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When there is a change in a process, the MaxMin exponentially weighted moving average (EWMA) control chart shows which parameters have increased or decreased. The MaxMin EWMA may also be viewed as smoothed tolerance limits. Tolerance limits are limits that include a specific proportion of the population at a given confidence level. In the context of process control, they are used to make sure that production will not be outside specifications. In this article, we provide useful coverages for the MaxMin EWMA chart, when also used as tolerance limits. The proposed EWMA smoothed tolerance limits require relatively small sample sizes to attain useful coverages at high confidence levels. The MaxMin EWMA chart has already been successfully field‐tested and subsequently implemented with 100 multi‐stream processes.

THE first part of this article, published in last month's issue of AIRCRAFT ENGINEERING, laid stress on the importance of the technique of quality control to modern production engineering processes, and discussed the basic principles of this technique. The present issue describes the practical applications of quality control and the use of control charts.

The purpose of this paper is to present a novel Kriging meta-model assisted method for multi-objective optimal tolerance design of the mechanical assemblies based on the operating conditions under both systematic and random uncertainties.

In the proposed method, the performance, the quality loss and the manufacturing cost issues are formulated as the main criteria in terms of systematic and random uncertainties. To investigate the mechanical assembly under the operating conditions, the behavior of the assembly can be simulated based on the finite element analysis (FEA). The objective functions in terms of uncertainties at the operating conditions can be modeled through the Kriging-based metamodeling based on the obtained results from the FEA simulations. Then, the optimal tolerance allocation procedure is formulated as a multi-objective optimization framework. For solving the multi conflicting objectives optimization problem, the multi-objective particle swarm optimization method is used. Then, a Shannon’s entropy-based TOPSIS is used for selection of the best tolerances from the optimal Pareto solutions.

The proposed method can be used for optimal tolerance design of mechanical assemblies in the operating conditions with including both random and systematic uncertainties. To reach an accurate model of the design function at the operating conditions, the Kriging meta-modeling is used. The efficiency of the proposed method by considering a case study is illustrated and the method is verified by comparison to a conventional tolerance allocation method. The obtained results show that using the proposed method can lead to the product with a more robust efficiency in the performance and a higher quality in comparing to the conventional results.

The proposed method is limited to the dimensional tolerances of components with the normal distribution.

The proposed method is practically easy to be automated for computer-aided tolerance design in industrial applications.

In conventional approaches, regardless of systematic and random uncertainties due to operating conditions, tolerances are allocated based on the assembly conditions. As uncertainties can significantly affect the system’s performance at operating conditions, tolerance allocation without including these effects may be inefficient. This paper aims to fill this gap in the literature by considering both systematic and random uncertainties for multi-objective optimal tolerance design of mechanical assemblies under operating conditions.

The purpose of this paper is to present a new efficient method for the tolerance–reliability analysis and quality control of complex nonlinear assemblies where explicit assembly functions are difficult or impossible to extract based on Bayesian modeling.

In the proposed method, first, tolerances are modelled as the random uncertain variables. Then, based on the assembly data, the explicit assembly function can be expressed by the Bayesian model in terms of manufacturing and assembly tolerances. According to the obtained assembly tolerance, reliability of the mechanical assembly to meet the assembly requirement can be estimated by a proper first-order reliability method.

The Bayesian modeling leads to an appropriate assembly function for the tolerance and reliability analysis of mechanical assemblies for assessment of the assembly quality, by evaluation of the assembly requirement(s) at the key characteristics in the assembly process. The efficiency of the proposed method by considering a case study has been illustrated and validated by comparison to Monte Carlo simulations.

The method is practically easy to be automated for use within CAD/CAM software for the assembly quality control in industrial applications.

Bayesian modeling for tolerance–reliability analysis of mechanical assemblies, which has not been previously considered in the literature, is a potentially interesting concept that can be extended to other corresponding fields of the tolerance design and the quality control.

In 1991 Lithuania reclaimed its independence from the Soviet Union and subsequently enlisted its education system as a tool for imparting the democratic skills and worldviews necessary for EU accession. However, the internalization of new democratic norms proved to be more complicated than the unidirectional transmission expected by many elites, as students, parents, and politicians played a part in the way that educational reforms were understood, implemented, embodied, and even resisted. Although tolerance education was initially included in Lithuanian reforms with little fanfare, there has been an increasingly visible backlash against it, as some now see its existence as an encroachment on the right of “Lithuanians” to develop a strong national identity after 60 years of occupation. By analyzing key educational policies in Lithuania, as well as international barometers for social tolerance, this chapter finds that the embrace of intolerance by many individuals and elites in Lithuania is not just a proclivity for prejudice, but a tool for challenging the boundaries of EU expectations to define the values and norms of an independent nation-state.

A variety of quantitative specifications, usually in terms of numerical limits, have been developed in industry for the description, prediction and control of product quality. As the theoretical foundations of these specifications are often beyond the working knowledge of many manufacturing engineers and managers, this article gives a non‐mathematical account of some of the limits commonly encountered in discussions related to product design, manufacture and inspection; emphasis is placed on the distinctions in their intended purposes and methods of application.

This paper summarizes the evolution of mechanical tolerancing practices, the general character of tolerances as they are currently understood, the current state of tolerancing technologies, and the recent surge of activity aimed at rationalizing and “mathematizing” form tolerancing. The paper concludes with two examples that illustrate current research frontiers.

Traditionally, tolerances are defined by an interval [LSL; USL] which can lead to several ambiguous interpretations of conformity. This paper examines an alternative method for setting specifications: “inertial tolerancing”. Inertial tolerancing consists of tolerancing the mean square deviation from the target rather than the distance. This alternative has numerous advantages over the traditional approach, particularly in the case of product assembly, mixed batches and conformity analysis. Coupled with a capability index Cpi, this alternative method leads to minimizing production costs for a specified level of quality. We propose to compare both approaches: traditional and inertial tolerancing.

This paper aims to develop a method to improve the accuracy of tolerance analysis considering the spatial distribution characteristics of part surface morphology (SDCPSM) and local surface deformations (LSD) of planar mating surfaces during the assembly process.

First, this paper proposes a skin modeling method considering SDCPSM based on Non-Gaussian random field. Second, based on the skin model shapes, an improved boundary element method is adopted to solve LSD of nonideal planar mating surfaces, and the progressive contact method is adopted to obtain relative positioning deviation of mating surfaces. Finally, the case study is given to verify the proposed approach.

Through the case study, the results show that different SDCPSM have different influences on tolerance analysis, and LSD have nonnegligible and different influence on tolerance analysis considering different SDCPSM. In addition, the LSD have a greater influence on translational deviation along the z-axis than rotational deviation around the x- and y-axes.

The surface morphology with different spatial distribution characteristics leads to different contact behavior of planar mating surfaces, especially when considering the LSD of mating surfaces during the assembly process, which will have further influence on tolerance analysis. To address the above problem, this paper proposes a tolerance analysis method with skin modeling considering SDCPSM and LSD of mating surfaces, which can help to improve the accuracy of tolerance analysis.

This paper aims to illustrate the tolerance optimization method based on the assembly accuracy constrain, precession constrain and the cost of production of the assembly product.

A tolerance optimization method is an excellent way to perform product assembly performance. The tolerance optimization method is adapted to the process analysis of the hatch and skin of an aircraft. In this paper, the tolerance optimization techniques are applied to the tolerance allocation for step difference analysis (example: step difference between aircraft cabin door and fuselage outer skin). First, a mathematical model is described to understand the relationship between manufacturing cost and tolerance cost. Second, the penalty function method is applied to form a new equation for tolerance optimization. Finally, MATLAB software is used to calculate 170 loops iteration to understand the efficiency of the new equation for tolerance optimization.

The tolerance optimization method is based on the assembly accuracy constrain, machinery constrain and the cost of production of the assembly product. The main finding of this paper is the lowest assembly and lowest production costs that met the product tolerance specification.

This paper illustrated an efficient method of tolerance allocation for products assembly. After 170 loops iterations, it founds that the results very close to the original required tolerance. But it can easily say that the different number of loops iterations may have a different result. But optimization result must be approximate to the original tolerance requirements.

It is evident from Table 4 that the tolerance of the closed loop is 1.3999 after the tolerance distribution is completed, which is less than and very close to the original tolerance of 1.40; the machining precision constraint of the outer skin of the cabin door and the fuselage is satisfied, and the assembly precision constraint of the closed loop is satisfied.

The research may support further research studies to minimize cost tolerance allocation using tolerance cost optimization techniques, which must meet the given constrain accuracy for assembly products.