Integrated parameter and tolerance design with computer experiments

Han, Mei; Tan, Matthias Hwai Yong

doi:10.1080/0740817x.2016.1167289

Cited by 26 publications

(10 citation statements)

References 25 publications

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“…18 For more detail about estimated probability density of metric E for the solution (x c i* , t i* ), the readers can refer to Section 6.1 of Han and Tan 18 . If the values of E(x c i* , t i* ; k j , ξ) are close to one with high probability, (x c i* , t i* ) is more robust than Pareto-optimal solution (x c 0* , t 0* ) with E(x c 0* , t 0* ; k j , ξ) far from one with high probability.…”

Section: Rubber Formula Process Studymentioning

confidence: 99%

“…To design parameter and tolerance simultaneously, Li and Wu, 3 Kim and Cho, 4 Shin et al, 15 Jeang, 16,17 and Han and Tan 18,19 proposed an approach to integrate parameter design and tolerance design into a single stage of design optimization based on the location and the dispersion of a response, but they are limited to a single quality characteristic only. In most industrial situations, a common problem in product or process design is how to optimize the multiple responses simultaneously.…”

mentioning

confidence: 99%

“…Meanwhile, quality loss is used to denote the cost that occurs after the product is sold. 18 Therefore, there is usually considerable uncertainty in estimating quality loss. 17 However, some costs that are hard to estimate may not be included in calculating the quality cost.…”

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confidence: 99%

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Integrated multiresponse parameter and tolerance design with model parameter uncertainty

Han

Ouyang

et al. 2019

Quality & Reliability Eng

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Integrated parameter and tolerance design is a cost-effective method to multiresponse quality improvement. However, previous methods usually ignore model parameter uncertainty, dispersion effect, or correlation among responses. This may lead to the obtained optimal solutions far from the true optimal values of parameters and tolerances. To address the problem, a novel integrated parameter and tolerance design method is proposed to solve correlated multiple response problems under consideration of model parameter uncertainty, the location and dispersion effects of the quality loss, and the tolerance costs simultaneously. As there usually exists uncertainty in the quality loss and tolerance costs, multiobjective optimization is adopted to seek for the robust optimal solutions. The effectiveness and robustness of the proposed method are illustrated with a practical example and a random simulation example. The results show that the proposed method provides more reasonable results in quality improvement and cost reduction than those of the existing methods. KEYWORDSlocation and dispersion effects, model parameter uncertainty, multiresponse, quality loss, tolerance design | INTRODUCTIONIn today's economy, improving quality and reducing cost are essential for the business enterprise to survive. However, this cannot be achieved without effective quality or process design. An efficient and cost-effective engineering approach of product or process design is advocated by Taguchi, 1 which includes system design, parameter design, and tolerance design. Parameter design is used to reduce variation in quality characteristic first, followed by tolerance design if the variability could not be satisfactorily. For more details on robust parameter design, please see Myers et al. 2 The design factors that are allowed to vary within certain tolerance ranges due to the manufacturing error, and the design factor is supposed to follow a normal distribution in tolerance design. Conventionally, Taguchi's product design approach is known as a sequential method in which the tolerance design is performed after parameter design. Li and Wu 3 and Kim and Cho 4 argued that the sequential design method may obtain a suboptimal solution, and they proposed an integrated parameter and tolerance design (IPTD) approach to optimal parameter and tolerance design simultaneously. The advantage of IPTD is that it can reduce time-to-market, improve quality, and reduce costs by considering manufacturing processes in the early stage of product design.

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Section: Rubber Formula Process Studymentioning

confidence: 99%

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confidence: 99%

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Integrated multiresponse parameter and tolerance design with model parameter uncertainty

Han

Ouyang

et al. 2019

Quality & Reliability Eng

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“…Li and Wu demonstrate that simultaneous robust and tolerance design, ie, IPTD, can give significant improvements over two‐stage design. Han and Tan consider standard robust and tolerance design for time‐consuming nonmixture computer experiments. The existing literature on robust and tolerance design for mixture proportions focuses on the use of linear models.…”

Section: Introductionmentioning

confidence: 99%

“…Since mixture amounts cannot take negative values, the use of the normal distribution to model the uncertainty in mixture amounts may be inappropriate. Moreover, IPTD methodologies, 10,11 which can achieve larger reductions in total cost, have not been applied to mixture computer experiments.…”

Section: Introductionmentioning

confidence: 99%

Optimal robust and tolerance design for computer experiments with mixture proportion inputs

Han

Tan

2017

Quality & Reliability Eng

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Computer experiments often have inputs that are proportions/fractions of components in a mixture. In these mixture computer experiments, it can be of interest to perform robust and tolerance design on the mixture proportions since the proportions are subjected to noise variations. Traditionally, manufacturing of mixture products is controlled via interval tolerances for mixture amounts. In this paper, an optimal tolerance region for proportions, which gives optimal quality cost among all possible tolerance regions for mixture proportions with the same acceptance probability, is proposed for integrated parameter and tolerance design in mixture computer experiments. Real examples are given to demonstrate the improvements that can be achieved with the optimal tolerance region.

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Robust functional response-based metamodel optimization considering both location and dispersion effects for aeronautical airfoil designs

Han

Ouyang

2021

Struct Multidisc Optim

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Integrated parameter and tolerance design with computer experiments

Cited by 26 publications

References 25 publications

Integrated multiresponse parameter and tolerance design with model parameter uncertainty

Integrated multiresponse parameter and tolerance design with model parameter uncertainty

Optimal robust and tolerance design for computer experiments with mixture proportion inputs

Robust functional response-based metamodel optimization considering both location and dispersion effects for aeronautical airfoil designs

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