Robust Design and Taguchi Method Application

Souza, Helder Jose Celani de; Silva, Messias Borges; Moysés, Cinthia Bachir; Alberto, Fernando; Pontes, Fabrício José; Ferreira, Ubirajara Rocha; Duarte, Roberto N.; Silva, Carlos Eduardo Sanches da

doi:10.5772/56580

Cited by 9 publications

(4 citation statements)

References 16 publications

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“…Therefore, to minimize the variability, the greatest value of S/N ratio must be chosen. 24 Some examples of S/N ratio are:…”

Section: Preliminary Notions: the Taguchi Robust Designmentioning

confidence: 99%

“…Especially, leading experts in design of experiments support the response surface approach or dual response approach 14,[16][17][18][19] or the procedure based on generalized linear models. [20][21][22] Nevertheless, there are still authors that base their studies on the Taguchi approach, [23][24][25] and also textbooks that deal with this topic. 26,27 Indeed, it is an argument still taught in university courses on statistical quality control.…”

Section: Introductionmentioning

confidence: 99%

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Inferential aspects for the optimal selection of the control parameters in Taguchi method

Facchinetti

Osmetti

Magagnoli

2020

Appl Stoch Models Bus & Ind

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This article focuses on the estimation of dispersion effects in off‐line quality control techniques. In this context, the Taguchi design for the optimal choice of process parameters is one of the most commonly used statistical methods. Starting from Taguchi methodology, we consider that an additive or a multiplicative model defines the relationship between the deterministic component and the variability of the process. We apply a hypothesis testing in order to find the optimal factor combination that minimizes the variability indicator of product quality, using ranking and selection methods of the Bechhofer kind. Moreover, an extensive simulation study shows how the probability of finding the optimal set of factors changes according to the main sampling parameters, in order to provide guidance for practitioners.

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“…Therefore, to minimize the variability, the greatest value of S/N ratio must be chosen. 24 Some examples of S/N ratio are:…”

Section: Preliminary Notions: the Taguchi Robust Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Inferential aspects for the optimal selection of the control parameters in Taguchi method

Facchinetti

Osmetti

Magagnoli

2020

Appl Stoch Models Bus & Ind

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“…and execution, (3) statistical analysis to determine optimum conditions, (4) confirmation test with optimum conditions [40]. elaborates on these steps further: i.…”

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confidence: 99%

Statistical optimization strategies on waste substrates for solving high-cost challenges in biosurfactants production: a review

Sharon,

Ahuekwe,

Nzubechi

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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Biosurfactants are bio-based amphiphilic molecules with extensive applications in various industries. These eco-friendly alternatives possess numerous advantages over chemical surfactants. However, high production costs hinder market competitiveness of biosurfactants. Production costs of synthetic surfactants range between $1-3/kg, while biosurfactants cost between $20-25/kg. Principal challenges hindering commercialization of biosurfactants are high costs of media constituents and downstream processing, accounting for 30% and 60-80% of production costs, respectively. Thus, cost-effective biosurfactant production would depend on the utilization of environment-friendly low-cost substrates and efficient product recovery. To this end, statistical tools such as Factorial Designs (FD) and Response Surface Methodology (RSM), are employed to optimize the production processes. FD as effective screening models comprise Plackett-Burman Design (PBD) and Taguchi design; and involves quantification of various significant factor effects including the main effect and level of dependency of one factor on the level of one or more factors. RSM predicts appropriate proportions of media constituents and optimal culture conditions; and is reportedly effective in reducing production cost and consequently, market price. Central Composite Design (CCD) and Box-Behnken Design (BBD) are common RSM for optimizing biosurfactants production. CCD assesses the relationship between one factor or more and a set of experimental variables. BBD is considered more proficient than CCD as it requires fewer experimental runs. Most recently, Artificial Neural Network which uses artificial intelligence-based tools to predict biosurfactant production using dependent variables of the process is gaining attention.

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“…Desta forma, os GL do erro corresponderiam a zero. Para o cálculo da média quadrática referente ao erro, utilizam-se então os valores da soma quadrática e do GL do fator de menor relevância (que foi o carregamento de lipase) para estimar a média quadrática do erro (SOUZA et al, 2013).…”

Section: Seleção Da Linhagem De Fungo Produtora De Lipase E Estabelecimento Das Condições De Hidróliseunclassified

Aplicações biotecnológicas do soro de queijo: produção de células íntegras com atividade lipolítica e geração de biogás

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Robust Design and Taguchi Method Application

Cited by 9 publications

References 16 publications

Inferential aspects for the optimal selection of the control parameters in Taguchi method

Inferential aspects for the optimal selection of the control parameters in Taguchi method

Statistical optimization strategies on waste substrates for solving high-cost challenges in biosurfactants production: a review

Aplicações biotecnológicas do soro de queijo: produção de células íntegras com atividade lipolítica e geração de biogás

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