The Optimal Tolerance Assignment With Less Than Full Acceptance

Michael, W.; Siddall, J. N.

doi:10.1115/1.3256448

Cited by 62 publications

(16 citation statements)

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“…This exponential hybrid manufacturing cost-tolerance function is often considered in the literature (see Michael and Siddall 1981, 1982, Kim and Cho 2000. Using Equation (16), the expected manufacturing cost can then be written as…”

Section: Manufacturing Costmentioning

confidence: 99%

Development of a sequential optimization procedure for robust design and tolerance design within a bi-objective paradigm

Shin

Cho

2008

Engineering Optimization

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Many practitioners and researchers have implemented robust design and tolerance design as quality improvement and process optimization tools for more than two decades. Robust design is an enhanced process/product design methodology for determining the best settings of control factors while minimizing process bias and variability. Tolerance design is aimed at determining the best tolerance limits for minimizing the total cost incurred by both the customer and manufacturer by balancing quality loss due to variations in product performance and the cost of controlling these variations. Although robust design and tolerance design have received much attention from researchers and practitioners, there is ample room for improvement. First, most researchers consider robust design and tolerance design as separate research fields. Second, most research work is based on a single quality characteristic. The primary goal of this paper is to integrate a sequential robust design-tolerance design optimization procedure within a bi-objective paradigm, which, the authors believe, is the first attempt in the robust design and tolerance design literature. Models are proposed and numerical examples along with sensitivity analysis are performed for verification purposes.

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Section: Manufacturing Costmentioning

confidence: 99%

Development of a sequential optimization procedure for robust design and tolerance design within a bi-objective paradigm

Shin

Cho

2008

Engineering Optimization

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show abstract

“…The majority of the published articles on tolerance synthesis are based on optimization, most of which use the cost-tolerance models. Michael and Siddall [1] extended the conventional design optimization problem, in which the nominal values of the design variables are of interest, to include the optimal allocation of manufacturing tolerances. Lee and Woo [2] formulated the tolerance synthesis as a probabilistic optimization problem in which a random variable is associated with a dimension and its tolerance.…”

Section: Literature Reviewmentioning

confidence: 99%

Particle swarm optimization (PSO) algorithm for optimal machining allocation of clutch assembly

Haq

Karthikeyan

Sivakumar³

et al. 2005

Int J Adv Manuf Technol

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Nowadays tolerance optimization is increasingly becoming an important tool for manufacturing and mechanical design. This seemingly, arbitrary task of assigning dimension tolerance can have a large effect on the cost and performance of manufactured products. With the increase in competition in today's market place, small savings in cost or small increase in performance may determine the success of a product. In practical applications, tolerances are most often assigned as informal compromises between functional quality and manufacturing cost. Frequently the compromise is obtained interactively by trial and error. A more scientific approach is often desirable for better performance. In this paper particle swarm optimization (PSO) is used for the optimal machining tolerance allocation of over running clutch assembly to obtain the global optimal solution. The objective is to obtain optimum tolerances of the individual components for the minimum cost of manufacturing. The result obtained by PSO is compared with the geometric programming (GP) and genetic algorithm (GA) and the performance of the result are analyzed.

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“…The majority of the published articles on tolerance synthesis are based on optimization, most of which use the cost-tolerance models. Michael and Siddall [2] extended the conventional design optimization problem, in which the nominal values of the design variables are of interest, to include the optimal allocation of manufacturing tolerances. Lee and Woo [3] formulated the tolerance synthesis as a probabilistic optimization problem in which a random variable is associated with a dimension and its tolerance.…”

Section: Literature Reviewmentioning

confidence: 99%

Tolerance design optimization of machine elements using genetic algorithm

Haq

Sivakumar²,

Saravanan

et al. 2004

Int J Adv Manuf Technol

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An important problem that faces design engineers is how to assign tolerance limits. In practical applications, tolerances are most often assigned as an informal compromise between functionality, quality and manufacturing cost. Frequently, the compromise is obtained iteratively by trial and error. A more scientific approach is often desirable for better performance. In this paper, a genetic algorithm (GA) is used for the design of tolerances of machine elements to obtain the global optimal solution. The objective is to design the optimum tolerances of the individual components to achieve the required assembly tolerance, zero percentage rejection of the components and minimum cost of manufacturing. The proposed procedure using GA is described in this paper for two tolerance design optimization problems: gear train and overrunning clutch assemblies. Results are compared with conventional techniques and the performances are analyzed.

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The Optimal Tolerance Assignment With Less Than Full Acceptance

Cited by 62 publications

References 0 publications

Development of a sequential optimization procedure for robust design and tolerance design within a bi-objective paradigm

Development of a sequential optimization procedure for robust design and tolerance design within a bi-objective paradigm

Particle swarm optimization (PSO) algorithm for optimal machining allocation of clutch assembly

Tolerance design optimization of machine elements using genetic algorithm

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