Robust design modeling with correlated quality characteristics using a multicriteria decision framework

Govindaluri, Srikrishna Madhumohan; Cho, Byung Rae

doi:10.1007/s00170-005-0349-6

Cited by 41 publications

(18 citation statements)

References 20 publications

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“…Using RSM, Huele and Engel 44 introduce a method of tolerance design for parameters, allowing non-normally distributed noise factors. Finally, the consideration of multiple quality characteristics in the context of RD has been studied in Govindaluri and Cho 45,46 and Kovach and Cho [47][48][49][50] .…”

Section: Literature Studymentioning

confidence: 98%

Development of a censored robust design model for time‐oriented quality characteristics

Shaibu

Cho

Kovach

2008

Quality & Reliability Eng

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Robust design (RD) techniques, which are based on the concept of building quality into products or processes, are increasingly popular in industry primarily because of their practicality. Traditional RD principles have often been applied to situations in which the quality characteristics of interest are time-insensitive. However, when time-oriented quality characteristics are studied, censored data often occur. As a result, current RD models reported in the literature may not be effective in finding solutions based on such data. To address such practical needs, this paper develops a censored RD model. We also propose an estimation method that is closely related to the expectation-maximization algorithm and compare it with the method of maximum likelihood estimation via a numerical example. Model validation is conducted, and comparative studies are discussed for model verification.

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Section: Literature Studymentioning

confidence: 98%

Development of a censored robust design model for time‐oriented quality characteristics

Shaibu

Cho

Kovach

2008

Quality & Reliability Eng

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“…One of the approaches for solving multiresponse problem based on programming is an approach proposed by Govindaluri and Cho (2007). They used programming to develop the multiresponse optimisation method based on Tchebycheff distance and relative importance (weights) of responses that are specified based on the customer requirements.…”

Section: Multiresponse Optimisation Based On Goal-programmingmentioning

confidence: 99%

Review of Multiresponse Process Optimisation Methods

Šibalija

Majstorovic

2015

Advanced Multiresponse Process Optimisation

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The review of methods used for multiresponse process parameter design and similar multiobjective optimisation problems is discussed in this chapter, implying the following classification: (1) conventional methods based on statistical or mathematical techniques: (i) experimental design techniques (response surface methodology, Taguchi's robust parameter design and related approaches), and (ii) iterative mathematical search techniques; (2) non-conventional methods based on artificial intelligence techniques: (i) fuzzy logic, (ii) artificial neural networks, (iii) metaheuristic search techniques (genetic algorithm, simulated annealing, particle swarm optimisation, ant colony optimisation, tabu search, and recent evolutionary algorithms such as artificial bee colony algorithm, biogeography-based optimisation, and teaching-learning-based optimisation), and (iv) expert systems.

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“…In [7] the strengths of two popular loss function-based methods [8,9] are combined. Other contributions introduced in the last decade are the mean squared error [10], weighted signal-to-noise ratio [11], PCA-based grey relational analysis [12], weighted principal component [13], capability index [14], patient rule induction [15], design envelopment analysis [16], compromise programming [17], goal programming [18], physical programming [19,20], bayesian probability [21], weighted Tchebycheff formulations [22], modified "-constraint method [23,24]. Relationships and differences among several criteria are highlighted in [25].…”

Section: Literature Overviewmentioning

confidence: 99%

Exploring Pareto Frontiers in the Response Surface Methodology

Costa

Lourenço

2015

Transactions on Engineering Technologies

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Multiple response optimization problems have many optimal solutions that impact differently on process or product. Some of these solutions lead to operation conditions more hazardous, more costly or more difficult to implement and control. Therefore, it is useful for the decision-maker to use methods capable of capturing solutions evenly distributed along the Pareto frontier. Three examples were used to evaluate the ability of three methods built on different approaches for depicting the Pareto frontier. Limitations of a desirability-based method are illustrated whereas the consistent performance of an easy-to-use global criterion gives confidence to use it in real-life problems developed under the Response Surface Methodology framework, as alternative to the sophisticated physical programming method.

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Robust design modeling with correlated quality characteristics using a multicriteria decision framework

Cited by 41 publications

References 20 publications

Development of a censored robust design model for time‐oriented quality characteristics

Development of a censored robust design model for time‐oriented quality characteristics

Review of Multiresponse Process Optimisation Methods

Exploring Pareto Frontiers in the Response Surface Methodology

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