Multiresponse Robust Design: A General Framework Based on Combined Array

Romano, Daniele; Varetto, Marco; Vicario, Grazia

doi:10.1080/00224065.2004.11980250

Cited by 48 publications

(31 citation statements)

References 18 publications

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“…Under such condition, the multi-response model must be able to consider the correlation among quality characteristic. A number of recent studies which have been attended variance-covariance framework of responses are Cheng et al (2013), Rathod et al (2013), Romano et al (2004) and Salmasnia et al (2013).…”

Section: Multi-objective Robust Optimizationmentioning

confidence: 99%

An overview on robust design hybrid metamodeling: Advanced methodology in process optimization under uncertainty

Parnianifard¹,

Azfanizam²,

Ariffin³

et al. 2018

10.5267/j.ijiec

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Nowadays, process optimization has been an interest in engineering design for improving the performance and reducing cost. In practice, in addition to uncertainty or noise parameters, a comprehensive optimization model must be able to attend other circumstances which might be imposed in problems under real operational conditions such as dynamic objectives, multiresponses, various probabilistic distribution, discrete and continuous data, physical constraints to design parameters, performance cost, computational complexity and multi-process environment. The main goal of this paper is to give a general overview on topics with brief systematic review and concise discussions about the recent development on comprehensive robust design optimization methods under hybrid aforesaid circumstances. Both optimization methods of mathematical programming based on Taguchi approach and robust optimization based on scenario sets are briefly described. Metamodels hybrid robust design is discussed as an appropriate methodology to decrease computational complexity in problems under uncertainty. In this context, the authors' policy is to choose important topics for giving a systematic picture to those who wish to be more familiar with recent studies about robust design optimization hybrid metamodels, also by attending real circumstances in practice. In particular, production and project management are considered as two important methodologies that could be improved by applications of advanced robust design combining with metamodel methods.

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Section: Multi-objective Robust Optimizationmentioning

confidence: 99%

An overview on robust design hybrid metamodeling: Advanced methodology in process optimization under uncertainty

Parnianifard¹,

Azfanizam²,

Ariffin³

et al. 2018

10.5267/j.ijiec

View full text Add to dashboard Cite

show abstract

“…A compromise approach by Vining [34] has provided promising results. Multiresponse robust design seems to be greatly extended by the method of combined array [35]. Recent research on the area of programming a numerical control machine has turned up useful accounts on multiple comparisons by introducing a new function that incorporates location and dispersion effects [36].…”

Section: Literature Reviewmentioning

confidence: 99%

Product Screening to Multicustomer Preferences: Multiresponse Unreplicated Nested Super-ranking

Besseris

2008

International Journal of Quality, Statistics, and Reliability

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Recommended by Fugee TsungModern production methods demand the synchronous multicharacteristic optimization of goods. There is a need to diversify a basic product to the importance placed on its individual quality components by a wide spectrum of concerned customers. This work shows how the super-ranking concept may be utilized taking into account relative weights among the implicated responses. The theoretical development is focused on the difficult situation where the optimization is attempted through unreplicated and saturated fractional factorial designs. A nested super-ranking scheme is devised to accommodate a dual weight assignment, first by setting up a single consolidated response per implicated customer and then, in a subsequent step, by incorporating a customer importance rating thus rendering an overall single master response. A demonstration of the proposed method on a pragmatic problem arising in aluminum milling involves optimization due to seven controlling factors concurrently influencing nine product responses modulated by six preference ratings set by a given customer base, respectively. Key benefits of this method are the offered ease of intermixing numerical and categorical data in mainstream multiresponse optimization problems, and keeping customer preferences in perspective through economical, short-cycle screening while relaxing stringent data normality and possible multidistributional effects among the implicated quality characteristics.

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“…A significant departure from the dual-response approach suggested by Welch et al (1990) is to simultaneously consider both the control and the noise factors in a single design called the combined array. Useful references on the combined array and its applications include Montgomery (1990), Shoemaker et al (1991), Lucas (1994), Borkowski and Lucas (1997), Borror and Montgomery (2000), Romano et al (2004). These designs typically require fewer runs than Taguchi's crossed arrays used in the dualresponse model and also allow the experimenter to estimate potentially important interactions.…”

Section: 1mentioning

confidence: 99%

Robust Parameter Design Using the Weighted Metric Method—The Case of ‘the Smaller the Better’

Ardakani¹,

Noorossana²,

Niaki³

et al. 2009

International Journal of Applied Mathematics and Computer Science

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In process robustness studies, it is desirable to minimize the influence of noise factors on the system and simultaneously determine the levels of controllable factors optimizing the overall response or outcome. In the cases when a random effects model is applicable and a fixed effects model is assumed instead, an increase in the variance of the coefficient vector should be expected. In this paper, the impacts of this assumption on the results of the experiment in the context of robust parameter design are investigated. Furthermore, two criteria are considered to determine the optimum settings for the control factors. In order to better understand the proposed method and to evaluate its performances, a numerical example for the case of 'the smaller the better' is included.

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Multiresponse Robust Design: A General Framework Based on Combined Array

Cited by 48 publications

References 18 publications

An overview on robust design hybrid metamodeling: Advanced methodology in process optimization under uncertainty

An overview on robust design hybrid metamodeling: Advanced methodology in process optimization under uncertainty

Product Screening to Multicustomer Preferences: Multiresponse Unreplicated Nested Super-ranking

Robust Parameter Design Using the Weighted Metric Method—The Case of ‘the Smaller the Better’

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