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# Uncertainty analysis of the coefficients of friction during the tightening process of bolted joints

- Arthur Seibel
^{1}Email author, - Andreas Japing
^{1}and - Josef Schlattmann
^{1}

**2**:21

https://doi.org/10.1186/s40467-014-0021-5

© Seibel et al.; licensee Springer. 2014

**Received:**18 February 2014**Accepted:**16 September 2014**Published:**3 October 2014

## Abstract

In this paper, we introduce closed-form symbolic expressions for the possibility distributions of the coefficients of friction during the tightening process of bolted joints. The parameters in the distribution functions are then identified by a standardized fastener testing system according to ISO 16047. An uncertainty analysis finally shows that the total amount of uncertainty in the coefficient of bearing friction is almost 40% larger than in the coefficient of thread friction. Furthermore, the real value of the coefficent of bearing friction is likely to be about 5% higher than the expected value.

## Keywords

- Bolted joints tribology
- Coefficient of bearing friction
- Coefficient of thread friction
- Uncertainty analysis
- Possibility theory

## Introduction

Bolted joints are among the most used joints in mechanical engineering. But failures still occur during the tightening process and in operation. In order to improve the quality and reliability of bolted joints, a main goal is to achieve a proper clamping force. With a certain effort [1], it is possible to measure the clamping force directly. But most of the bolts and screws still are assembled by an indirect measurement of the clamping force [2].

*T*for tightening a bolted joint is determined by [3]

where *T*_{b} denotes the bearing friction torque component and *T*_{t} the thread friction torque component. These components are in a direct relationship with the clamping force and the coefficients of friction of the particular material pairing. Hence, the information about the coefficients of friction has a great influence on the quality and reliability of bolted joint connections.

Suppliers of bolts and screws often give only parameter windows for the desired coefficients of friction. These intervals are rather general and may not be suitable for individual applications. Furthermore, no information about the distribution functions of the coefficients of friction is provided.

*p*(

*x*) is equal to one:

*π*(

*x*) are always normalized:

*σ*the standard deviation of

*f*(

*x*). In probability theory, the constant scaling factor is set to

to meet the requirement (3).

The general disadvantage in working with probability distribution functions is that in order to construct a probabilistic model, a large amount of statistical data is needed, which, in reality, is not always the case. In contrast to that, when only little information about the uncertain parameters is available, possibility theory is most appropriate. For this reason, we use possibility theory in this paper to model parametric uncertainty.

The main contributions of this paper are the following. First, closed-form symbolic expressions for the possibility distributions of the coefficients of friction during the tightening process of bolted joints are introduced for the first time. The parameters in the distribution functions are then identified by a standardized fastener testing system according to ISO 16047. An uncertainty analysis finally compares the total amount of uncertainty in the coefficient of bearing friction with that in the coefficient of thread friction. Furthermore, the deviations of the real values of the coefficients of friction from the expected values are given.

## Possibility theory

*π*(

*x*) of the parameter

*x*[7]. More specifically, the possibility distribution

*π*(

*x*) is a function that maps a value

*x*to the possibility of the singleton event {

*x*} [8]:

*P*(

*A*) of an event

*A*can be viewed as a lower bound for the corresponding possibility Π(

*A*) [9]:

This is in accordance with intuition since `before an event becomes probable, it must be possible' [8]. For a detailed overview of possibility theory, the reader is referred to [5].

## Sensitivity analysis

of the model function *f* at the point $\stackrel{-}{\mathit{x}}=({\stackrel{-}{x}}_{1},\dots ,{\stackrel{-}{x}}_{n})$, where d *f* represents the total change of *f* in a neighborhood of $\stackrel{-}{\mathit{x}}$ if all parameters are changed simultaneously. The change d*x*_{
i
} of a single parameter *x*_{
i
} contributes to the amount (d *f*)_{
i
} to the total change d *f*.

*x*

_{ i }of the parameters

*x*

_{ i }are assumed to be a constant fraction

*c*of the corresponding modal values ${\stackrel{-}{x}}_{i}$, the total differential from Equation (4) can be written as [10]

## Uncertainty propagation

In order to propagate the uncertainties through the computations, we introduce the following transformation:

*x*in the

*x*domain, where

*π*

^{L}(

*x*) denotes the left branch,

*π*

^{R}(

*x*) the right branch, and $\stackrel{-}{x}$ the modal value of

*π*(

*x*). The transformation of

*π*(

*x*) into the

*π*domain leads to the (generalized) interval

*x*(

*π*)=[

*x*

^{L}(

*π*),

*x*

^{R}(

*π*)] with

Using the above transformation, we can formulate the following analytical approach [11]:

*π*(

*x*

_{1}),…,

*π*(

*x*

_{ n }) be the possibility distributions of the

*n*independent parameters

*x*

_{1},…,

*x*

_{ n }, and let

*f*: ℝ

^{ n }→

*ℝ*be a continuous function with

*y*=

*f*(

*x*

_{1},…,

*x*

_{ n }). Furthermore, let

*f*be (strictly) monotonic increasing in

*x*

_{ i },

*i*=1,…,

*k*, and (strictly) monotonic decreasing in

*x*

_{ j },

*j*=

*k*+1,…,

*n*, in the domain of interest. Then, the possibility distribution

*y*(

*π*)=[

*y*

^{L}(

*π*),

*y*

^{R}(

*π*)] of

*y*in the

*π*domain is determined by

*π*, then the possibility distribution of

*y*in the

*y*domain yields

## Distribution functions

In this section, we use the above analytical approach to derive closed-form symbolic expressions for the distribution functions of the coefficients of friction during the tightening process of bolted joints.

### Coefficient of bearing friction

*μ*

_{b}can be computed from [3]

where *T*_{b} denotes the bearing friction torque component, *F* the clamping force, and *r*_{b} the effective bearing radius.

During the tightening process, the clamping force *F* should be achieved exactly. Hence, it exhibits no uncertainty. The parameters *T*_{b} and *r*_{b}, on the other hand, are assumed to be normally distributed.

*μ*

_{b}are

Hence, the uncertainties of *T*_{b} and *r*_{b} contribute to the same amount to the overall uncertainty of *μ*_{b}.

*μ*

_{b}is (strictly) monotonic increasing in

*T*

_{b}and (strictly) monotonic decreasing in

*r*

_{b}for positive values. Hence, according to Equations (6), the possibility distribution of

*π*

_{b}in the

*μ*domain is ${\mu}_{\mathrm{b}}\left(\pi \right)=\left[\phantom{\rule{0.3em}{0ex}}{\mu}_{\mathrm{b}}^{\mathrm{L}}\left(\pi \right),{\mu}_{\mathrm{b}}^{\mathrm{R}}\left(\pi \right)\right]$ with

*μ*

_{b}domain,

### Coefficient of thread friction

*μ*

_{t}can be approximated by [3]

where *d*_{t} denotes the effective thread diameter, *T*_{t} the thread friction torque component, *F* the clamping force, and *P* the thread pitch. (Note that the possibility degree *π*, which is used throughout the paper, should not be confused with the mathematical constant *π*=3.14.).

Again, the clamping force *F* is assumed to exhibit no uncertainty, and the other parameters are assumed to be normally distributed.

*μ*

_{t}are

Hence, the uncertainties of *d*_{t} and *T*_{t} contribute to the nearly same amount to the overall uncertainty of *μ*_{t}, whereas the uncertainty of *P* has a negligible influence.

*μ*

_{t}is (strictly) monotonic increasing in

*T*

_{t}and (strictly) monotonic decreasing in

*d*

_{t}for positive values. Hence, according to Equations (6), the possibility distribution of

*μ*

_{t}in the

*π*domain is ${\mu}_{\mathrm{t}}\left(\pi \phantom{\rule{0.3em}{0ex}}\right)=\left[{\mu}_{\mathrm{t}}^{\mathrm{L}}\left(\pi \phantom{\rule{0.3em}{0ex}}\right),{\mu}_{\mathrm{t}}^{\mathrm{R}}\left(\pi \phantom{\rule{0.3em}{0ex}}\right)\right]$ with

*μ*

_{t}domain,

## Parameter identification

*μ*

_{b}and

*μ*

_{t}, practical experiments had been carried out on a fastener testing system according to ISO 16047 [12] as illustrated in Figure 2. This system contains a multi-parameter sensor that enables a measurement of the fastener preload, the tightening torque, and one of the friction torque components at the same time.

### Materials and methods

**Descriptions and materials of the test parts**

Test part | Description/material | Remark |
---|---|---|

Bolt | DIN 6921 M8 × 50 10.9 | Black chromated |

Nut | DIN 934 M8 8.8 | Black chromated |

Flat bar | S235JRC+C (EN 1.0122) | Surface polished |

For carrying out the experiments, we followed the instructions from ISO 16047 [12]. A tightening torque was steadily applied until a clamping force of 16 kN was reached. Here, *F*, *T*, and *T*_{b} were measured, and *T*_{t} was computed according to Equation (1). The sample size was 32 bolts.

### Experimental results

*r*

_{b}is assumed to be normally distributed with the mean value

where *d*_{o} denotes the outer diameter and *d*_{i} the inner diameter of the bearing surface.

*d*

_{t}is also assumed to be normally distributed with the mean value

where *d*_{2} denotes the thread pitch diameter, *d* the nominal thread diameter, and *D*_{1} the minor nut thread diameter. The standard deviations are chosen such that the intervals [ *d*_{i}, *d*_{o}] and [ *D*_{1}, *d*] correspond to $6\u20222\phantom{\rule{0.3em}{0ex}}{\u2022}_{{r}_{\mathrm{b}}}$ and $6\phantom{\rule{0.3em}{0ex}}{\sigma}_{{d}_{\mathrm{t}}}$, respectively. (The factor two at ${\sigma}_{{r}_{\mathrm{b}}}$ results from the fact that the standard deviation of a diameter is twice as large as the standard deviation of the corresponding radius). Beyond the interval boundaries, the possibility values are smaller than 1% [14] and can be thus neglected. In fact, they are physically impossible.

*μ*

_{b}and

*μ*

_{t}for

*F*ϵ [ 2,16] kN are illustrated in Figure 4. We can see that after an initial shakedown, a steady-state distribution is always reached.

*F*=12 kN. The distribution functions of

*μ*

_{b}and

*μ*

_{t}at this point are

## Uncertainty analysis

*π*(

*x*), we use the (absolute) cardinality [14]:

meaning that in our application, the total amount of uncertainty in *μ*_{b} is almost 40% larger than in *μ*_{t}.

denotes the defuzzified value of *π*(*x*).

Both eccentricities are positive, that is, the real values of the coefficients of friction will be potentially higher than the expected values. More specifically, the real value of *μ*_{b} is likely to be about 5% and the real value of *μ*_{t} to be about 1% higher than the corresponding expected value.

## Conclusions

We introduced closed-form symbolic expressions for the possibility distributions of the coefficients of friction during the tightening process of bolted joints. This relieves the engineer from the burden of propagating the uncertainties through the computations to obtain the uncertain output. The parameters in the distribution functions can be identified by a standardized fastener testing system according to ISO 16047 as has been demonstrated in this paper. An uncertainty analysis also revealed that the total amount of uncertainty in the coefficient of bearing friction is almost 40% larger than in the coefficient of thread friction. This finding suggests that in practice, it is more important to control the coefficient of bearing friction than the coefficient of thread friction. The uncertainty analysis also showed that the real value of the coefficient of bearing friction is likely to be about 5% and the real value of the coefficient of thread friction to be about 1% higher than the corresponding expected value.

## Declarations

## Authors’ Affiliations

## References

- Baker R: Ultrasonic tightening.
*Sealing Technol*2011, 2011(8):10–12. 10.1016/S1350-4789(11)70303-8View ArticleGoogle Scholar - Kloos K-H, Thomala W:
*Schraubenverbindungen: Grundlagen, Berechnung, Eigenschaften, Handhabung*. Springer, Berlin; 2007.Google Scholar - Kellermann R, Klein H-C: Untersuchungen über den Einfluß der Reibung auf Vorspannung und Anzugsmoment von Schraubenverbindungen.
*Konstruktion*1955, 7(2):54–68.Google Scholar - Marczyk J:
*Principles of Simulation-Based Computer-Aided Engineering*. FIM Publications, Barcelona; 1999.Google Scholar - Dubois D, Prade H:
*Possibility Theory: An Approach to Computerized Processing of Uncertainty*. Plenum, New York; 1988.View ArticleMATHGoogle Scholar - Zadeh LA: Fuzzy sets.
*Inform Contr*1965, 8: 338–353. 10.1016/S0019-9958(65)90241-XMathSciNetView ArticleMATHGoogle Scholar - Zadeh LA: Fuzzy sets as a basis for a theory of possibility.
*Fuzzy Set Syst*1978, 1: 3–28. 10.1016/0165-0114(78)90029-5MathSciNetView ArticleMATHGoogle Scholar - Degrauwe D:
*Uncertainty propagation in structural analysis by fuzzy numbers*. PhD Thesis, Katholieke Universiteit Leuven, Leuven, Belgium; 2007.Google Scholar - Bothe H-H:
*Fuzzy Logic: Einführung in Theorie und Anwendungen*. Springer, Berlin; 1993.View ArticleGoogle Scholar - Hanss M, Klimke A: On the reliability of the influence measure in the transformation method of fuzzy arithmetic.
*Fuzzy Set Syst*2004, 143(3):371–390. 10.1016/S0165-0114(03)00163-5MathSciNetView ArticleMATHGoogle Scholar - Seibel, A, Schlattmann, J: An analytical approach to evaluating monotonic functions of fuzzy numbers. In: Proceedings of the 8th Conference of the European Society for Fuzzy Logic and Technology, Milano, Italy, 11-13 Sept 2013, pp. 289–293 (2013).Google Scholar
- International Organization for Standardization: ISO 16047: Fasteners—Torque/Clamp Force Testing. International Organization for Standardization, Geneva; 2005.Google Scholar
- Zou Q, Zhu D, Sun TS, Nassar S, Barber GC, El-Khiamy H: Contact mechanics approach to determine effective radius in bolted joints.
*J Tribol*2005, 127(1):30–36. 10.1115/1.1829717View ArticleGoogle Scholar - Hanss M:
*Applied Fuzzy Arithmetic: An Introduction with Engineering Applications*. Springer, Berlin; 2005.MATHGoogle Scholar

## Copyright

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.