then .
For , we have and .
Using the g definition, the second constraint and the inequality onR_{Jeu}:
By concavity of g then using its decreasing and the inequality between arithmetic mean and quadratic :
Finally, by definition of g and decay of:
Below, we present the coherence property of the inertialconformity.
Definition 2 – Characteristic conditionally conformed.
For (Y, ν, R) a characteristic, a Y's process (Y, M) admits a joint law, and is independent of M. The notation Y/M will mean Y conditionally to Mprocess of Y.
Is a conformity. Y/M is Cconformed if: (15) with, for all real x: and .
A conformity C is said to be coherent if for any characteristic(Y, ν, R) and M a Y's process, if Y/M is C conformed then Y is C conformed. It reflects the facts that the mixing of conforming production batches remains conformed.
The consistency of a conformity is not verified in general (if Y/M is Cconformed, (since ) but does not generally impose .
The following lemma is used for the demonstration of the coherence property below.
Lemma – The types of concave lenses.
Is r : [0, 1] → ℝ a continuous application. The following assertions are equivalent:
1.r is decreasing concave.
2. For any v.a.r. U ∈ [− 1, 1] p . s. E(r(U)) ≤ r(E(U)).
1 ⇒ 2: By inequality of Jensen,^{16} as r is concave, E (r (U)) ≤ r (E (U)). Furthermore r is decreasing and E (U)  ≤ E (U).
2 ⇒ 1: We pose g = r ∘  : [− 1, 1] → ℝ. For any continuous f : [0, 1] → [− 1, 1] and uniform U on [0, 1], f(U) ∈ [ − 1, 1]p . s. and by assumption: (19)
Are defined by:
f_{ε} is continuous and . Furthermore:
But g is continuous on the compact [a, b] because r is continuous, it thus reaches a minimum m ∈ ℝ and . Consequently, according to (19):
This inequality being true for any , we deduce, by definition, that g = r ∘  is concave on [ − 1, 1]. r is therefore concave. Moreover it is decreasing on [0, 1] since for any (λ, ν) ∈ [0, 1]^{2}:
^{.}
Note that if r(0) > 0 and r(1) ≥ 0, the concavity of r ensures that it is strictly positive on [0,1[.
Property 3 – Characterization of a conformity coherency.
Are a continuous application such as .
The following assertions are equivalent:
1. is a coherent conformity.
2. Is exists continuous concave decreasing f : [0, 1] → ]0, 1] as f(0) = 1, and for any u ∈ [0, 1] .
1 ⇒ 2: Are U ∈ [−1, 1] p . s., ν ∈ ℝ, . We pose:
There is then a characteristic (Y, ν, R) of M process Mas: . By definition, Y/M is Cconformed and then, by coherence of C, Y is C conformed, that is, noting that :
Then, and the lemma ensures that the application is concave decreasing.
Especially: because r (1) =0.
Also, there are λ ∈ [0, 1] as and checks 2.
2 ⇒ 1: As for any u ∈ [0, 1] _{f(1)u2 ≤ f(1) ≤ f(u) ≤ f (0)} the application r verifies: .
is a conformity. Indeed, r : [0, 1] → [0, 1] is strictly positive on [0,1[, r(1) = 0, , ris decreasing because f is decreasing and for any u ∈ [0, 1] because .
Finally, for a characteristic (Y, ν, R)M process as Y/M is Cconformed, and:
(The first inequality is a consequence of Y/M Cconformed and the second is an application of the inequality of Jensen with f concave decreasing).
Therefore, Y is Cconformed and C is coherent.
References
 M. Forestier, Vers une solution d'ensemble du problème de tolérancement, Revue de Statistique Appliquée 13, 83–92 (1965) (In the text)
 A. Hassani et al, Méthodologie d'analyse et d'optimisation des tolérances dans un contexte de conception intégrée, Mech. Indust. 9, 381–395 (2008) (In the text)
 J. Gilson, A New Approach to Engineering Tolerances (Machinery Publishing Co., London, England, 1951)
 V. Srinivasan, M.A. O'Connor, F.W. Scholtz, Techniques for Composing a Class of Statistical Tolerance Zones, in Advanced Tolerancing Techniques, Ed. Zhang (WileyInterscience, ISBN, 1997), 471145947, pp. 139–165 (In the text)
 E. Kureková, Measurement process capability, trends and approaches, Meas. Sci. Rev. 1, 43–46 (2001) (In the text)
 D. Grau, Testing capability indices for manufacturing processes with asymmetric tolerance limits and measurement errors, Int. J. Metrol. Qual. Eng. 2, 61–73 (2011) [CrossRef] [EDP Sciences] (In the text)
 V.E. Kane, Process capability indices, J. Qual. Technol. 18, (1986) (In the text)
 B.S. Gildeh, S. Asghari, Inertial capability index based on fuzzy, Int. J. Metrol. Qual. Eng. 2, 45–49 (2011) [CrossRef] [EDP Sciences] (In the text)
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 W.A. Taylor, Process Tolerancing: a solution to the dilemma of worstcase versus statistical tolerancing, in Fall Technical Conference (1995) (In the text)
 J.M. Judic, Process Tolerancing: A new statistical tolerancing method for industrial processes not daily adjustable in mass production. Proposal of an improvement to Wayne Taylor's method, in Proceedings of IDMME, Virtual Concept, Bordeaux, France (2010) (In the text)
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The notion of centered nominal characteristic is totally different from the notion of centered characteristic which means that a process makes it possible to produce the characteristic, on average, at the center of its tolerance interval. In other words, it is not because the nominal of a characteristic is at the center of its tolerance interval that this characteristic is centered. Unfortunately, the usual vocabulary makes confusion frequent and leads to misinterpretations.
These relationships cannot be justified by identifying mean and nominal or centers and using the relation on the averages (2). It should be noted that if the characteristics are, on average, at nominal or at the center, there is no evidence that the resultant characteristic is on average at nominal or center.
Factor h > 0 is a weighting factor introduced to ease certain constraints of the quadratic tolerancing [3]. If h = 6, it is the quadratic tolerancing strictly speaking and when , it is called probabilistic tolerancing.
These indicators are defined without any reference to the form of the law of Y_{l} even if the coefficient 3 comes from considerations of the normal law. The capability indexes which are defined by using their denominator as the length of a fluctuation interval at 99.73%, for example, are not included here because they are too dependent on the law of Y_{l}, making it difficult to exploit. Usually, Pp is said to be the potential coefficient of capability, Ppk the coefficient of performance and Ppm the capability coefficient of Taguchi.
These real numbers are chosen objectives and the reason for their ratings is due to the fact that the conformity criteria are expressed in a simplified way using the capability indexes. However, it should not be assumed that these objectives are capability indexes associated with objective decentralization concepts and objective standard deviation. These are numbers chosen to express the compliance criteria outlined.
Cite this article as: Laurent Leblond, Maurice Pillet, Conformity and statistical tolerancing, Int. J. Metrol. Qual. Eng. 9, 1 (2018)
All Figures
Fig. 1 Illustration of quantities associated with a characteristic. 

In the text 
Fig. 2 Illustration of surfaces defined by conformities for k = 3. 

In the text 
Fig. 3 Illustration of surfaces of maximum conformity. 

In the text 
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