Robust Tolerance Design Research Articles

A Robust Tolerance Design Method Based on Fuzzy Quality Loss

The traditional tolerance design model ignores the impact of noise factor, so that the design may be infeasible due to variations in design constraints. Based on the analysis of fuzzy factors in tolerance design and the limitations of the traditional Taguchi squared quality loss function, a fuzzy quality loss function model utilizing fuzzy theory was introduced. Concepts on fuzzy quality loss and fuzzy quality loss cost were proposed in the model. The characteristics of the new model and the advantages over the traditional Taguchi quality loss function were analyzed. A robust tolerance design model using a fuzzy quality loss function was proposed. An example was given to illustrate the proposed model. Results and comparisons show that the method is suitable and reliable, and makes the conclusions more objective and reasonable.

A robust tolerance design method based on process capability

A robust tolerance design method based on process capability

A robust tolerance design method based on process capability

A robust tolerance design method based on process capability

Combined Robust Parameter and Tolerance Design Using Orthogonal Arrays

Conventionally, parameter design is carried out prior to tolerance design for economic considerations. However, this non-combined (two-step) design strategy cannot guarantee an economic and quality product for some quality characteristics. The rapid development of new products, and the planning and early implementation of product development are the important keys to competitiveness. This paper presents an approach based on the techniques of orthogonal arrays, computer simulation, and statistical methods. It also adopts a cost function that represents the combined impact of assigned parameters and tolerance values. This cost function is the sum of the tolerance cost and the quality loss. Computer simulation generates a set of experimental data by following the experimental design suggested by the orthogonal array. The orthogonal array experiment enables an engineer to generate experimental data required for statistical analysis with less experimental effort. Based on the experimental data and cost function, a set of response values are found for statistical analysis, and for detecting the critical assignment of parameter and tolerance values. As a result, a combined robust parameter and tolerance design for quality improvement and cost reduction can be achieved effectively at an early stage of design and planning.

A computer model for time-based tolerance design with response surface methodology

The Response Surface Methodology (RSM) approach is adapted to data analysis for computer simulation to determine the optimal component tolerance in an assembly. RSM is a combination of mathematical and statistical techniques, which provides designers not only with optimal values but also with the critical components of an assembly design. These two properties provide designers with guidelines, feedback, and suggestions during the design processes. Since the relevant quality characteristics in an assembly are systematically shifted as the customer using time increases, due to the wear process in some components, a time-based tolerance analysis should be included for assembly design. In this study, the response values for measuring relevant quality characteristics at various times are the process capability index and the total cost. The total cost consists of quality loss and tolerance cost. Because most of the assembly functions are unknown, difficult to obtain, or need to be changed during the design process, VSA-3D/Pro software is employed to run a computer simulation and convert the computer experimental data into response values. Based on these values, a robust and optimal tolerance design is achieved through RSM analysis. As a result, a tolerance design for quality improvement and cost reduction can be completed in terms of product life cycle.

An integrated joint optimization procedure for robust and tolerance design

Many manufacturers have discovered that optimizing design parameters is a costeffective means of improving product quality and being competitive in the world market. In this regard, the issues of robust design (RD) and tolerance design (TD) are clearly important, but there is significant room for improvement. The primary objective of this paper is to propose a set of enhanced optimization strategies by combining RD and TD. To be more specific, first, we consider an alternative experimental scheme using response surface methodology, while avoiding the use of controversial tools for RD such as orthogonal arrays and signal-to-noise ratios. Secondly, we discuss an enhanced optimization model by simultaneously considering both the process mean and variance, and then show that this model provides a better (or at least equal) solution in terms of the control factor settings. Thirdly, we show how the response functions for the process mean and variance, which are estimated by using an RD principle, are transmitted into the TD stage. Fourthly, we propose an optimization model for TD and present closed-form solutions for optimum tolerance limits. Finally, we study the possible effects of major cost components, and observe the behaviour of the optimum control parameter settings and the tolerance limits by carrying out sensitivity analysis.

Robust Tolerance Design by Response Surface Methodology

The Response Surface Methodology (RSM) approach is adopted for data analysis for an experimental model in order to determine the optimal component tolerances in an assembly. The response variable is the total cost, which consists of quality loss and tolerance cost. RSM is a combination of mathematical and statistical techniques, which provides designers, not only with the optimal tolerance values, but also with the critical components in an assembly. This feature is very important during the design activities since it enables designers to have feedback and suggestions for possible design improvement. Using RSM to optimise and analyse the experimental results, a tolerance design for quality improvement and cost reduction can be achieved at an early stage of design, particularly, in an uncertain design environment.

Robust tolerance design by computer experiment

Tolerance design affects the quality and cost of a product cycle time. Most of the literature on tolerance design problems has focused on developing exact methods to minimize manufacturing cost or quality loss. The inherent assumption in this approach is that the assembly function is known before a tolerance design problem is analysed. With the current development in CAD (Computer-Aided Design) software, design engineers can proceed with the tolerance design problems, without knowing assembly functions in advance. In this study, the Monte Carlo simulation is employed using VSA-3D/Pro software to obtain experimental data. Then the design of experiments (DOE) approach is adopted for data analysis in order to select critical components for cost reduction and quality improvement. Implementing the discussed computer experiments, a tolerance design analysis which improves quality and reduces cost can be performed for any complex assembly via computer during the early stage of design.

Tolerance design for products with asymmetric quality losses

This paper presents a design method for allocating dimensional tolerances of products with asymmetric quality losses. The design strategies proposed include the adjustment of manufacturing target and the approach of robust tolerance design. The average quality losses of batch products are computed differently in accordance with the distribution of functional characteristics. The robust tolerance design is based on the balance between manufacture cost and quality loss expected. An illustrative example shows the proposed procedure for the tolerance design of a helical spring.

ROBUST TOLERANCE ALLOCATION USING STOCHASTIC PROGRAMMING

Tolerance allocation in manufacturing is a prominent industrial task for enhancing productivity and reducing manufacturing costs. The classical tolerance allocation problem can be formulated as a stochastic program to determine the assignment of component tolerances such that the manufacturing cost is minimized. However, tolerance design is a prerequisite to the overall quality and cost of a product; robust tolerance design is particularly important and should be considered. In this paper, robustness is considered in formulating the tolerance allocation problem by minimizing the manufacturing cost's sensitivity. Moreover, from a practical perspective, the process capability index for each component and the upper bound of the manufacturing cost are also considered. To effectively and efficiently resolve the robust tolerance allocation problem, a sequential quadratic programming algorithm embedded with a Monte Carlo simulation is developed. To demonstrate this design method's robustness, two commonly used test problems are solved. The designs devised in this paper have lower manufacturing costs and smaller variations in manufacturing costs than those in previous studies, indicating that the proposed method is highly promising in the robust tolerance design.

Robust design of assembly and machining tolerance allocations

Tolerancing is one of the most important tasks in product and manufacturing process design. The allocation of design tolerances between the components of a mechanical assembly and manufacturing tolerances in the intermediate machining steps of component fabrication can significantly affect a product's quality and its robustness. This paper presents a methodology to maximize a product's robustness by appropriately allocating assembly and machining tolerances. The robust tolerance design problem is formulated as a mixed nonlinear optimization model. A simulated annealing algorithm is employed to solve the model and an example is presented to illustrate the methodology

Quality by Experimental Design.

THE PHILOSOPHY OF EXPERIMENTATION Why Design Experiments? Organizing the Experiment The Neglected Response Variable STATISTICAL EXPERIMENTAL DESIGN The Factorial 2-Level Design General Factorial Designs Fractional Factorials at 2-Levels Multi-Level Designs 3-Level Designs Randomized Block and Latin Square Nested Designs Evolutionary Operation SORTING THE SIGNAL FROM THE NOISE Simple Analysis Analysis of Means by Using the Variance YATES Analysis Analysis of 2k and 2k-p Designs Matrix Algebra Least Squares Analysis Putting ANOVA and Least Squares to Work ANOVA for Blocked and Nested Designs THE DERIVATION OF EMPIRICAL EQUATIONS FROM STATISTICALLY DESIGNED EXPERIMENTS A Case History of an Experimental Investigation UTILIZATION OF EMPIRICAL EQUATIONS Robust Design Monte Carlo Simulation and Tolerance Design Case History Completed: The Utilization of the Equation Statistical Tables Index

ROBUST DESIGN USING COMPROMISE DECISION SUPPORT PROBLEMS

Compromise Decision Support Problems (DSPs) are used to model engineering decisions involving multiple trade-offs. In this paper, the focus is on how to apply such decision models in robust design. Suh's independence and information content axioms and Taguchi's signal to noise ratio are used as metrics for the assessment and improvement of the quality in this decision model. As an example, a compromise DSP for the robust design of an electrical network is used. Traditionally, in robust design, parameter and tolerance design are done sequentially and not concurrently. Furthermore, each time parameter and tolerance design are done in practice, the focus is usually on looking at one parameter at a time and not on looking at multiple parameters simultaneously. Using the electrical network as an example, it is shown how parameter and tolerance design involving multiple parameters can be performed concurrently.