Development of a highly efficient and resistant robust design

Lee, Seong Beom; Park, Chanseok; Cho, Byung-Rae

doi:10.1080/00207540600649202

Cited by 37 publications

(15 citation statements)

References 24 publications

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“…In this section, a simulation study is conducted to assess the performance of the newly proposed method and compare it with the commonly used methods, such as VM, LT, and WMSE. Following [13,23] the five responses ( 1 , . .…”

Section: Simulation Study and Resultsmentioning

confidence: 99%

An Alternative Approach of Dual Response Surface Optimization Based on Penalty Function Method

Baba

Midi

Rana

et al. 2015

Mathematical Problems in Engineering

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The dual response surface for simultaneously optimizing the mean and variance models as separate functions suffers some deficiencies in handling the tradeoffs between bias and variance components of mean squared error (MSE). In this paper, the accuracy of the predicted response is given a serious attention in the determination of the optimum setting conditions. We consider four different objective functions for the dual response surface optimization approach. The essence of the proposed method is to reduce the influence of variance of the predicted response by minimizing the variability relative to the quality characteristics of interest and at the same time achieving the specific target output. The basic idea is to convert the constraint optimization function into an unconstraint problem by adding the constraint to the original objective function. Numerical examples and simulations study are carried out to compare performance of the proposed method with some existing procedures. Numerical results show that the performance of the proposed method is encouraging and has exhibited clear improvement over the existing approaches.

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Section: Simulation Study and Resultsmentioning

confidence: 99%

An Alternative Approach of Dual Response Surface Optimization Based on Penalty Function Method

Baba

Midi

Rana

et al. 2015

Mathematical Problems in Engineering

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“…In the interest of finding better optimal settings in dual-response surface optimization problems when nonnormal conditions and/or outliers exist, Ch'ng et al 37 compared OLS to the M-M robust estimation technique developed by Yohai. 41 In the examination of estimators in RPD involving contaminated data, Lee et al 39 also included a comparison of the OLS method to the M-M regression technique. It is worth noting that the nonnormal conditions examined in both of these cases focused on symmetric distributions.…”

Section: Robust Parameter Design: Development and Applicationmentioning

confidence: 99%

Decision making in health care using robust parameter design with conditions‐based selection of regression estimators

Pegues

Boylan

Cho

2017

Quality & Reliability Eng

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Health care professionals often use regression methods to quantitatively describe the functional relationships between predictors and outcomes. However, little research investigates the appropriateness of tool application for response‐surface‐based design of experiments, the choice of regression estimators under different environments, and the impact of the determination of optimum conditions, or optimal factor‐level settings, to achieve desirable target outcomes. Robust parameter design (RPD) is an established methodology for determining optimum conditions for a process to achieve specified process targets while minimizing variability of the outcomes. Underlying assumptions for RPD modeling and process conditions should be taken into account when selecting a regression estimator for developing fitted models. If these assumptions are incorrect, then a direct use of estimates obtained has the potential to be problematic, and the results may be potentially catastrophic, particularly when applied to the health care field. Many approaches to RPD in existing literature use ordinary least squares to obtain response functions by assuming normality and moderate variability in the underlying process. Given that some biologic processes are often highly variable and inherently asymmetric, a conditions‐based approach for the selection of a regression estimate technique needs to be explored. This paper examines alternative approaches to regression estimation when the process data indicates that asymmetry or a high degree of process variability exists. The performance of select alternative regression methods is compared using Monte Carlo simulation and numerical analysis.

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“…Additional changes to the mean-squared error model have recently been discussed in. 7,10,15,25,29,30 Shin and Cho 27,28 proposed a bi-objective approach to simultaneously optimize process mean and variance. Besides, some dual response methods with linear models have been proposed in.…”

Section: Robust Designmentioning

confidence: 99%

“…One of them is the mean‐squared error model which may result better or equal robust design solutions unless the zero‐bias assumption must be met. Additional changes to the mean‐squared error model have recently been discussed in . Shin and Cho proposed a bi‐objective approach to simultaneously optimize process mean and variance.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Approach for Enhancing the Quality of the Product by Combining Robust Design and Customer Requirements

Shahriari

Haji

Eslamipoor

2013

Quality & Reliability Eng

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Enhancing the quality of the product has always been one considerable concern of production process management, and this subject gave way to implementing so many methods including robust design. In this paper, robust design utilizes response surface methodology (RSM) considering the mean and variance of the response variable regarding system design, parameter design, and tolerance design. In this paper, customer requirements and robust design are regarded simultaneously to achieve enriched quality. Subsequently, with a non‐linear programming, a novel method for integrating RSM and quality function deployment has been proposed to achieve robustness in design. The customer requirements are regarded in every stage of product development process meaning system design, parameter design and tolerance design. To validate the applicability of the proposed approach, it has been implemented in a real case of chemical industry. Research findings show that the proposed method is much better than other existing methods including MSE and dual response methods. According to this method, the resulted mean is better than MSE method, and more importantly, the variance of the process is approximately 14% and 10% lesser than dual response and MSE method. Copyright © 2013 John Wiley & Sons, Ltd.

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Development of a highly efficient and resistant robust design

Cited by 37 publications

References 24 publications

An Alternative Approach of Dual Response Surface Optimization Based on Penalty Function Method

An Alternative Approach of Dual Response Surface Optimization Based on Penalty Function Method

Decision making in health care using robust parameter design with conditions‐based selection of regression estimators

An Integrated Approach for Enhancing the Quality of the Product by Combining Robust Design and Customer Requirements

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